research science skills

Research Scientist Skills

Learn about the skills that will be most essential for Research Scientists in 2024.

Getting Started as a Research Scientist

• What is a Research Scientist
• How To Become
• Certifications
• Tools & Software
• LinkedIn Guide
• Interview Questions
• Work-Life Balance
• Professional Goals
• Resume Examples
• Cover Letter Examples

What Skills Does a Research Scientist Need?

Find the important skills for any job.

Types of Skills for Research Scientists

Critical thinking and problem-solving, technical proficiency and specialization, data analysis and computational skills, communication and dissemination, project management and organization, top hard skills for research scientists.

Empowering discovery through robust data analysis, cutting-edge experimentation, and interdisciplinary expertise in today's dynamic scientific landscape.

• Statistical Analysis and Modeling
• Experimental Design and Execution
• Data Mining and Machine Learning
• Scientific Writing and Publishing
• Advanced Mathematics
• Laboratory Techniques and Instrumentation
• Computer Programming and Simulation
• Big Data Analytics
• Research Project Management
• Domain-Specific Knowledge (e.g., Genomics, Neuroscience, Materials Science)

Top Soft Skills for Research Scientists

Fostering innovation through critical thinking, collaboration, and resilience, while leading with emotional intelligence and meticulous organization.

• Critical Thinking and Problem Solving
• Effective Communication
• Collaboration and Teamwork
• Adaptability and Flexibility
• Creativity and Innovation
• Time Management and Organization
• Attention to Detail
• Resilience and Perseverance
• Emotional Intelligence
• Leadership and Mentoring

Most Important Research Scientist Skills in 2024

Interdisciplinary collaboration, advanced data analysis and interpretation, scientific communication and public engagement, grant writing and fundraising acumen, problem-solving and critical thinking, technical proficiency in emerging technologies, project management and organizational skills, adaptability to scientific paradigm shifts.

Show the Right Skills in Every Application

Research scientist skills by experience level, important skills for entry-level research scientists, important skills for mid-level research scientists, important skills for senior research scientists, most underrated skills for research scientists, 1. interdisciplinary knowledge, 2. intellectual curiosity, 3. resilience, how to demonstrate your skills as a research scientist in 2024, how you can upskill as a research scientist.

• Deepen Your Expertise with Specialized Courses: Enroll in advanced courses that focus on cutting-edge topics within your field to deepen your expertise and stay abreast of the latest scientific breakthroughs.
• Master Data Analysis and Statistical Software: Become proficient in the latest data analysis tools and software, such as R, Python, or specialized bioinformatics software, to enhance your research capabilities.
• Collaborate on Interdisciplinary Research Projects: Seek out opportunities to work with professionals from different scientific disciplines to broaden your perspective and foster innovation through cross-pollination of ideas.
• Participate in Scientific Conferences and Seminars: Attend and, if possible, present your research at national and international conferences to stay informed about recent developments and network with leading scientists.
• Contribute to Peer-Reviewed Journals: Writing and reviewing articles for reputable scientific journals will not only contribute to your field but also refine your critical thinking and writing skills.
• Engage with Research Funding and Grant Writing: Develop your skills in writing grant proposals to secure funding for your research, which is a critical component of a successful scientific career.
• Adopt Open Science Practices: Embrace open science by sharing your data and findings openly when possible, and using open-source resources to promote transparency and reproducibility in research.
• Develop Teaching and Mentoring Skills: Take on roles that involve teaching or mentoring to improve your communication skills and give back to the scientific community by helping to train the next generation of researchers.
• Stay Informed on Ethical Research Practices: Ensure that you are up-to-date with the ethical considerations and regulations in your field to conduct responsible and credible research.
• Invest in Soft Skills Development: Enhance your soft skills, such as teamwork, leadership, and problem-solving, which are invaluable in collaborative research environments and when leading projects or labs.

Skill FAQs for Research Scientists

What are the emerging skills for research scientists today, how can research scientists effectivley develop their soft skills, how important is technical expertise for research scientists.

Research Scientist Education

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How To Become A Research Scientist: What To Know

Updated: Feb 29, 2024, 1:40pm

Research is at the center of everything we know and discover, whether it’s food science, engineering, wildlife or the climate. Behind these discoveries, a research scientist conducts experiments, collects data, and shares their findings with the world.

Research and development scientist, or R&D scientist, is a broad career term that encompasses numerous types of scientists, from geologists to historians. Still, every research scientist has the same goal of furthering their field through experimentation and data analysis.

Browse this guide to discover how to become a research scientist and learn about this role, responsibilities and career outlook.

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What Does a Research Scientist Do?

Research scientists design and conduct research projects and experiments to collect and interpret relevant data. Many research scientists work in laboratory settings for universities, private businesses or government agencies.

These professionals are key players in many industries, from healthcare to marine biology . For instance, a chemist may test various materials for future upgrades to a medical device, while a wildlife research scientist might conduct long-term studies on a species’s breeding patterns.

The typical duties of a research scientist, regardless of their industry and position, include:

• Identifying research needs
• Collaborating with other professionals in a project
• Conducting research and experiments
• Writing laboratory reports
• Writing grant proposals
• Analyzing data
• Presenting research to appropriate audiences
• Developing research-related plans or projects

Research scientists may face challenges throughout their careers, like securing research funding or staying updated with policy changes and technologies. Additionally, to become involved in high-level research projects, research scientists usually need a doctoral degree, requiring substantial time and financial commitment.

How To Become a Research Scientist

The path to becoming a research scientist depends on your desired type of work.

For example, if you plan to become a research scientist for a hospital’s oncology department, you’ll likely need a doctoral degree and postdoctoral research experience. However, a product development researcher may only need a bachelor’s or master’s degree.

The following steps outline the general path needed for many research scientist positions.

Earn a Bachelor’s Degree

Research scientists can start by pursuing a bachelor’s degree in a field relevant to the research they want to conduct. For instance, an undergraduate degree in natural resources is helpful to become a wildlife biologist, while a prospective forensic scientist can pursue a degree in forensics.

If you’re undecided about your post-graduate goals, you can pursue a general major like chemistry, biology or physics before choosing a more field-specific master’s or doctoral degree.

Complete a Master’s Degree

Many higher-level research jobs require a master’s degree in a relevant field. Pursuing a master’s degree lets you gain work experience before beginning a doctorate, sets you apart from other doctoral candidates and qualifies you for advanced research positions.

However, you can skip a master’s degree and enter a doctoral program. Many doctoral programs only require a bachelor’s degree for admission, so you could save time and money by choosing that route rather than earning a master’s.

Get a Doctoral Degree

Doctorates require students to hone their research skills while mastering their field of interest, making these degrees the gold standard for research scientists.

A doctorate can take four to six years to complete. Research scientists should opt for the most relevant doctorate for their career path, like clinical research, bioscience or developmental science.

Pursue a Research Fellowship

Some jobs for research scientists require candidates to have experience in their field, making a research fellowship beneficial. In a research fellowship, students execute research projects under the mentorship of an industry expert, often a researcher within the student’s college or university.

Students can sometimes complete a fellowship while pursuing their doctoral degree, but other fellowships are only available to doctoral graduates.

Research Scientist Salary and Job Outlook

Payscale reports the average research scientist earns about $87,800 per year as of February 2024. However, research scientist salaries can vary significantly depending on the field and the scientist’s experience level.

For example, Payscale reports that entry-level research scientists earn about $84,000 annually, but those with 20 or more years of experience average approximately $106,000 as of February 2024.

The U.S. Bureau of Labor Statistics (BLS) reports salary data for several types of research scientist careers. For example, a geoscientist earns a median wage of about $87,000, while the median wage of a physicist is around $139,000 as of May 2022.

As salaries vary based on research science positions, so does demand. To illustrate, the BLS projects the need for chemists and materials scientists to grow by 6% from 2022 to 2032 but projects medical scientist jobs to increase by 10% in the same timeframe. Both projections demonstrate above-average career growth, however.

Research Scientist Specializations

A research scientist can work in many industries, so it’s crucial to understand your options before beginning your studies. Pinpointing a few areas of interest can help you find the right educational path for your future career.

Research scientists can specialize in life, physical or earth sciences.

Life science researchers like botanists, biologists and geneticists study living things and their environments. Physical research scientists, like chemists and physicists, explore non-living things and their interactions with an environment. Earth science researchers like meteorologists and geologists study Earth and its features.

Frequently Asked Questions (FAQs) About Becoming a Research Scientist

What degree does a research scientist need.

Research scientist education requirements vary by specialization, but entry-level research positions require at least a bachelor’s degree in a relevant field. Some employers prefer a master’s or doctoral degree, as advanced degrees demonstrate specialized knowledge and research experience.

How do I start a career in scientific research?

Research scientists need at least a bachelor’s degree. Many graduates pursue a master’s or doctoral degree while gaining experience with an entry-level position, internship or fellowship.

Does being a research scientist pay well?

Research scientist careers generally pay well; some specializations pay more than others. For example, the BLS reports a median salary of about $67,000 for zoologists and wildlife biologists as of May 2022, but physicists and astronomers earn just over $139,000 annually.

How many years does it take to become a research scientist?

It can take up to 10 years to become a doctorate-prepared research scientist, plus another one to five years to complete a postdoctoral fellowship. Entry-level research scientist roles may only require a four-year bachelor’s degree or a master’s degree, which takes one to two years.

Do you need a Ph.D. to be a research scientist?

No, not all research scientists need a Ph.D. Entry-level roles like forensic scientist technicians may only need a bachelor’s degree, and sociologists and economists usually need a master’s. Some research scientist roles, like physicists and medical scientists, require a doctoral degree.

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As a self-proclaimed lifelong learner and former educator, Amy Boyington is passionate about researching and advocating for learners of all ages. For over a decade, Amy has specialized in writing parenting and higher education content that simplifies the process of comparing schools, programs and tuition rates for prospective students and their families. Her work has been featured on several online publications, including Online MBA, Reader’s Digest and BestColleges.

How to Become a Research Scientist

Industry Advice Science & Mathematics

Professionals with a background in biotechnology can choose to pursue many lucrative careers . One of the most common choices is to become a research scientist. These individuals work in drug and process development, consistently conducting research and performing experiments to help move the biotechnology industry forward. 

“At the highest level, a research scientist is somebody who can design and execute experiments to prove or disprove a hypothesis,” says Jared Auclair , director of the biotechnology and bioinformatics programs at Northeastern. “Within the world of biotechnology, that can mean a number of different things, from creating new drugs to improving the process of how we make a drug.”

Professionals in this industry are often drawn to the wide array of applications of this work, as well as the consistently positive career outlook. The average salary of a biotechnology research scientist is $85,907 per year, with plenty of opportunities for increased salary potential depending on specializations, location, and years of experience. 

These factors—alongside the growing demand for advancement in biotechnology over the last few decades—have led many aspiring biotechnologists to consider a career in research science. Below we offer five steps professionals can take to kick-start a career in this field.

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5 Steps to Become a Research Scientist

1. acquire the necessary technical skills..

According to Auclair, there are four main applications of research science within the biotechnology field:

• Molecular Biology
• Process Science
• Biochemistry
• Analytical Biotechnology

Professionals hoping to pursue a career in research science must begin by deciding which of these four areas is the best fit for their interests and backgrounds. They must then acquire the specific skill sets they need to excel in that area. 

Below, Auclair breaks down some of the key skills and knowledge required within each of these specializations:

• Molecular biologists should focus on developing a complex understanding of DNA and learn how to do a Polymerase Chain Reaction alongside other DNA-related experiments. 
• Process scientists must understand cell biology and how to work with living mammalian cells, as well as how to perform analytical experiments using mass spectrometry and other analytical tools.
• Biochemists should focus on obtaining the skills necessary to make a protein drug, including the expression and purification of proteins.
• Analytical biotechnicians must become comfortable with techniques like mass spectrometry—a process that uncovers what drug products are at a molecular level.

One efficient way aspiring research scientists can obtain these specific skill sets is to pursue a master’s degree in biotechnology at a top university like Northeastern. 

“The biotech program is designed in collaboration with industry so that we’re meeting their needs,” Auclair says. “This includes training students with the skills they need to be a successful research scientist.”

The curriculum of Northeastern’s program explores the core competencies required to excel in the general biotechnology field and provides students with the unique subsets of skills they need to specialize in a specific area of research science. Students can even declare one of 10 industry-aligned concentrations, including options that directly relate with these common research science roles.

“Especially in industry, most people who are doing research science—who are actually doing the experiments and helping think about experiments with some of the senior leaders in the company—are people with a master’s degree,” Auclair says.

2. Become a critical thinker.

Alongside honing technical skills, Auclair says that critical thinking abilities are key for aspiring research scientists. 

“It’s important to become a critical thinker and a problem solver, and to challenge yourself wherever you can to step outside of your comfort zone,” Auclair says. 

Though critical thinking is a common requirement among most professional career paths, it is especially important for research scientists, who are constantly tasked with innovating and thinking creatively to solve problems.

Northeastern’s master’s in biotechnology program is designed to help students grow in this regard. “Everything we do within the program is geared [toward] making you a critical thinker and a problem solver,” Auclair says. “We try to define classes and assessments to make you think, [and] we also hire most of the faculty in our program directly from the industry, so they bring with them real-world experience that they can talk about with the students.”

These real-world case studies are a core component of Northeastern’s approach to learning, and they help prepare students to think critically about their work. By bringing this exposure into the classroom, students also graduate better prepared to tackle current industry challenges and adapt to evolving trends .

3. Hone your “power skills.”

It’s no longer enough for research scientists in biotechnology to have obtained the technical skills needed to complete their work. Today, many employers require an array of industry-specific “power skills”—previously known as “soft skills”—among candidates for research science roles.

Below we explore the top three “power skills” for biotechnology research scientists:

• Communication: As a research scientist, “you must be able to communicate scientific information to both technical and non-technical people,” Auclair says. For this reason, professionals should work to hone their verbal and written communication styles, focusing specifically on the variances in each depending on which audience they’re interacting with.
• Presentation Ability: Research scientists must be able to present their findings clearly and concisely to a variety of different audiences, ranging from fellow scientists to investors to C-suite executives. Research scientists must be comfortable in front of a group and know how to speak about their experiments and conclusions in an engaging and informative way.
• Teamwork: Although one might think a research scientist’s work is very siloed, today’s professionals must be very comfortable working with others in a lab environment. They must become comfortable sharing ideas, providing feedback to others in their cohort, and tweaking their experiments based on contributed findings.

Northeastern offers students the chance to explore each of these core “power skills” during their time within the master’s in biotechnology program. For example, the university offers countless opportunities for students to collaborate with and present to classmates, instructors, and even industry-leading organizations through Northeastern’s experiential learning opportunities, giving them the chance to apply these skills in both classroom and real-world situations early on.

Learn More: How to Become a Biotechnologist: Build Your Soft Skills

4. Obtain hands-on experience.

One of the most effective ways an aspiring research scientist can prepare for a career in this field is to obtain experiences working in a real lab. While finding these kinds of opportunities can be difficult for those just breaking into the field, programs like Northeastern’s MS in biotechnology bake hands-on learning directly into the curriculum. 

“Students do essentially four to six months [working in the] industry, and put what they learn in the classroom…into practice,” Auclair says.

These opportunities, known as co-ops , provide students with the chance to work within top organizations in the industry and explore the real-world challenges of the field from inside a functioning lab.

Did You Know: Northeastern’s program provides students with exposure to the tools and equipment used within labs in the industry. This access to cutting-edge technology reduces the learning curve and allows students to dive into their work as soon as they graduate.

Another unique way Northeastern provides hands-on experience is through Experiential Network (XN) Projects . Students who participate in these projects are typically paired with a sponsor from an active biotech company that has a real-world problem they need to solve. Then, “under the guidance of a faculty member, students spend the semester trying to come up with solutions to that problem,” Auclair says. “It’s all student-driven.”

Hands-on learning opportunities like these give students a competitive advantage when it comes to applying for jobs. “The experiential learning piece [of our program] is what has our students actually stand out above others in the field,” Auclair says, because employers like to see that their candidates are capable of applying their skills in a real-world environment. 

5. Grow your network.

Research shows that 85 percent of all jobs today are filled through networking, making it more important than ever for professionals across industries to invest time and energy into building these vital relationships.

Professionals hoping to establish a career as a research scientist are no exception. These individuals should aim to develop connections with organizations and individuals within the greater biotech industry early on in their careers, and use those relationships to help carve their path forward.

Northeastern’s master’s in biotechnology program has strategically created many great opportunities for students to network throughout their time in the program. They are encouraged to build relationships with their classmates, guest speakers, faculty, and even the industry leaders they meet through co-ops and XN projects. As a result, they establish various impactful connections with individuals at different stages in their careers, all before they graduate.

Learn More: Networking Tips for Scientists

Another way Northeastern’s program supports networking is through opportunities for student/faculty collaboration. “We encourage our students to interact with our own faculty who are research scientists as much as possible, whether that’s volunteering in their lab or finding a half an hour to talk to them about what they’re doing,” Auclair says. “We want our students to be exposed to as many research scientists as possible while they’re in the program.”

Take the Next Step

Pursuing a master’s degree in biotechnology from a top university like Northeastern is a great way for aspiring research scientists to break into the field. Students in these programs can hone related skill sets, grow their professional networks, and experience hands-on learning, all while pursuing graduate-level education. 

Learn more about how a master’s in biotechnology can set you up for success as a research scientist on our program page , then get in touch with our enrollment coaches who can help you take the first step.

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What does a research scientist do and how do I become one?

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As a research scientist, you’ll plan and conduct experiments to help expand the canon of scientific knowledge. With limitless opportunities for discovery across a range of high-growth sectors and industries, being a research scientist is one of the most exciting career paths in STEM. 

What does a research scientist do, exactly.

The purpose of a research scientist role is to conduct lab-based trials and experiments.

Work is often divided between pure research, which advances our understanding of basic processes, and applied research, which uses the information gathered to meet targets such as creating new products, processes, or commercial applications.

Of course, your targets will depend on the specialism of your employer. Research scientists work across a variety of different fields, including biology, chemistry, medicine, computer science, environmental science, and even political science.

Responsibilities

Typical day-to-day responsibilities of a research scientist include:

• Creating research proposals
• Planning and conducting experiments
• Collecting samples
• Monitoring experiments
• Recording and analysing data
• Collaborating with other researchers and academia to develop new techniques and products
• Supervising junior staff
• Carrying out fieldwork and monitoring environmental factors
• Researching and writing published papers
• Staying up-to-date with the latest scientific developments

Work environment

As a research scientist, you’ll spend most of your week in a laboratory. These environments can vary depending on your specialism. For example, biology labs are designed to safely house and contain living specimens, while psychology labs may simply consist of a bank of computers.

Aside from lab work, certain aspects of your role (including writing up results or research papers) will be undertaken in an office environment. You may also be required to visit the labs or offices of other researchers or companies, especially if you are collaborating on the same project.

Working hours

Research scientists typically work 35 to 40 hours a week on a 9-to-5, full-time basis. On occasion, you may be required to work overtime or visit the laboratory on weekends to complete certain tasks. That said, most organisations offer flexible working arrangements. 

What skills are needed to be a research scientist?

Though research scientists come in all personality types, you’ll need to have an academic mindset and be naturally inquisitive. Research scientist skills include:

• A methodical approach to gathering and analysing data
• Meticulous attention to detail
• Critical thinking
• Advanced research skills
• Time management
• Strong communication and interpersonal skills
• The ability to work independently
• A collaborative mindset
• Stakeholder management
• Patience and tenacity

How to become a research scientist

As a minimum requirement, you’ll need to obtain a 2:1 bachelor’s degree or higher in a relevant field of science. Most research scientists also have a postgraduate qualification, such as an MSc, an MSci or MBiol. Relevant qualifications include:

• Biochemistry
• Biomedical science
• Environmental science
• Microbiology
• Natural science
• Pharmacology

While a PhD isn’t necessarily required, some employers prefer candidates that either have or are working towards a doctorate. Demonstrable experience of working in a laboratory environment will also improve your employment chances.

Tip: If you’re currently studying or have already attained a relevant degree, try to gain research experience in a lab environment. The best place to start is by expressing your interest to your university department, who may have some voluntary positions available. Alternatively, sending your CV/resume to hospitals and STEM companies will also increase your chances of gaining that vital experience.

How much do research scientists earn?

Like many roles in science, salaries for research scientists depend on your level of experience, your specialism, the employer, and, to a lesser extent, the location. It’s also worth bearing in mind that private-sector salaries tend to be higher than those in the public sector or academia.

In the UK, research scientist salaries range from £20,000 at the entry-level to over £70,000 for university professor senior research fellow roles. The average research scientist salary is £32,330. Most research assistants earn between £26,000 and £35,000.

According to Indeed, the average salary for a research scientist in the US is $111,444.

Please note that income figures are subject to economic conditions and are only intended as a guide.

Is research scientist a good career?

With science constantly opening up exciting new avenues of research, working as a research scientist provides secure employment and gives you the chance to make a real difference within STEM.

Indeed, the outlook for the role is positive: in the US alone, the vocation is expected to grow by 8% and produce over 10,000 job opportunities across the country by 2028 (Zippia). As one of the least likely jobs to be automated in the coming years, the role also offers stability in these turbulent times. 

Offering a strong earning potential and the opportunity to conduct cutting-edge research in a range of industries and locations, research scientist represents one of the most fulfilling career paths around.

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Research Scientist skills for your resume and career

Research scientists need a range of hard skills depending on their field and industry. In general, data analysis is crucial, as it helps make sense of scientific research. Knowledge of procedures and the ability to train others on these procedures are also important. In molecular biology, skills in cell culture and flow cytometry are valuable. In addition, programming skills in languages like Python, Java, and R are often required.

When it comes to soft skills, research scientists need a strong sense of curiosity above all else. They also need to be organized, as they need to record and keep track of data. As Shiri Noy Ph.D. , Assistant Professor at Denison University, puts it, "What good is technical knowledge if you don't know how to approach a problem critically, from diverse vantages, and while being open to feedback and others' ideas when you hit a dead end?" In today's world, patience and communication are key, along with the ability to learn and ask for help when needed.

15 research scientist skills for your resume and career

Python is a programming language used for various purposes such as data analysis, visualization, and machine learning. Research scientists use Python to develop custom scripts, integrate publicly available software, and automate various tasks. They also use Python for data collection, cleansing, and analysis, as well as for developing large-scale statistic data analysis tools. As Dr. John Stevens , a Professor of Statistics at Utah State University, puts it, "Python and R programming are the hot things now, but I'd emphasize the need to do more than just taste those things in a surface-level introduction. True understanding and value only come with deep experience, and deep experience requires real, dedicated time."

• Created open-source data analysis and visualization package in Python.
• Coded a class that integrated OpenGL with Python and C++ to simulate chaotically dynamic system.

2. Data Analysis

Data analysis is the process of examining and interpreting data to draw conclusions. Research scientists use data analysis to evaluate and present their findings. They develop plans for data analysis, collect and interpret data, and write reports on their results. They also use software and pipelines to analyze data, such as next-generation sequence data. As Josh Kaplan Ph.D. , Associate Professor at Western Washington University, puts it, "Being able to work with various computer coding languages and implementing free, open-source software...will be increasingly valuable in a work setting that involves research, data analysis, or program optimization."

• Worked extensively with data analysis/interpretation and presentation.
• Coordinated associated test-planning, developed test planning documents, conducted operational tests, conducted data analysis, and developed reporting documents.

3. Patients

Patients are individuals who receive medical care. Research scientists use patients in their work by studying their medical conditions, analyzing their blood samples, and developing treatments tailored to their needs. They also use patient data to identify high-risk individuals who require early intervention and to reduce medical costs. In some cases, research scientists interview patients to gather data or use their medical information to develop new cancer drugs.

• Participated in laboratory and clinical research designed to provide clinically-relevant insights into blood cell formation and function in cancer patients.
• Developed a computer-based patient model to identify high risk, early intervention patients and reduce medical costs.

C++ is a programming language used for creating a wide range of applications. Research scientists use C++ in various ways, such as implementing new capabilities into software, developing algorithms for waveform analysis, and creating optimization algorithms for noisy functions. They also use C++ to develop software for data monitoring, predictive models, and avionic data buses. As Gabriel Loiacono , Associate Professor of History at the University of Wisconsin - Oshkosh, puts it, "strengthening C++ skills in college can help increase earning potential."

• Implemented C++ capabilities to simulate the effective mechanical behavior of rubber-metal composites within an in-house Finite Element software.
• Performed extensive waveform analysis using C++ to characterize light sensor performance that led to defining detector design requirements.

5. Research Projects

Research projects are scientific investigations focused on a specific topic. Research scientists use research projects to develop new ideas, solve problems, and answer questions. They design and perform experiments, collect data, and analyze results to draw conclusions. They also collaborate with other researchers, provide training and support, and write reports about their findings.

• Dedicated to continued enhancement of leadership competencies, problem solving, independent evaluation of scientific data for basic research projects.
• Identified and established novel clinical collaborations with academic centers, researchers and physicians that could support research projects.

6. Chemistry

Chemistry is the scientific study of the composition, properties, and reactions of matter. Research scientists use chemistry in various ways, including developing new synthetic pathways, optimizing chemistry formulations, and conducting experiments in a chemical research setting. They also apply medicinal chemistry knowledge to drug discovery projects and evaluate chemistry practices on a manufacturing scale. Dr. Richard Knight Ph.D. , a Teaching Professor and Associate Department Head at Drexel University's Department of Materials Science and Engineering, highlights the importance of chemistry certifications, stating, "Earning a degree from a university that takes time and effort to be accredited by either the American Chemical Society (ACS) and/or the American Society for Biochemistry and Molecular Biology(ASBMB) ensures the graduate will have marketable skills."

• Process Chemistry Safety Steward/Safety Mentor/E-Team member
• Specialized in synthetic methodology, chemistry optimization, and the development of new synthetic pathways to complex molecular targets.

Choose from 10+ customizable research scientist resume templates

Java is a programming language used to develop software and applications. Research scientists use Java in various ways, such as building interactive programming and visualization tools, developing secure socket layer interfaces for web services, and implementing graph analysis algorithms for network vulnerability removal. They also use Java to develop applications for converting data streams to XML and to design online process controllers. As Autumn Mathias Ph.D., LCSW , Associate Professor, states, "quantitative research skills in particular are advantageous for many positions. This includes attaining coding skills and learning coding languages such as Python, R, and Java."

• Developed the camera take detection service via java and integrated it into an automatic video object annotation system utilizing social cues.
• Research Scientist Worked with the Java Tools research group designing advanced interactive programming and visualization tools; explored agent-based component architectures.

8. Molecular Biology

Molecular biology is the study of the structure, function, and interactions of biological molecules. Research scientists use molecular biology to design and execute studies, create laboratory operations, and train students in various techniques. They also use it to analyze cell development, develop new technologies, and troubleshoot results. As Darrell Fry , Associate Professor at Stephen F. Austin State University, notes, "Earning a degree from an University that takes time and effort to be accredited by either the American Chemical Society (ACS) and/or the American Society for Biochemistry and Molecular Biology (ASBMB) ensures the graduate will have marketable skills."

• Created/established molecular biology laboratory within department.
• Design and execute molecular biology and biochemistry studies to support project objectives with minimal direct supervision from Research Director.

9. Data Collection

Data collection is the process of gathering and measuring information on variables of interest. Research scientists use data collection to support research design, monitor study sites, and conduct qualitative interviews. They also manage databases, oversee data compilation, and verify the accuracy of the information. As Harriet Phinney Ph.D., Associate Professor at Seattle University, puts it, "Empirical data collection is a crucial skill for research scientists. It involves collecting original data, analyzing it, writing up the information, and presenting it in a professional manner."

• Identified and determined methods, procedures, and techniques to support research design; identified and recommended data collection methodology.
• Participate in data collection, including coordination of study sites, administration of research instruments, and conducting qualitative interviews.

10. Cell Culture

Cell culture is the process of growing cells in a controlled environment, outside of their natural environment. Research scientists use cell culture to isolate and prepare primary cells, develop personalized therapies, and produce viral antigens. They improve the efficiency of cell scale-up processes using automated cell culture robotics and optimize cell culture conditions for antibody expression, purification, and functional characterization.

• Conducted isolation and preparation of primary cells including T-cell and performed primary cell culture for developing personalized therapies.
• Created and implemented standard operating procedures on mammalian cell culture and viral antigen production.

11. TensorFlow

Tensorflow is an open-source software library for machine learning and artificial intelligence. Research scientists use TensorFlow to develop and train neural networks for various tasks, such as document classification. They design and train these networks to process data, like sentences, and make predictions.

• Project: Convolutional Neural Network for document classification (2016.10-2017.1) - Develop a convolutional neural network sentence classifier using Tensorflow.

12. Visualization

Visualization is the process of creating images or videos to communicate information. Research scientists use visualization to analyze and present data in a clear and concise manner. They might use tools like Tableau to generate visually appealing graphs, or develop innovative ways to display complex information like contested environment analysis challenges and analyst skill sets. They also use visualization to study phenomena like bubble characteristics through flow visualization experiments.

• Research includes developing robust and adaptive systems for computer-aided analysis and visualization with machine learning and image processing/computer vision approaches.
• Manage the development of innovative visualization and concept mapping of contested environment analysis challenges and analyst skill sets.

13. Excellent Interpersonal

Excellent interpersonal skills are the ability to interact well with others. Research scientists use these skills to create a comfortable environment when interacting with a diverse population. They also use these skills to work well with different departments within a research facility.

• Utilized excellent interpersonal and empathetic skills while interacting with a diverse population, creating a comfortable environment.
• Team oriented professional with excellent interpersonal and communication skills.

14. Laboratory Equipment

Laboratory equipment is the tools and machinery used in a laboratory setting to conduct experiments, analyze data, and make new discoveries. Research scientists use laboratory equipment daily to perform their job. They manage and maintain this equipment, train others to use it, and troubleshoot any issues that arise. They also use laboratory equipment to collect and analyze data, and sometimes even design and develop new equipment to suit their specific needs.

• Managed maintenance/upgrades of laboratory equipment.
• Supervised and trained post-doctorate fellows, students and lab assistants to perform general laboratory techniques and operate laboratory equipment.

15. Experimental Design

Experimental design is the process of planning and organizing an experiment to ensure the results are valid and relevant. Research scientists use experimental design to manage investigations, collaborate with team members, and organize and report results. They also use it to optimize product development and marketing decisions, develop and implement power analysis methodology, and assist laboratory personnel with complex data interpretation. As Dr. Sharon Locke Ph.D. , Director at the Center for STEM Research, Education, and Outreach, Professor at the Department of Environmental Sciences, and ISSP Sustainability Excellence Associate at Southern Illinois University Edwardsville, puts it, "How to design a research study/experimental design" is one of the most important hard/technical skills for Research Scientists.

• Managed investigations from incident to CAPA closure including experimental design, collaboration with team members, and organizing and reporting results.
• Contributed scientific expertise to critical projects, manage the day-to-day operations of those initiatives and perform advanced analysis and experimental design.

12 Research Scientist Resume Examples

Build a professional research scientist resume in minutes. Browse through our resume examples to identify the best way to word your resume. Then choose from 12 + resume templates to create your research scientist resume.

What skills help Research Scientists find jobs?

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What type of skills will young Research Scientists need?

Professor, Pharmacology & Toxicology; Professor, Obstetrics & Gynecology , Wright State University

What soft skills should all Research Scientists possess?

Alexandra (Sasha) Ormond Ph.D.

Associate Professor of Chemistry, Director of Dual Degree Engineering , Meredith College

What hard/technical skills are most important for Research Scientists?

What skills stand out on research scientist resumes, what research scientist skills would you recommend for someone trying to advance their career.

Assistant Department Chair, Geology , Auburn University

List of research scientist skills to add to your resume

The most important skills for a research scientist resume and required skills for a research scientist to have include:

• Data Analysis
• Research Projects
• Molecular Biology
• Data Collection
• Cell Culture
• Visualization
• Excellent Interpersonal
• Laboratory Equipment
• Experimental Design
• Statistical Analysis
• Flow Cytometry
• Prototyping
• Product Development
• Research Findings
• Technical Reports
• Drug Discovery
• Next-Generation Sequencing
• Method Development
• Analytical Methods
• Distributed Computing
• Clinical Trials
• Cell-Based Assays
• Western Blotting
• Technical Support
• Emerging Technologies

Updated June 25, 2024

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

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Home › Study Tips › 11 Tips to Improve Your Research Skills for Academic Success

11 Tips to Improve Your Research Skills for Academic Success

• Published May 24, 2024

Strong research skills are a must-have skill for academic success. Why are research skills important?

They’re essential for academic success. You need them for all term papers, research reports, and assignments. These skills also help to deepen your understanding of all the topics in your curriculum. 

By design, research questions are not answerable by simple Google searches. They require planning, hypothesis evaluation, data or information analysis, critical thinking, information synthesis, logical and well-thought-out presentation, and more.

With these skills, you can produce credible, logical, accurate, and plagiarism-free research efficiently and promptly. 

Moreover, being a skilled researcher is not only necessary for academic success. It is a lifelong competency that would remain helpful in your future career and personal life. 

Some tips you can adopt to improve your research skills include understanding the research process, using library resources, effectively searching the internet, adopting proper citation and referencing, developing your analytical skills, managing time efficiently, utilising academic support services, enhancing your note-taking capabilities, using primary sources only, and avoiding confirmation bias. 

Below, we examine these strategies to help you improve your research skills. 

1. Always Create a Research Strategy Document

Think of strategy as a roadmap highlighting how you want to attack the research problem. We believe creating a strategy before diving knee-deep into research provides clarity and saves you time.

Some of the constituents of the strategy document include:

• Research goals
• Research deadline
• Rewriting the research problem the way you understand it, in your own words and simple terms. Then, translate the research problem into a research question. “HR managers are struggling to attract and keep top talent with top talent spending an average of 6 months in each role” is an example of a research problem, while “What strategies and techniques can HR managers adopt to better attract and retain top talents?” is an example of a research question.  
• Outline the major outcomes the research must fulfil. For example, “The research must provide a nexus between company actions and top talent loyalty, in addition to providing actionable tips for HR managers.”  
• Identify the type of research you’re doing. There are three categories of research: basic vs applied, exploratory vs explanatory, and inductive vs deductive research. 
• Findings from preliminary research. We recommend quick preliminary research to see the resources, including scholarly knowledge, readily available in the public domain. This step can help identify a new angle to pursue your research from or drop if you reckon other researchers and authors have adequately dealt with the question, preventing you from wasting time and resources on research that adds no additional value to the body of existing knowledge. 

2. Understand the Research Process

The research process consists of six major stages, including topic selection, literature evaluation, refining the research topic, relevant information gathering (could also include sampling and recruitment, depending on the topic or research focus), data analysis, and knitting everything together. 

Topic Selection

Sometimes, your tutor may provide the research topic. However, you’ll likely need to work with your supervisor to choose a topic for your thesis and undergrad projects. 

For your choice of research topic, it’s imperative to think of your current interests and future ambitions. 

Beyond top grades, your undergrad research may serve as evidence of your interest in a particular area and be helpful for future academic and career progression. 

Every research topic or question starts from a broad problem statement, which you can then fine-tune after exploring the existing body of knowledge in that field. 

Overall, a great topic has the following characteristics:

• Focused on a single issue. However, you may subdivide the issue into several interconnected but related problem statements.
• Researchable with credible sources. For example, requiring proprietary data that is not readily available may seriously hamper your success. 
• Feasible and specific. Additionally, ensure that you have adequate time and resources to complete the study before the due date. 
• Avoids value judgement questions like “Is vitamin D better than magnesium in treating bone issues?”
• Not close-ended such that the answer is a simple yes or no. The lack of clear answers provides room for robust investigation and is where your arguments shine. 
• The answer to your question should not be readily available. It must require rigorous work and iterative problem-solving to complete. 
• The topic must be original and address a relevant industry or niche problem. Originality doesn’t mean other researchers haven’t attempted something similar but that you’re presenting a new angle. 

Literature Review

The goals of conducting the literature review include:

• To ensure other researchers haven’t answered the research question before and that the study will contribute significant value to the existing body of knowledge.
• To identify gaps in existing works and determine how your project will fill that gap. In essence, the research must considerably add to existing knowledge or improve on earlier methodologies. Without meeting these standards, most research journals will not accept your work. 
• The third goal is to help you evaluate the research methods, research design, data sources, and key concepts other researchers adopted for their work. 

A literature review is a lot of work and requires scouring through numerous academic journals, books, and online publications. 

You can leverage AI tools like Elicit AI, Research Rabbit, Semantic Scholar, and Connected Papers to find papers, summarise studies, conduct citation-based mapping, find similar research papers, and more. 

Refine Research Topic

Armed with more information, context, potential data sources, availability of reliable and credible data, and the scope of work required from your literature review, you often need to refine your topic. 

For example, your research question may be too narrow if you find very few credible papers and books on the subject. Your research topic could also be suffering from being too broad. 

You can finetune a broad project topic by asking the why, what, who, where, and when questions. 

Which group of people are you targeting for the research? What geographic location would the study be limited to? Why do you think the research is relevant? What period would you limit the research to?

For example, “What will be the impact of climate change in the United Kingdom?” is quite broad. What kind of impact are we talking about? Economic? Migration? Health? 

A more specific variant of the question would be: “How will climate change affect net migration between the UK coastline and major cities in the next 20 years?” 

Data Gathering

Collecting data is the heart of the research process. This step allows you to gather variables essential for reaching conclusions. Depending on your research question, these variables can either be qualitative (non-numerical) or quantitative (numerical). 

You may gather data through one or more of the following methods:

Surveys are a series of questions used to extract specific data from a sample of the target population. When running surveys, you should take note of the following:

• Sample size: Ensure the number of participants adequately represents the population. 
• Bias: Ensure the questions do not tilt respondents in a particular direction or the sampling is not based on subjective measures. For example, assuming the age of shoppers who walk into a store can lead to bias. 
• Ambiguity and clarity: Avoid ambiguous questions that are prone to personal interpretation. “Do you drink plenty of alcohol during the week?” is subjective because the answer depends on who you ask. 
• Resource management: The larger the sample size, the more expensive and time-consuming the survey process is. 

Experiments

Experiments will be your go-to research method if you’re in any natural and physical sciences programme. It’s easier to establish a cause-and-effect relationship with experiments than with surveys. 

A typical example of an experiment involves splitting test subjects into a control and an experimental group. The researchers then give the latter group a medicine, drug, or treatment or subject them to changes. 

The researchers then evaluate the two groups for a specific variable. If the variable varies significantly, then suffice it to say that the changes made to the experimental group are responsible for the significant differences in the observed variable. 

Observational Studies

Observational studies are more popular in social sciences for obvious reasons. They involve going to the field to observe the attitudes and behaviours of a specific group in the natural habitat. 

Observational studies may either be participant observation or nonparticipant observation. The former involves the researcher staying in the same habit as the group they’re observing, while the latter is the reverse. 

Participant observation may influence how the target population acts. So, it’s imperative to conduct the study such that your presence is not disruptive to the data collection process. 

Existing Data

In every sector or industry, there’s existing data that can help with your research. Need economic activity data on the UK? The Office for National Statistics (ONS) is perhaps the most credible primary source on the subject matter. 

What about data on the UK environment? The Department for Environment, Food & Rural Affairs data services platform (DSP) is your best bet. 

Beyond facts and figures, court records, medical records (without personally identifiable information), and police interview tapes can also be excellent sources of information. 

Data Analysis

You have gathered all the data you need to answer your research question. Now, this is where you begin to look for clues, determine relationships between variables, establish trends, find patterns, and more. 

For numerical variables, you’ll need complex statistical techniques to extract insights from the data. Tools like Statistical Analysis System (SAS), R, Python, MS Excel, and the Statistical Package for the Social Sciences (SPSS) can help with quantitative data analysis. 

Some tools can help with most qualitative methodological techniques. Examples of these tools include nVivo and ATLAS.ti. It’s imperative to note that while these tools are helpful, you’ll need to put on your sound critical thinking cap to ensure your analysis is accurate. 

Result Discussion

The data analysis above will provide evidence to prove or disprove your hypothesis or question. The discussion section helps you convey these results in a deeper conversation. 

What results do you have? What are the implications of such results? How relevant are the results from both a statistical point of view and practical applications? 

These and many more questions are the answers this section should provide. Furthermore, share the limitations of your research and potential avenues for further exploration. 

If there’s any additional tip we would leave you with here is to stay with the facts and provide your findings in context with previous studies. Doing this strengthens your argument and makes your research more credible and citable. 

3. Use Library Resources

Librarians curate only authoritative and credible sources. These sources include books, journals, and databases. 

Another benefit of using library resources is that they are organised, making it easy for you to find the resources you need. 

As a college student, you should never pay out of pocket for any resource. Your school library probably already provides access to that resource. If not, you can make a request, and it’ll most likely be granted. 

So, what kind of resources are available?

• A searchable library catalogue tool, basically a search engine for academic sources
• Access to third-party databases
• Extensive collection of e-books
• Access to conference papers, newspaper articles, and other credible publications
• Subject Librarian to help you with resources not in the library catalogue
• Reference management tools and resources on how to use them

Tips to Search Databases

• Use the truncation symbol (*) and the wildcard symbol (?) to broaden your search to ensure you do not miss out on relevant results due to spelling or plural versions. For example, “agricultur*” will provide search results that include the following words: agriculture, agricultural, and agriculturalist. “Lab?r” will search for resources with both “labor” and “labour” in them. 
• Use boolean operators. We discuss this extensively below. The same principles apply here. 
• Use inverted commas to search for a specific phrase together. We also explain this below. 
• Leverage proximity search: This tells the database to return results that have words within certain distances from each other. For example, typing “labour same union” on Web of Science returns publications with “labour” and “union” in the same sentence. Typing “labour union ~4” on JSTOR retrieves records where “labour” and “union” are only separated by four words. 
• Combine the methods above to create more sophisticated search queries.

4. Effective Internet Research

The internet is a treasure trove of information and resources. That said, you must be cautious of every page on the internet, especially in the age of AI content. 

Every source for a research project must be up-to-date, factual, unbiased, and from a credible source. True story: we’ve seen students quote data from satirical publications. 

Moreover, most pages on the internet don’t go through a review process and may be rife with misinformation. 

Just because a page appears on number one of your search results doesn’t make it a great resource. The article author or publisher may just be great at search engine optimization. 

Assessing a Website’s Credibility and Accuracy

Many people create websites to make money. While some provide some measure of value, others simply do not care. 

Moreover, some of these websites may present information from the owner or author’s bias. For the most part, it’s best to stick with non-academic resources provided by government agencies and reputable organisations. 

You can evaluate a website’s credibility by examining:

• The About Us page: Who or which group owns the website? What are their goals?
• The author bio: Who’s the author, and what’s their qualification and experience to authoritatively speak on the subject? You may do further Google and social media (LinkedIn in particular) investigations to assess the author’s qualifications. 
• Domain ownership: Use whois.net to track who owns a website. This information may or may not be available. 
• Articles dates and recency: Avoid undated websites and articles using dated facts to draw recent conclusions.  

Internet Search Techniques

Here are a few techniques to help you find relevant pages that answer your search queries. 

Use Inverted Commas

Search engines will treat each word in your search query as individual keywords without inverted commas. 

So, you may get web pages that only contain the term “anatomy” or only “heart” if you type heart anatomy without quotation marks.

However, encasing your keyword in quotation marks, like this: “heart anatomy” only returns results with the exact phrase, thus providing fewer web pages to examine. 

Boolean Operators

Boolean operators include AND, OR, and NOT. They can be a powerful way to hone in on the sources you need. 

Boolean Operators 

Example 

Search result includes web pages containing keywords joined by AND

“Traffic data” AND “London” 

Search result includes pages with one or all the keywords linked by OR

“Manager” OR “Coordinator” 

NOT or – 

Excludes web pages with a particular term from the search result. Helpful when a term skews your search results

-animal or “NOT animal”

Used to include a term that must be included in the results. Helpful for narrowing a broad search query

2024 United Kingdom Elections report +fraud +voting pattern

Brackets ()

Powerful for combining boolean operators. Helpful when a keyword also has a popular synonyms or alternative

Project (manager OR coordinator)

Site: 

Provide search results from the website you provide only. Helpful when searching a website like the ONS for data

site: https://www.ons.gov.uk/

Search Engine Tools

Search engines have additional tools to help you refine your search. Google, for example, has tools to limit the results to those published within a specified date range. 

You may also limit results to a particular file type, such as images, books, videos, and news. 

Use Different Search Engines

Each search engine has its own unique algorithms (set of rules to arrange web pages in search results). Trying a new search engine may just be the trick you need. 

Examples of other search engines to try include:

• www.duckduckgo.com
• www.bing.com
• www.ask.com

Use Google’s Advanced Search Tool

Google’s advanced search tool allows you to enter multiple parameters to refine your search. Behind the hood, the tool simplifies the use of boolean operators. Instead of typing boolean operators, you simply enter terms in textboxes. 

You can specify other parameters like the last time the authors updated the website, region to target, and language. 

5. Citation and referencing

Any idea, words, data, images, infographic, or information you take from any source requires a reference. Without citations, you’re practically stealing someone else’s ideas and thoughts. 

Many schools have strict rules against plagiarism, including formal warnings, suspension, admission withdrawal, and other penalties. 

Aside from helping you avoid plagiarism, citations also make your work more authoritative and persuasive. 

There are multiple referencing styles, including AMS (American Meteorological Society), APA (American Psychological Association), Chicago, Harvard, MHRA (Modern Humanities Research Association), OSCOLA (Oxford Standard for the Citation of Legal Authorities), and others. 

Your student handbook will usually provide which of the above styles your programme uses. 

Tips For Managing Citations and References

• Make a list of your references and cite them as you write.
• Add notes to each reference, highlighting the sections, paragraphs, and pages you’re most interested in.
• Be consistent with the reference style you use. 
• Familiarise yourself with the project’s reference style.
• Use referencing tools. Examples include EndNote, Zotero or Mendeley. Practice with the program to ensure you know the type of information required and where to input it.

6. Develop Analytical Skills

Per the Rockwell Career Centre, “ analytical skills are problem-solving skills that help you parse data and information to develop creative, rational solutions.”

Analytical skills are essential to every step of the research process, especially in objectively analysing the problem and the result of your experiments. 

Analytical skills require critical reasoning, understanding different concepts (including complex and abstract ones), explaining or articulating your thoughts, applying what you read to tackle problems, and much more. 

Strategies for analysing and synthesising information

1. gain foundational knowledge.

Nothing strengthens your ability to critically analyse the data you’ve gathered than having a solid grasp of the basic concepts in the area you’re investigating. 

For example, you can’t discuss recidivism without understanding the court and prison process.  

2. Create an information matrix

An information matrix is a table that helps organise your sources by major themes. Identifying key ideas from sources is an integral part of information synthesis. 

Here’s an example with five sources:

Theme 

Quality time

Words of affirmation

Acts of service

Physical touch

For each source, enter what they say about each major theme you identified. Leave the corresponding cell blank if a source is mute on a theme. 

Create a new row if any of the sources present a compelling key theme that aligns with your research. 

3. Summarise and Paraphrase the Original Source

Summarise and paraphrase important ideas and quotations you lift from sources. This primarily means presenting your original thoughts and interpretation of the content in the source. 

To paraphrase, you must understand the original source. So, this is good practice for information synthesis. If you’re struggling to paraphrase or summarise an idea, maybe you do not understand it yet. 

Doing this keeps quotes to a minimum, which can help you achieve better grades. Additionally, it promotes the use of your own voice more and to avoid plagiarism. 

4. Improve your comprehension skills

Some of the ways you can improve your comprehension skills include:

• Broadening your vocabulary often by reading widely and critically
• Recollecting the main points and critical details about the text from memory
• Reading in a distraction-free environment
• Slow down and embrace active reading. The Open University defines active reading as “ reading something with a determination to understand and evaluate it for its relevance to your needs.” This process involves highlighting key texts, raising questions, explaining the text to others, self-testing with flashcards or help from a colleague, and more. 
• Summarising and identifying key ideas

5. Identify the Context of Each Source

While two papers may speak about the same topic, they may approach the subject from totally opposite angles. This makes it hard to do an apples-to-apples comparison. Identifying the context helps you avoid this pitfall. 

7. Time Management in Research

Strategies and tips for effecting time management in research include:

1. Setting Realistic and Attainable Goals

Earlier, we mentioned how it’s imperative to choose a research topic that you can complete within the deadline provided by your tutor. Doing otherwise will only lead to poor time management. While you may complete the research, other areas may suffer. 

2. Fix Regular Schedule

Creating a schedule helps you devote ample time to the research daily. Consistent attention is better than last-minute rushes. A regular schedule helps avoid procrastination, interruptions, and lack of discipline.  

How many hours you commit daily will depend on your other commitments and the research deadline. It’s important to design your schedule such that you’ve completed your research report far ahead of the deadline. 

3. Incorporate Task Lists for Each Block of Time

Approach each block of time you schedule with a task list. Doing so helps you focus and prioritise important tasks. 

4. Avoid Multitasking

Focus on one task at a time and finish the same before doing something else. Multitasking is often unproductive and can be a source of stress when you fail to achieve anything noteworthy. 

5. Leverage Technology

Tools for project management, to-do lists, and calendar apps can help you implement the time management strategies shared above and help you stay organised.  

6. Ask For Help

Ask for help from your tutor, coursemates, and librarian whenever you’re stuck. Also, use the numerous free resources available to you. 

7. Take Breaks and Reward Yourself 

Bake breaks into your schedule to alleviate stress and ensure you operate optimally. Additionally, reward yourself for completing the tasks on your to-do lists. 

8. Utilise Academic Support Services

Most universities offer programs to help students achieve academic success, including those geared towards how to conduct research and improve research skills.  

If you’re unaware of the academic support services on offer, it’s important to ask. There’s no shame in asking for help. You’d be amazed at how much help these centres offer. 

One of the best ways to identify the areas you need to work on is to ask your tutor for feedback and help. 

Typical academic support services include:

• Writing guidance
• Peer tutoring
• After-school programmes
• Counselling
• Academic monitoring
• Experiential learning

9. Enhance Note-Taking Techniques

Note-taking is essential during research projects as it’s a vital tool for information synthesis. Note-taking helps you organise the points in the sources you’re reading. It also helps avoid being overwhelmed by the volume of resources you must review. 

Here are some tips to make note-taking fruitful:

1. Think Of Your Research Goals

Your research goals will determine what you prioritise during note-taking. There’s no point in extensive note-taking if the content doesn’t help you strengthen your arguments or answer your research question. 

2. Use Headings, Subheadings, and Numbered List to Organise Key Ideas

This is similar to the information matrix we discussed above. However, this is more like a fleshed-out version. Use indenting and numbering to create idea hierarchies that distinguish major points from minor ones.  

3. Adopt One of the Many Note-Taking Methods

Examples of note-taking methods include the mapping method, the Cornell Method, the sentence method, and the outlining method. Diving into each of these techniques is beyond the scope of this article.  

4. Use Colours and Symbols

Create a colour code for identifying themes and crucial sections. You may also underline important keywords or circle data points that buttress certain themes. These colours and symbols help simplify and visualise ideas. 

5. Create Linkages Between Ideas

Highlight ideas or variables that have clear relationships. The relationship can be causal or correlational. State what this relationship means for your research question. 

6. Leave Space For Future Comments And Questions

Leave room to add more information, such as comments, questions, and reactions. As you read more, you’re likely to come across new information that may challenge or buttress the ideas you found earlier. 

10. Engage with Primary Sources

You may find the answer to a search engine query in a news article or even a random article. It’s advisable and more prudent to search for the primary source. 

So the Guardian can publish details about digital imaging delays in the United Kingdom, but NHS England is the primary source of that information. You must cite the latter and not the former. 

The same applies to academic sources. A paper may make a statement and cite another author or study. You must track the other study to cite it as a separate source in your bibliography. 

One of the importance of using primary sources is the secondary source may have taken the information out of context or reported the same to fit a particular narrative. 

Reading the primary source yourself ensures you have all the contexts and the data as the primary authors presented it. 

11. Avoid Confirmation Bias 

Simply searching for papers and studies that align with your position is a limiting research strategy. 

We recommend seeking studies and sources that challenge your assertion. This is a far more enriching prospect that adds depth to your research. 

Research projects don’t necessarily have to be right or wrong but a means to provide informed arguments based on facts, logical reasoning, and strong analytical skills. 

Research studies enhance ongoing conversations, adding a new point of view to the existing body of knowledge. 

Master Research Skills for Successful Research Reports

Academic success at all levels require research skills that can translate any topic into detailed, coherent, logical, and credible reports, whether it’s quantitative or qualitative research. 

We believe the tips outlined in this article can transform your research skills, but it requires putting them into practice. 

Not only would your research skills take a leap, but other attending skills like comprehension, analytical, and how to tie information together would also improve. 

Additionally, you’ll master project management, time management, and reference management tools useful in other areas of your life. 

Do you want to dig deeper into these strategies through specialised 1-on-1 tutorials or group sessions? Immerse Education’s Online Research Programme is tailor-made for specific subject study and led by tutors from world-renowned Oxbridge and Ivy League universities.Moreover, our accredited Online Research Programme is an excellent choice for students aged 14-18 who want to improve their research skills while earning valuable UCAS points for university applications. Explore our accredited Online Research Programme today.

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• Research Matters — to the Science Teacher

The Science Process Skills

Introduction.

One of the most important and pervasive goals of schooling is to teach students to think. All school subjects should share in accomplishing this overall goal. Science contributes its unique skills, with its emphasis on hypothesizing, manipulating the physical world and reasoning from data.

The scientific method, scientific thinking and critical thinking have been terms used at various times to describe these science skills. Today the term "science process skills" is commonly used. Popularized by the curriculum project, Science - A Process Approach (SAPA), these skills are defined as a set of broadly transferable abilities, appropriate to many science disciplines and reflective of the behavior of scientists. SAPA grouped process skills into two types-basic and integrated. The basic (simpler) process skills provide a foundation for learning the integrated (more complex) skills. These skills are listed and described below.

Basic Science Process Skills

Observing - using the senses to gather information about an object or event. Example: Describing a pencil as yellow. Inferring - making an "educated guess" about an object or event based on previously gathered data or information. Example: Saying that the person who used a pencil made a lot of mistakes because the eraser was well worn. Measuring - using both standard and nonstandard measures or estimates to describe the dimensions of an object or event. Example: Using a meter stick to measure the length of a table in centimeters. Communicating - using words or graphic symbols to describe an action, object or event. Example: Describing the change in height of a plant over time in writing or through a graph. Classifying - grouping or ordering objects or events into categories based on properties or criteria. Example: Placing all rocks having certain grain size or hardness into one group. Predicting - stating the outcome of a future event based on a pattern of evidence. Example: Predicting the height of a plant in two weeks time based on a graph of its growth during the previous four weeks.

Integrated Science Process Skills

Controlling variables - being able to identify variables that can affect an experimental outcome, keeping most constant while manipulating only the independent variable. Example: Realizing through past experiences that amount of light and water need to be controlled when testing to see how the addition of organic matter affects the growth of beans. Defining operationally - stating how to measure a variable in an experiment. Example: Stating that bean growth will be measured in centimeters per week. Formulating hypotheses - stating the expected outcome of an experiment. Example: The greater the amount of organic matter added to the soil, the greater the bean growth. Interpreting data - organizing data and drawing conclusions from it. Example: Recording data from the experiment on bean growth in a data table and forming a conclusion which relates trends in the data to variables. Experimenting - being able to conduct an experiment, including asking an appropriate question, stating a hypothesis, identifying and controlling variables, operationally defining those variables, designing a "fair" experiment, conducting the experiment, and interpreting the results of the experiment. Example: The entire process of conducting the experiment on the affect of organic matter on the growth of bean plants. Formulating models - creating a mental or physical model of a process or event. Examples: The model of how the processes of evaporation and condensation interrelate in the water cycle.

Learning basic process skills

Numerous research projects have focused on the teaching and acquisition of basic process skills. For example, Padilla, Cronin, and Twiest (1985) surveyed the basic process skills of 700 middle school students with no special process skill training. They found that only 10% of the students scored above 90% correct, even at the eighth grade level. Several researchers have found that teaching increases levels of skill performance. Thiel and George (1976) investigated predicting among third and fifth graders, and Tomera (1974) observing among seventh graders. From these studies it can be concluded that basic skills can be taught and that when learned, readily transferred to new situations (Tomera, 1974). Teaching strategies which proved effective were: (1) applying a set of specific clues for predicting, (2) using activities and pencil and paper simulations to teach graphing, and (3) using a combination of explaining, practice with objects, discussions and feedback with observing. In other words-just what research and theory has always defined as good teaching.

Other studies evaluated the effect of NSF-funded science curricula on how well they taught basic process skills. Studies focusing on the Science Curriculum Improvement Study (SCIS) and SAPA indicate that elementary school students, if taught process skills abilities, not only learn to use those processes, but also retain them for future use. Researchers, after comparing SAPA students to those experiencing a more traditional science program, concluded that the success of SAPA lies in the area of improving process oriented skills (Wideen, 1975; McGlathery, 1970). Thus it seems reasonable to conclude that students learn the basic skills better if they are considered an important object of instruction and if proven teaching methods are used.

Learning integrated process skills

Several studies have investigated the learning of integrated science process skills. Allen (1973) found that third graders can identify variables if the context is simple enough. Both Quinn and George (1975) and Wright (1981) found that students can be taught to formulate hypotheses and that this ability is retained over time.

Others have tried to teach all of the skills involved in conducting an experiment. Padilla, Okey and Garrard (1984) systematically integrated experimenting lessons into a middle school science curriculum. One group of students was taught a two week introductory unit on experimenting which focused on manipulative activities. A second group was taught the experimenting unit, but also experienced one additional process skill activity per week for a period of fourteen weeks. Those having the extended treatment outscored those experiencing the two week unit. These results indicate that the more complex process skills cannot be learned via a two week unit in which science content is typically taught. Rather, experimenting abilities need to be practiced over a period of time.

Further study of experimenting abilities shows that they are closely related to the formal thinking abilities described by Piaget. A correlation of +.73 between the two sets of abilities was found in one study (Padilla, Okey and Dillashaw, 1983). In fact, one of the ways that Piaget decided whether someone was formal or concrete was to ask that person to design an experiment to solve a problem. We also know that most early adolescents and many young adults have not yet reached their full formal reasoning capacity (Chiapetta, 1976). One study found only 17% of seventh graders and 34% of twelfth graders fully formal (Renner, Grant, and Sutherland, 1978).

What have we learned about teaching integrated science processes? We cannot expect students to excel at skills they have not experienced or been allowed to practice. Teachers cannot expect mastery of experimenting skills after only a few practice sessions. Instead students need multiple opportunities to work with these skills in different content areas and contexts. Teachers need to be patient with those having difficulties, since there is a need to have developed formal thinking patterns to successfully "experiment."

Summary and Conclusions

A reasonable portion of the science curriculum should emphasize science process skills according to the National Science Teachers Association. In general, the research literature indicates that when science process skills are a specific planned outcome of a science program, those skills can be learned by students. This was true with the SAPA and SCIS and other process skill studies cited in this review as well as with many other studies not cited.

Teachers need to select curricula which emphasize science process skills. In addition they need to capitalize on opportunities in the activities normally done in the classroom. While not an easy solution to implement, it remains the best available at this time because of the lack of emphasis of process skills in most commercial materials.

by Michael J. Padilla, Professor of Science Education, University of Georgia, Athens, GA

Allen, L. (1973). An examination of the ability of third grade children from the Science Curriculum Improvement Study to identify experimental variables and to recognize change.  Science Education, 57 , 123-151. Chiapetta, E. (1976). A review of Piagetian studies relevant to science instruction at the secondary and college level.  Science Education, 60 , 253-261. McGlathery, G. (1970). An assessment of science achievement of five and six-year-old students of contrasting socio-economic background.  Research and Curriculum Development in Science Education, 7023 , 76-83. McKenzie, D., & Padilla, M. (1984). Effect of laboratory activities and written simulations on the acquisition of graphing skills by eighth grade students. Paper presented at the annual meeting of the National Association for Research in Science Teaching, New Orleans. Padilla, M., Okey, J., & Dillashaw, F. (1983). The relationship between science process skills and formal thinking abilities.  Journal of Research in Science Teaching, 20 . Padilla, M., Cronin, L., & Twiest, M. (1985). The development and validation of the test of basic process skills. Paper presented at the annual meeting of the National Association for Research in Science Teaching, French Lick, IN. Quinn, M., & George, K. D. (1975). Teaching hypothesis formation.  Science Education, 59 , 289-296. Science Education, 62 , 215-221. Thiel, R., & George, D. K. (1976). Some factors affecting the use of the science process skill of prediction by elementary school children.  Journal of Research in Science Teaching, 13 , 155-166. Tomera, A. (1974). Transfer and retention of transfer of the science processes of observation and comparison in junior high school students.  Science Education, 58 , 195-203. Wideen, M. (1975). Comparison of student outcomes for Science - A Process Approach and traditional science teaching for third, fourth, fifth, and sixth grade classes: A product evaluation.  Journal of Research in Science Teaching, 12 , 31-39. Wright, E. (1981). The long-term effects of intensive instruction on the open exploration behavior of ninth grade students.  Journal of Research in Science Teaching, 18.

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• Springer Nature - PMC COVID-19 Collection

Fostering students’ motivation towards learning research skills: the role of autonomy, competence and relatedness support

Louise maddens.

1 Centre for Instructional Psychology and Technology, Faculty of Psychology and Educational Sciences, KU Leuven and KU Leuven Campus Kulak Kortrijk, Etienne Sabbelaan 51 – bus 7800, 8500 Kortrijk, Belgium

2 Itec, imec Research Group at KU Leuven, imec, Leuven, Belgium

3 Vives University of Applied Sciences, Kortrijk, Belgium

Fien Depaepe

Annelies raes.

In order to design learning environments that foster students’ research skills, one can draw on instructional design models for complex learning, such as the 4C/ID model (in: van Merriënboer and Kirschner, Ten steps to complex learning, Routledge, London, 2018). However, few attempts have been undertaken to foster students’ motivation towards learning complex skills in environments based on the 4C/ID model. This study explores the effects of providing autonomy, competence and relatedness support (in Deci and Ryan, Psychol Inquiry 11(4): 227–268, https://doi.org/10.1207/S15327965PLI1104_01, 2000) in a 4C/ID based online learning environment on upper secondary school behavioral sciences students’ cognitive and motivational outcomes. Students’ cognitive outcomes are measured by means of a research skills test consisting of short multiple choice and short answer items (in order to assess research skills in a broad way), and a research skills task in which students are asked to integrate their skills in writing a research proposal (in order to assess research skills in an integrative manner). Students’ motivational outcomes are measured by means of students’ autonomous and controlled motivation, and students’ amotivation. A pretest-intervention-posttest design was set up in order to compare 233 upper secondary school behavioral sciences students’ outcomes among (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. Both learning environments proved equally effective in improving students’ scores on the research skills test. Students in the need supportive condition scored higher on the research skills task compared to their peers in the baseline condition. Students’ autonomous and controlled motivation were not affected by the intervention. Although, unexpectedly, students’ amotivation increased in both conditions, students’ amotivation was lower in the need supportive condition compared to students in the baseline condition. Theoretical relationships were established between students’ need satisfaction, students’ motivation (autonomous, controlled, and amotivation), and students’ cognitive outcomes. These findings are discussed taking into account the COVID-19 affected setting in which the study took place.

Introduction

Several scholars have argued that the process of learning research skills is often obstructed by motivational problems (Lehti & Lehtinen, 2005 ; Murtonen, 2005 ). Some even describe these issues as students having an aversion towards research (Pietersen, 2002 ). Examples of motivational problems are that students experience research courses as boring, inaccessible, or irrelevant to their daily lives (Braguglia & Jackson, 2012 ). In a research synthesis on teaching and learning research methods, Earley ( 2014 ) argues that students fail to see the relevance of research methods courses, are anxious or nervous about the course, are uninterested and unmotivated to learn the material, and have poor attitudes towards learning research skills. It should be mentioned that the studies mentioned above focused on the field of higher university education. In upper secondary education, to date, students’ motivation towards learning research skills has rarely been studied. As difficulties while learning research seem to relate to problems involving students’ previous experiences regarding learning research skills (Murtonen, 2005 ), we argue that fostering students’ motivation from secondary education onwards is a promising area of research.

The current study combines insights from instructional design theory and self-determination theory (SDT, Deci & Ryan, 2000 ), in order to investigate the cognitive and motivational effects of providing psychological need support (support for the need for autonomy, competence and relatedness) in a 4C/ID based (van Merriënboer & Kirschner, 2018 ) online learning environment fostering upper secondary schools students’ research skills. In the following section, we elaborate on the definition of research skills in the understudied domain of behavioral sciences; on 4C/ID (van Merriënboer & Kirschner, 2018 ) as an instructional design model for complex learning; and on self-determination theory and its related need theory (Deci & Ryan, 2000 ). In addition, the research questions addressed in the current study are outlined.

Conceptual framework

Research skills.

As described by Fischer et al., ( 2014 , p. 29), we define research skills 1 as a broad set of skills used “to understand how scientific knowledge is generated in different scientific disciplines, to evaluate the validity of science-related claims, to assess the relevance of new scientific concepts, methods, and findings, and to generate new knowledge using these concepts and methods”. Furthermore, eight scientific activities learners engage in while performing research are distinguished, namely: (1) problem identification, (2) questioning, (3) hypothesis generation, (4) construction and redesign of artefacts, (5) evidence generation, (6) evidence evaluation, (7) drawing conclusions, and (8) communicating and scrutinizing (Fischer et al., 2014 ). Fischer et al. ( 2014 ) argue that both the nature of, and the weights attributed to each of these activities, differ between domains. Intervention studies aiming to foster research skills are almost exclusively situated in natural sciences domains (Engelmann et al., 2016 ), leaving behavioral sciences domains largely understudied. The current study focuses on research skills in the understudied domain of behavioral sciences. We refer to the domain of behavioral sciences as the study of questions related to how people behave, and why they do so. Human behavior is understood in its broadest sense, and is the study of object in fields of psychology, educational sciences, cultural and social sciences.

The design of the learning environments used in this study is based on an existing instructional design model, namely the 4C/ID model (van Merriënboer & Kirschner, 2018 ). The 4C/ID model has been proven repeatedly effective in fostering complex skills (Costa et al., 2021 ), and thus drew our attention for the case of research skills, as research skills can be considered complex skills (it requires learners to integrate knowledge, skills and attitudes while performing complex learning tasks). Since the 4C/ID model focusses on supporting students’ cognitive outcomes, it might not be considered as relevant from a motivational point of view. However, since we argue that a deliberately designed learning environment from a cognitive point of view is an important prerequisite to provide qualitative motivational support, we briefly sketch the 4C/ID model and its characteristics. The 4C/ID model has a comprehensive character, integrating insights from different theories and models (Merrill, 2002 ), and highlights the relevance of four crucial components: learning tasks, supportive information, part task-practice, and just-in-time information. Central characteristics of these four components are that (a) high variability in authentic learning tasks is needed in order to deal with the complexity of the task; (b) supportive information is provided to the students in order to help them build mental models and strategies for solving the task under study (Cook & McDonald, 2008 ); (c) part-task practice is provided for recurrent skills that need to be automated; and (d) just-in-time (procedural) information is provided for recurrent skills.

Taking into account students’ cognitive struggles regarding research skills, and the existing research on the role of support in fostering research skills (see for example de Jong & van Joolingen, 1998 ), the 4C/ID model was found suitable to design a learning environment for research skills. This is partly because of its inclusion of (almost) all of the support found effective in the literature on research skills, such as providing direct access to domain information at the appropriate moment, providing learners with assignments, including model progression, the importance of students’ involvement in authentic activities, and so on (Chi, 2009 ; de Jong, 2006 ; de Jong & van Joolingen, 1998 ; Engelmann et al., 2016 ). While mainly implemented in vocational oriented programs, the 4C/ID model has been proposed as a good model to design learning environments aiming to foster research skills as well (Bastiaens et al., 2017 ; Maddens et al., 2020b ). Indeed, acquiring research skills requires complex learning processes (such as coordinating different constituent skills). Overall, the 4C/ID model can be considered to be highly suitable for designing learning environments aiming to foster research skills. Given its holistic design approach, it helps “to deal with complexity without losing sight of the interrelationships between the elements taught” (van Merriënboer & Kirschner, 2018 , p. 5).

Although the 4C/ID model has been used widely to construct learning environments enhancing students’ cognitive outcomes (see for example Fischer, 2018 ), research focusing on students’ motivational outcomes related to the 4C/ID model is scarce (van Merriënboer & Kirschner, 2018 ). Van Merriënboer and Kirschner ( 2018 ) suggest self-determination theory (SDT; Deci & Ryan, 2000 ) and its related need theory as a sound theoretical framework to investigate motivation in relation to 4C/ID.

Self-determination theory

Self-determination theory (SDT; Deci & Ryan, 2000 ) provides a broad framework for the study of motivation and distinguishes three types of motivation: amotivation (a lacking ability to self-regulate with respect to a behaviour), extrinsic motivation (extrinsically motivated behaviours, be they self-determined versus controlled), and intrinsic motivation (the ‘highest form’ of self-determined behaviour) (Deci & Ryan, 2000 ). According to Deci and Ryan ( 2000 , p. 237), intrinsic motivation can be considered “a standard against which the qualities of an extrinsically motivated behavior can be compared to determine its degree of self-determination”. Moreover, the authors (Deci & Ryan, 2000 , p. 237) argue that “extrinsic motivation does not typically become intrinsic motivation”. As the current study focuses on research skills in an academic context in which students did not voluntary chose to learn research skills, and thus learning research skills can be considered instrumental (directed to attaining a goal), the current study focuses on students’ amotivation, and students’ extrinsic motivation, realistically striving for the most self-determined types of extrinsic motivation.

Four types of extrinsic motivation are distinguished by SDT (external regulation, introjection, identification, and integration). These types can be categorized in two overarching types of motivation (autonomous and controlled motivation). Autonomous motivation contains the integrated and identified regulation towards a task (be it because the task is considered interesting, or because the task is considered personally relevant respectively). Controlled motivation refers to the external and introjected regulation towards the task (as a consequence of external or internal pressure respectively) (Vansteenkiste et al., 2009 ). More autonomous types of motivation have been found to be related to more positive cognitive and motivational outcomes (Deci & Ryan, 2000 ).

SDT further maintains that one should consider three innate psychological needs related to students’ motivation. These needs are the need for autonomy, the need for competence, and the need for relatedness. The need for autonomy can be described as the need to experience activities as being “concordant with one’s integrated sense of self” (Deci & Ryan, 2000 , p. 231). The need for competence refers to the need to feel effective when dealing with the environment (Deci & Ryan, 2000 ). The need for relatedness contains the need to have close relationships with others, including peers and teachers (Deci & Ryan, 2000 ). The satisfaction of these needs is hypothesized to be related to more internalization, and thus to more autonomous types of motivation (Deci & Ryan, 2000 ). This relationship has been studied frequently (for a recent overview, see Vansteenkiste et al., 2020 ). Indeed, research established the positive relationships between perceived autonomy (see for example Deci et al., 1996 ), perceived competence (see for example Vallerand & Reid, 1984 ), and perceived relatedness (see for example Ryan & Grolnick, 1986 for a self-report based study) with students’ more positive motivational outcomes. Apart from students’ need satisfaction, several scholars also aim to investigate need frustration as a different notion, as “it involves an active threat of the psychological needs (rather than a mere absence of need satisfaction)” (Vansteenkiste et al., 2020 , p. 9). In what follows, possible operationalizations are defined for the three needs.

Possible operationalizations of autonomy need support found in the literature are: teachers accepting irritation or negative feelings related to aspects of a task perceived as “uninteresting” (Reeve, 2006 ; Reeve & Jang, 2006 ; Reeve et al., 2002 ); providing a meaningful rationale in order to explain the value/usefulness of a certain task and stressing why involving in the task is important or why a rule exists (Deci & Ryan, 2000 ); using autonomy-supportive, inviting language (Deci et al., 1996 ); and allowing learners to regulate their own learning and to work at their own pace (Martin et al., 2018 ). Related to competence support, possible operationalizations are: providing a clear task rationale and providing structure (Reeve, 2006 ; Vansteenkiste et al., 2012 ); providing informational positive feedback after a learning activity (Deci et al., 1996 ; Martin et al., 2018 ; Vansteenkiste et al., 2012 ); providing an indication of progress and dividing content into manageable blocks (Martin et al., 2018 ; Schunk, 2003 ); and evaluating performance by means of previously introduced criteria (Ringeisen & Bürgermeister, 2015 ). Possible operationalizations concerning relatedness support are: teacher’s relational supports (Ringeisen & Bürgermeister, 2015 ); encouraging interaction between course participants and providing opportunities for learners to connect with each other (Butz & Stupnisky, 2017 ; van Merriënboer & Kirschner, 2018 ); using a warm and friendly approach or welcoming learners personally into a course (Martin et al., 2018 ); and offering a platform for learners to share ideas and to connect (Butz & Stupnisky, 2017 ; Martin et al., 2018 ).

In the current research, SDT is selected as a theoretical framework to investigate students’ motivation towards learning research skills, as, in contrast to other more purely goal-directed theories, it includes the concept of innate psychological needs or the Basic Psychological Need Theory (Deci & Ryan, 2000 ; Ryan, 1995 ; Vansteenkiste et al., 2020 ), and it describes the relation between these perceived needs and students’ autonomous motivation: higher levels of perceived needs relate to more autonomous forms of motivation. The inclusion of this need theory is considered an advantage in the case of research skills because research revealed problems of students with respect to both their feelings of competence in relation to research skills (Murtonen, 2005 ), as their feelings of autonomy in relation to research skills (Martin et al., 2018 ), as was indicated in the introduction. As such, fostering students’ psychological needs while learning research skills seems a promising way of fostering students’ motivation towards learning research skills.

4C/ID and SDT

One study (Bastiaens et al., 2017 ) was found to implement need support in 4C/ID based learning environments, comparing a traditional module, a 4C/ID based module and an autonomy supportive 4C/ID based module in a vocational undergraduate education context. Autonomy support was operationalized by means of providing choice to the learners. No main effect of the conditions was found on students’ motivation. Surprisingly, providing autonomy support did also not lead to an increase in students’ autonomy satisfaction. Similarly, no effects were found on students’ relatedness and competence satisfaction. Remarkably, students did qualitatively report positive experiences towards the need support, but this did not reflect in their quantitatively reported need experiences. In a previous study performed in the current research trajectory, Maddens et al. ( under review ) investigated the motivational effects of providing autonomy support in a 4C/ID based online learning environment fostering students’ research skills, compared to a learning environment not providing such support. Autonomy support was operationalized as stressing task meaningfulness to the students. Based on insights from self-determination theory, it was hypothesized that students in the autonomy condition would show more positive motivational outcomes compared to students in the baseline condition. However, results showed that students’ motivational outcomes appeared to be unaffected by the autonomy support. One possible explanation for this unexpected finding was that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support (Deci & Ryan, 2000 ; Niemiec & Ryan, 2009 ), and thus, that the intervention was insufficiently powerful for effects to occur. Autonomy support has often been manipulated in experimental research (Deci et al., 1994 ; Reeve et al., 2002 ; Sheldon & Filak, 2008 ). However, the three needs are rarely simultaneously manipulated (Sheldon & Filak, 2008 ).

Integrated need support

Although not making use of 4C/ID based learning environments, some scholars have focused on the impact of integrated (autonomy, competence and relatedness) need support on learners’ motivation. For example, Raes and Schellens ( 2015 ) found differential effects of a need supportive inquiry environment on upper secondary school students’ motivation: positive effects on autonomous motivation were only found in students in a general track, and not in students in a science track. This indicates that motivational effects of need-supportive environments might differ between tracks and disciplines. However, Raes and Schellens ( 2015 ) did not experimentally manipulate need support, as the learning environment was assumed to be need-supportive and was not compared to a non-need supportive learning environment. Pioneers in manipulating competence, relatedness and autonomy support in one study are Sheldon and Filak ( 2008 ), predicting need satisfaction and motivation based on a game-learning experience with introductory psychology students. Relatedness support (mainly operationalized by emphasizing interest in participants’ experiences in a caring way) had a significant effect on intrinsic motivation. Competence support (mainly operationalized by means of explicating positive expectations) had a marginal significant effect on intrinsic motivation. No main effects on intrinsic motivation were found regarding autonomy support (mainly operationalized by means of emphasizing choice, self-direction and participants’ perspective upon the task). However, as is often the case in motivational research based on SDT, the task at hand was quite straight forward (a timed task in which students try to form as many words as possible from a 4 × 4 letter grid), and thus, the applicability of the findings for providing need support in 4C/ID based learning environments for complex learning might be limited.

In the preceding section, several operationalizations of need support were discussed. Deci and Ryan ( 2000 ) argue that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support. However, such integrated need support has rarely been empirically studied (Sheldon & Filak, 2008 ). In addition, research investigating how need support can be implemented in learning environments based on the 4C/ID model is particularly scarce (van Merriënboer & Kirschner, 2018 ). This study aims to combine insights from instructional design theory for complex learning (van Merriënboer & Kirschner, 2018 ) and self-determination theory (Deci & Ryan, 2000 ) in order to investigate the motivational effects of providing need support in a 4C/ID based learning environment for students’ research skills. A pretest-intervention-posttest design is set up in order to compare 233 upper secondary school behavioral sciences students’ cognitive and motivational outcomes among two conditions: (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. The following research questions are answered based on a combination of quantitative and qualitative data (see ‘method’): (1) Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task? ; ( 2) What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes (i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)? ; (3) What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)? ; (4) How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment? .

The first three questions are answered by means of quantitative data. Since the learning environment is constructed in line with existing instructional design principles for complex learning, we hypothesize that both learning environments will succeed in improving students’ research skills (RQ1). Relying on insights from self-determination theory (Deci & Ryan, 2000 ), we hypothesize that providing need support will enhance students’ autonomous motivation (RQ2). In addition, we hypothesize students’ need satisfaction to be positively related to students’ autonomous motivation (RQ3). These hypotheses on the relationship between students’ needs and students’ motivation rely on Vallerands’ ( 1997 ) finding that changes in motivation can be largely explained by students’ perceived competence, autonomy and relatedness (as psychological mediators). More specifically, Vallerand ( 1997 ) argues that environmental factors (in this case the characteristics of a learning environment) influence students’ perceptions of competence, autonomy, and relatedness, which, in turn, influence students’ motivation and other affective outcomes. In addition, based on the self-determination literature (Deci & Ryan, 2000 ), we expect students’ motivation to be positively related to students’ cognitive outcomes. In order to answer the fourth research question, qualitative data (students’ qualitative feedback on the learning environments) is analysed and categorized based on the need satisfaction and need frustration concepts (RQ4) in order to thoroughly capture the meaning of the quantitative results collected in light of RQ1–3. No hypotheses are formulated in this respect.

Methodology

Participants.

The study took place in authentic classroom settings in upper secondary behavioral sciences classes. In total, 233 students from 12 classes from eight schools in Flanders participated in the study. All participants are 11th or 12th grade students in a behavioral sciences track 2 in general upper secondary education in Flanders (Belgium). Classes were randomly assigned to one out of two experimental conditions. Of all 233 students, 105 students (with a mean age of 16.32, SD 0.90) worked in the baseline condition (of which 62% 11th grade students, 36% 12th grade students, and 2% not determined; and of which 31% male, 68% female, and 1% ‘other’), and 128 students (with a mean age of 16.02, SD 0.59) worked in the need supportive condition (of which 80% 11th grade students, and 20% 12th grade students; and of which 19% male, and 81% female). As the current study did not randomly assign students within classes to one out of the two conditions, this study should be considered quasi-experimental. Full randomization was considered but was not feasible as students worked in the learning environments in class, and would potentially notice the experimental differences when observing their peers working in the learning environment. As such, we argued that this would potentially cause bias in the study. By taking into account students’ pretest scores on the relevant variables (cognitive and motivational outcomes) as covariates, we aimed to adjust for inter-conditional differences. No such differences were found for students’ autonomous motivation t (226) =  − 0.115, p  < 0.909, d  = 0.015, and students’ amotivation t (226) =  − 0.658, p  < 0.511, d  =  − 0.088. However, differences were observed for students’ controlled motivation t (226) =  − 2.385, p  < 0.018, d  =  − 0.318, and students’ scores on the LRST pretest t (225) = − 5.200, p  < 0.001, d  =  − 0.695.

Study design and procedure

In a pretest session of maximum two lesson hours, the Leuven Research Skills Test (LRST, Maddens et al., 2020a ), the Academic Self-Regulation Scale (ASRS, Vansteenkiste et al., 2009 ), and four items related to students’ amotivation (Aydin et al., 2014 ) were administered in class via an online questionnaire, under supervision of the teacher. In the subsequent eight weeks, participants worked in the online learning environment, one hour a week. Out of the 233 participating students, 105 students studied in a baseline online learning environment. The baseline online learning environment 3 is systematically designed using existing instructional design principles for complex learning based on the 4C/ID model (van Merriënboer & Kirschner, 2018 ). All four components of the 4C/ID model were taken into account in the design process: regarding the first component, the learning tasks included real-life, authentic cases. More specifically, tasks were selected from the domains of psychology, educational sciences and sociology. As such, there was a large variety in the cases used in the learning tasks. This large variety in learning tasks is expected to facilitate transfer of learners’ research skills in a wide range of contexts. Furthermore, the tasks were ill-structured and required learners to make judgments, in order to provoke deep learning processes. Regarding the second component, supportive information was provided for complex tasks in the learning environment, such as formulating a research question, where students can consult general information on what constitutes a good research question, can consult examples or demonstrations of this general information, and can receive cognitive feedback on their answers (for example by means of example answers). Examples of the implementation of the third component (procedural information) are the provision of information on how to recognize a dependent and an independent variable by means of on-demand (just-in-time) presentation by means of pop-ups; information on how to use Boolean operators; and information on how to read a graph. To avoid split attention, this kind of information was integrated with the task environment itself (van Merriënboer & Kirschner, 2018 ). Finally, the fourth component, part-task-practice (by means of short tests) was implemented for routine aspects of research skills that should be automated, for example the formulation of a search query.

The remaining participating students ( n  = 128) completed an adapted version of the baseline online learning environment, in which autonomy, relatedness and competence support are provided. In total, need support consisted of 12 implementations (four implementations for each need), based on existing research on need support. An overview of these adaptations can be found in Tables ​ Tables1 1 and ​ and2. 2 . Although, ideally, students would work in class, under supervision of their teacher, this was not possible for all classes, due to the COVID-19 restrictions. 4 As a consequence, some students completed the learning environment partly at home. All students were supervised by their teachers (be it virtually or in class), and the researcher kept track of students’ overall activities in order to be able to contact students who did not complete the main activities. During the last two sessions of the intervention, participants submitted a two-pages long research proposal (“two-pager”). One week after the intervention, the LRST (Maddens et al., 2020a ), the ASRS (Vansteenkiste et al., 2009 ), four items related to students’ amotivation (Aydin et al., 2014 ), the value/usefulness scale (Ryan, 1982 ) and the Basic Psychological Need Satisfaction and Frustration Scale (BPNSNF, Chen et al., 2015 ) were administered in a posttest session of maximum two hours. Although most classes succeeded in organizing this posttest session in class, for some classes this posttest was administered at home. However, all classes were supervised by the teacher (be it virtually or in class). These contextual differences at the test moments will be reflected upon in the discussion section.

Adaptations online learning environment

Overview instruments

a When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively

Instruments

In this section, we elaborate on the tests used during the pretest and the posttest. Example items for each scale are presented in Appendix 1.

Motivational outcomes

In the current study, two groups of motivational outcomes are assessed: (1) students’ need satisfaction and frustration, and students’ experiences of value/usefulness; and (2) students’ level of autonomous motivation, controlled motivation, and amotivation. When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively.

The BPNSNF-training scale (The Basic Psychological Need Satisfaction and Frustration Scale, Chen et al., 2015 ; translated version Aelterman et al., 2016 5 ) measured students’ need satisfaction and need frustration while working in the learning environment, and consists of 24 items (four items per scale): (autonomy satisfaction, α  = 0.67; ω = 0.67; autonomy frustration, α  = 0.76; ω = 0.76; relatedness satisfaction, α  = 0.79; ω = 0.79; relatedness frustration, α  = 0.60; ω = 0.61; competence satisfaction, α  = 0.72; ω = 0.73; competence frustration, α  = 0.68; ω = 0.67). The items are Likert-type items ranging from one (not at all true) to five (entirely true). Although the current study focusses mainly on students’ need satisfaction, the scales regarding students’ need frustration are included in order to be able to also detect students’ potential ill-being and in order to detect potential critical issues regarding students’ needs. In addition to the BPNSNF, by means of seven Likert-type items ranging from one (not at all true) to seven (entirely true), the (for the purpose of this research translated) value/usefulness scale of the Intrinsic Motivation Inventory (IMI, Ryan, 1982 ) measured to what extent students valued the activities of the online learning environment ( α  = 0.92; ω = 0.92). Since in the research skills literature problems have been observed related to students’ perceived value/usefulness of research skills (Earley, 2014 ; Murtonen, 2005 ), and this concept is not sufficiently stressed in the BPNSNF-scale, we found it useful to include this value/usefulness scale to the study. The difference in the range of the answer possibilities (one to five vs one to seven) exists because we wanted to keep the range as initially prescribed by the authors of each instrument. All motivational measures are calculated by adding the scores on every item, and dividing this sum score by the number of items on a scale, leading to continuous outcomes. Although the IMI and the BPNSNF targeted students’ experiences while completing the online learning environment, these measures were administered during the posttest. Thus, students had to think retrospectively about their experiences. In order to prevent cognitive overload while completing the online learning environment, these measures were not administered during the intervention itself.

Students’ autonomous and controlled motivation towards learning research skills was measured by means of the Dutch version of the Academic Self-Regulation Scale (ASRS; Vansteenkiste et al., 2009 ), adapted to ‘ research skills ’. The ASRS consists of Likert-type items ranging from one (do not agree at all) to five (totally agree), and contains eight items per subscale (autonomous and controlled motivation). In the autonomous motivation scale, four items are related to identified regulation, and four items are related to intrinsic motivation. 6 In the controlled motivation scale, four items are related to external regulation, and four items are related to introjected regulation. Both scales (autonomous motivation and controlled motivation) indicated good internal consistency for the study’s data (autonomous motivation: α  = 0.91; 0.92; ω = 0.90; 0.92; controlled motivation: α  = 0.83; 0.86; ω = 0.82; 0.85). The items were adapted to the domain under study (motivation to learn about research skills). Based on students’ motivational issues related to research skills, we found it useful to also include a scale to assess students’ amotivation. This was measured with (for the purpose of the current research translated) four items related to students’ amotivation regarding learning research skills, adapted from Academic Motivation Scale for Learning Biology (Aydin et al., 2014 ) ( α  = 0.80; 0.75; ω = 0.81; 0.75). Also this measure consist of Likert-type items ranging from one (do not agree at all) to five (totally agree).

Cognitive outcomes

Students’ research skills proficiency was measured by means of a research skills test (Maddens et al., 2020a ) and a research skills task.

The research skills test used in this study is the LRST (Maddens et al., 2020a ) consisting of a combination of 37 open ended and close ended items ( α  = 0.79; 0.82; ω = 0.78; ω = 0.80 for this data set), administered via an online questionnaire. Each item of the LRST is related to one of the eight epistemic activities regarding research skills as mentioned in the introduction (Fischer et al., 2014 ), and is scored as 0 or 1. The total score on the LRST is calculated by adding the mean subscale scores (related to the eight epistemic activities), and dividing them by eight (the number of scales). In a previous study (Maddens et al., 2020a ), the LRST was checked and found suitable in light of interrater reliability ( κ  = 0.89). As the same researchers assessed the same test with a similar cohort in the current study, the interrater reliability was not calculated for this study.

In the research skills task (“two pager task”), students were asked to write a research proposal of maximum two pages long. The concrete instructions for this research proposal are given in Appendix 1. In this research proposal, students were asked to formulate a research question and its relevance; to explain how they would tackle this research question (method and participants); to explain their hypotheses or expectations; and to explain how they would communicate their results. The two-pager task was analyzed using a pairwise comparison technique, in which four evaluators (i.e. the four authors of this paper) made comparative judgements by comparing two two-pagers at a time, and indicating which two-pager they think is best. All four evaluators are researchers in educational sciences and are familiar with the research project and with assessing students’ texts. This shared understanding and expertise is a prerequisite for obtaining reliable results (Lesterhuis et al., 2018 ). The comparison technique is performed by means of the Comproved tool ( https://comproved.com ). As described by Lesterhuis et al. ( 2018 , p. 18), “the comparative judgement method involves assessing a text on its overall quality. However, instead of requiring an assessor to assign an absolute score to a single text, comparative judgement simplifies the process to a decision about which of two texts is better”. In total, 1635 comparisons were made (each evaluator made 545 comparisons), and this led to a (interrater)reliability score of 0.79. In a next step, these comparative judgements were used to rank the 218 products (15 students did not submit a two-pager) on their quality; and the products were graded based on their ranking. This method was used to grade the two-pagers because it facilitates the holistic evaluation of the tasks, based on the judgement of multiple experts (interrater reliability).

Qualitative feedback

Students’ experiences with the online learning environment were investigated in the online learning environment itself. After completing the learning environment, students were asked how they experienced the tasks, the theory, the opportunity to post answers in the forum and to ask questions via the chat, what they liked or disliked in the online learning environment, and what they disliked in the online learning environment (Fig.  1 ).

Study overview

The first research question (” Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task?” ) is answered by means of a paired samples t -test in order to look for overall improvements in order to detect potential general trends, followed by a full factorial MANCOVA, as this allows us to investigate the effectiveness for both conditions taking into account students’ pretest scores. Hence, the condition is included as an experimental factor, and students’ scores on the LRST and the two-pager task are included as continuous outcome variables. Students’ pretest scores on the LRST are included as a covariate. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariate.

The second research question (“ What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes, i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?”) ;) is answered by means of a full factorial MANCOVA. The condition (need satisfaction condition versus baseline condition) is included as an experimental factor, and students’ responses on the value/usefulness, autonomous and controlled motivation, amotivation, and need satisfaction scales are included as continuous outcome variables. ASRS pretest scores (autonomous and controlled motivation) are included as covariates in order to test the differences between group means, adjusted for students’ a priori motivation. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariates, and assumptions to be met to perform a MANCOVA are checked. 7

The third research question ( “ What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?” ), is initially answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students’ value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables. The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores, and (5) scores on the two-pager task as dependent variables. As a follow-up analysis (see ‘ results ’) two additional regression analyses are performed to look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST (6) and two-pager (7)). As the goal of this analysis is to investigate the relationships between variables as described in SDT research, this analysis focuses on the full sample, rather than distinguishing between the two conditions. An ‘Enter’ method (Field, 2013 ) is used in order to enter the independent variables simultaneously (in line with Sheldon et al., 2008 ).

The fourth research question (“ How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment?” ) is analyzed by means of the knowledge management tool Citavi. Based on the theoretical framework, students’ experiences are labeled by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. For example, students’ quotes referring to the value/usefulness of the learning environment, are labeled as ‘autonomy satisfaction’ or ‘autonomy frustration’. Students’ references towards their feelings of mastery of the learning content are labeled as ‘competence satisfaction’ or ‘competence frustration’. Students’ quotes regarding their relationships with peers and teachers are labeled as ‘relatedness satisfaction’ or ‘relatedness frustration’ (Fig.  2 ).

Overview variables

Does the deliberately designed (4C/ID based) learning environments improve students’ research skills, as measured by a research skills test and a research skills task?

Paired samples t -test. A paired samples t -test reveals that, in general, students ( n  = 210) improved on the LRST-posttest ( M  = 0.57, SD  = 0.16) compared to the pretest ( M  = 0.51, SD  = 0.15) (range 0–1). The difference between the posttest and the pretest is significant t (209) =  − 8.215, p  < 0.001, d 8  =  − 0.567. The correlation between the LRST pretest and posttest is 0.70 ( p  < 0.010).

MANCOVA. A MANCOVA model ( n  = 196) was defined checking for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariate LRST pretest did not show significant interaction effects for the two outcome variables LRST post ( p  = 0.259) and the two-pager task ( p  = 0.702). The correlation between the outcome variables (LRST post and two-pager), is 0.28 ( p  < 0.050).

Of all 233 students, 36 students were excluded from the main analysis because of missing data (for example, because they were absent during a pretest or posttest moment). These students were excluded by means of a listwise deletion method because we found it important to use a complete dataset, since, in a lot of cases, students who did not complete the pretest or posttest, did also not complete the entire learning environment. Including partial data for these students could bias the results. The baseline condition counted 86 students, and the need satisfaction condition counted 111 students. Using Pillai’s Trace [ V  = 0.070, F (2,193) = 7.285, p  ≤ 0.001], there was a significant effect of the condition on the cognitive outcome variables, taking into account students’ LRST pretest scores. Separate univariate ANOVAs on the outcome variables revealed no significant effect of the condition on the LRST posttest measure, F (1,194) = 2.45, p  = 0.120. However, a significant effect of condition was found on the two-pager scores, F (1,194) = 13.69, p  < 0.001 (in the baseline group, the mean score was 6,6/20; in the need condition group, the mean score was 7,6/20). It should be mentioned that both scores are rather low.

What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID based) learning environment fostering students’ research skills, on students’ motivational outcomes (students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?

Paired samples t -tests. The correlations between students’ pretest and posttestscores for the motivational measures are 0.67 ( p  < 0.010) for autonomous motivation; 0.44 ( p  < 0.010) for controlled motivation, and 0.38 for amotivation ( p  < 0.010). Regarding the differences in students’ motivation, three unexpected findings were observed. Overall, students’ ( n  = 215) amotivation was higher on the posttest ( M  = 2.26, SD  = 0.89) compared to the pretest ( M  = 1.77, SD  = 0.79) (based on a score between 1 and 5). The difference between the posttest and the pretest is significant t (214) =  − 7.69, p  < 0.001, d  =  − 0.524. Further analyses learn that the amotivation means in the baseline group increased with 0.65, and the amotivation in the need support group increased with 0.37. In addition, students’ ( n  = 215) autonomous motivation was higher on the pretest ( M  = 2.81, SD  = 0.81) compared to the posttest ( M  = 2.64, SD  = 0.82). The difference between the posttest and the pretest is significant t (214) = 3.72, p  < 0.001, d  = 0.254. Students’ mean scores on autonomous motivation in the baseline condition decreased with 0.19, and students’ autonomous motivation in the need support condition decreased with 0.15. Students’ ( n  = 215) controlled motivation was higher on the posttest ( M  = 2.33, SD  = 0.75) compared to the pretest ( M  = 1.93, SD  = 0.67). The difference between the posttest and the pretest is significant t (214) =  − 07.72, p  < 0.001, d  =  − 0.527. Students’ controlled motivation in the baseline group increased with 0.36, and students’ controlled motivation in the need support group increased with 0.43. However, overall, all mean scores are and stay below neutral score (below 3), indicating robust low autonomous, controlled and amotivation scores (see Table ​ Table3). 3 ). An independent samples T -test on the mean differences between these measures shows that the increases/decreases on autonomous motivation [ t (213) =  − 0.506, p  = 0.613, d  =  − 0.069] and controlled motivation [ t (213) =  − 0.656, p  = 0.513, d  =  − 0.090] did not differ between the two groups. However, the increases in amotivation [ t (213) = 2.196, p  = 0.029, d  = 0.301] does differ significantly between the two conditions. More specifically, the increase was lower in the need supportive condition compared to the baseline condition.

Mean scores and standard deviations motivational variables

a Overall, students’ ( n  = 215) autonomous motivation was significantly higher on the pretest compared to the posttest ( t (214) 3.72, p  ≤ 0.001, d  = 0.254

b Students’ (n = 215) controlled motivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 7.72, p  ≤ 0.001, d  =  − 0.527

c Students’ ( n  = 215) amotivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 07,69, p  ≤ 0.001, d  =  − 0.534)

MANCOVA. Of all 233 students, 18 students were excluded from the analysis because of missing data (for example, because they were absent during a pretest or posttest moment). Compared to the cognitive analyses, the amount of missing data is lower concerning motivational outcomes since, concerning the cognitive outcomes, some students did not complete the two-pager task. However, we found it important to use all relevant data and chose to report this is in a clear way. In total, the baseline condition counted 97 students, and the experimental condition counted 118 students. Similar to the analysis for the cognitive outcomes, a MANCOVA model was defined to check for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariates did not show significant interaction effects for the outcome variables. 9

Using Pillai’s Trace [ V  = 0.113, F (10,201) = 2.558, p  = 0.006], there was a significant effect of condition on the motivational variables, taking into account students’ autonomous and controlled pretest scores, and students’ a priori amotivation. Separate univariate ANOVAs on the outcome variables revealed a significant effect of the condition on the outcome variables amotivation, F (1,210) = 3.98, p  = 0.047; and relatedness satisfaction F (1,210) = 6.41, p  = 0.012. As was hypothesized, students in the need satisfaction group reported less amotivation ( M  = 2.38), compared to students in the baseline group ( M  = 2.18). In contrast to what was hypothesized, students in the need satisfaction group reported less relatedness satisfaction ( M  = 2.43) compared to students in the baseline group ( M  = 2.73), and no significant effects of condition were found on the outcome variables autonomous motivation post, controlled motivation post, value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, and relatedness frustration. Table ​ Table4 4 shows the correlations between the motivational outcome variables.

Correlations motivational outcome variables

AM autonomous motivation, CM controlled motivation, AMOT amotivation, VU value/usefulness, AS autonomy satisfaction, AF autonomy frustration, CS competence satisfaction, CF competence frustration, RS relatedness satisfaction, RF relatedness frustration

**Correlation is significant at the 0.010 level (2-tailed)

*Correlation is significant at the 0.050 level (2-tailed)

What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?

The third research question (investigating the relationships between students’ need satisfaction, students’ motivation and students’ cognitive outcomes), is answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables ( n  = 219). The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores ( n  = 215), and (5) scores on the two-pager task as dependent variables ( n  = 206). Table ​ Table4 4 depicts the correlations for the first three analyses. Table ​ Table5 5 depicts the correlations for the last two analyses.

Correlations motivational and cognitive outcome variables

AM  autonomous motivation, CM  controlled motivation, AMOT  amotivation, LRST  score on LRST, Twopager  score on Twopager

In Table ​ Table3, 3 , we can see that students in both conditions experience average competence and autonomy satisfaction. However, students’ relatedness satisfaction seems low in both conditions. This finding will be further discussed in the discussion section. For autonomous motivation, a significant regression equation was found F (7,211) = 37.453, p  < 0.001. The regression analysis (see Table ​ Table5) 5 ) further reveals that all three satisfaction scores (competence satisfaction, relatedness satisfaction and autonomy satisfaction) contribute positively to students’ autonomous motivation, as does students’ experienced value/usefulness. Also for students’ controlled motivation a significant regression equation was found F (7,211) = 8.236, p  < 0.001, with students’ autonomy frustration and students’ relatedness satisfaction contributing to students’ controlled motivation. The aforementioned relationships are in line with the expectations. However, we noticed that relatedness satisfaction contributed to students’ controlled motivation in the opposite direction of what was expected (the higher students’ relatedness satisfaction, the lower students’ controlled motivation). This finding will be reflected upon in the discussion section. Also for students’ amotivation, a significant regression equation was found F (7,211) = 7.913, p  < 0.001. Students’ autonomy frustration, competence frustration and students’ value/usefulness contributed to students’ amotivation in an expected way. Also for cognitive outcomes related to the research skills test, a significant regression equation was found F (3,211) = 8.351, p  < 0.001. In line with the expectations, the regression analysis revealed that the higher students’ amotivation, the lower students’ scores on the research skills test. No significant regression equation was found for the outcome variable related to the research skills task F (3,202) = 0.954, p  < 0.416. For all regression equations, the R 2 and the exact regression weights are presented in Table ​ Table6 6 .

Linear model of predictors of autonomous motivation, controlled motivation, amotivation, LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

*Significant at .050 level

As a follow-up analysis and in order to better understand the outcomes, we decided to also look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST and two-pager) by means of two additional regression analyses. The motivation behind this decision relates to possible issues regarding the motivational measures used, which might complicate the investigation of indirect relationships (see discussion). The results are provided in Table ​ Table7, 7 , and show that both for the LRST and the two-pager, respectively, a significant [ F (7,207) = 4.252, p  < 0.001] and marginally significant regression weight [ F (7,199) = 2.029, p  = 0.053] was found. More specifically, students’ relatedness satisfaction and students’ perceived value/usefulness contribute to students’ scores on the two-pager and on the research skills test. As one would expect, we see that the higher students’ value/usefulness, the higher students’ scores on both cognitive outcomes. In contrast to one would expect, we found that the higher students’ relatedness satisfaction, the lower students’ scores on the cognitive outcomes. These findings are reflected upon in the discussion section.

Linear model of predictors of LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID based) learning environment?

As was mentioned in the method section, the fourth research question was analysed by labelling students’ qualitative feedback by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. By means of this approach, we could analyse students’ need experiences in a fine grained manner. When students’ quotes were applicable to more than one code, they were labelled with different codes. In what follows, students’ quotes are indicated with the codes “BC” (baseline condition) or “NSC” (need satisfaction condition) in order to indicate which learning environment the student completed. Of all 233 students, 124 students provided qualitative feedback (44 in BC and 80 in NSC). In total, 266 quotes were labeled. Autonomy satisfaction was coded 40 times BC and 41 times in NSC; autonomy frustration was coded 13 times in BC and four times in NSC; competence satisfaction was coded 28 times in BC and 34 times in NSC; competence frustration was coded 31 times in BC and 27 times in NSC; relatedness satisfaction was coded 10 times in BC and 16 times in NSC; and relatedness frustration was coded five times in BC and 17 times in NSC. Several observations could be drawn from the qualitative data.

Related to autonomy satisfaction , in both conditions, several students explicitly mentioned the personal value and usefulness of what they had learned in the learning environment. While in the baseline condition, these references were often vague (“Now I know what people expect from me next year ”; “I think I might use this information in the future ”); some references appeared to be more specific in the need support condition (“I want to study psychology and I think I can use this information!”; “This is a good preparation for higher education and university ”; “I can use this information to write an essay ”; “I think the theory was interesting, because you are sure you will need it once. I don’t always have that feeling during a normal lesson in school”). In addition, students in both conditions mentioned that they found the material interesting, and that they appreciated the online format: “It’s different then just listening to a teacher, I kept interested because of the large variety in exercises and overall, I found it fun” (NSC).

Several comments were coded as ‘ autonomy frustration’ in both conditions. Some students indicated that they found the material “useless” (BC), or that “they did not remember that much” (BC). Others found the material “uninteresting” (BC), “heavy and boring” (NSC) or “not fun” (BC). In addition, some students “did not like to complete the assignments” (NSC), or “prefer a book to learn theory” (NSC).

Related to competence satisfaction , students in both conditions found the material “clear” (BC, NSC). In addition, students’ appreciated the example answers, the difficulty rate (“Some exercises were hard, but that is good. That’s a sign you’re learning something new” (NSC)), and the fact that the theory was segmented in several parts. In addition, students recognized that the material required complex skills: “I learned a lot, you had to think deeper or gain insights in order to solve the exercises” (NSC), “you really had to think to complete the exercises” (NSC). In the need satisfaction group, several quotes were labelled related to the specific need support provided. For example, students indicated that they appreciated the forum option: “If something was not clear, you could check your peer’s answers” (NSC). Students also valued the fact that they could work at their own pace: “I found it very good that we could solve everything at our own pace” (NSC); “good that you could choose your own pace, and if something was not clear to you, you could reread it at your own pace” (NSC). In addition, students appreciated the immediate feedback provided by the researcher “I found it very good that we received personal feedback from xxx (name researcher). That way, I knew whether I understood the theory correctly” (NSC); and the fact that they could indicate their progress “It was good that you could see how far you proceeded in the learning environment” (NSC).

In both the baseline and the need supportive condition, there were also several comments related to competence frustration . For example, students found exercises vague, unclear or too difficult. While students, overall, understood the theory provided, applying the theory to an integrative assignment appears to be very difficult: “I did understand the several parts of the learning environment, but I did not succeed in writing a research proposal myself” (NSC). “I just found it hard to respond to questions. When I had to write my two-pager research proposal, I really struggled. I really felt like I was doing it entirely wrong” (NSC)). In addition, a lot comments related to the fact that the theory was a lot to process in a short time frame, and therefore, students indicated that it was hard to remember all the theory provided. In addition, this led pressure in some students: “Sometimes, I experiences pressure. When you see that your peers are finished, you automatically start working faster.” (BC).

Concerning relatedness satisfaction , in the baseline condition, students appreciated the chat function “you could help each other and it was interesting to hear each other’s opinions about the topics we were working on” (BC). However, most students indicated that they did not make use of the chat or forum options. In the need satisfaction condition, students appreciated the forum and the chat function: “You knew you could always ask questions. This helped to process the learning material” (NSC), “My peers’ answers inspired me” (NSC), “Thanks to the chat function, I felt more connected to my peers” (NSC). In addition, students in the need satisfaction condition appreciated the fact that they could contact the researcher any time.

Several students made comments related to relatedness frustration . In both groups, students missed the ‘live teaching’: “I tried my best, but sometimes I did not like it, because you do not receive the information in ‘real time’, but through videos” (BC). In addition, students missed their peers: “We had to complete the environment individually” (BC). While some students appreciated the opportunity of a forum, other students found this possibility stressful: “I think the forum is very scary. I posted everything I had to, but I found it very scary that everyone can see what you post” (NSC). Others did not like the fact that they needed to work individually: “Sometimes I lost my attention because no one was watching my screen with me” (NSC); “I found it hard because this was new information and we could not discuss it with each other” (NSC); “I felt lonely” (NSC); “It is hard to complete exercises without the help of a teacher. In the future this will happen more often, so I guess I will have to get used to it” (NSC); “When I see the teacher physically, I feel less reluctant to ask questions” (NSC).

The current intervention study aimed at exploring the motivational and cognitive effects of providing need support in an online learning environment fostering upper secondary school students’ research skills. More specifically, we investigated the impact of autonomy, competence and relatedness support in an online learning environment on students’ scores on a research skills test, a research skills task, students’ autonomous motivation, controlled motivation, amotivation, need satisfaction, need frustration, and experienced value/usefulness. Adopting a pretest-intervention-posttest design approach, 233 upper secondary school behavioral sciences students’ motivational outcomes were compared among two conditions: (1) a 4C/ID inspired online learning environment condition (baseline condition), and (2) a condition with an identical online learning environment additively providing support for students’ autonomy, relatedness and competence need satisfaction (need supportive condition). This study aims to contribute to the literature by exploring the integration of need support for all three needs (the need for competence, relatedness and autonomy) in an ecologically valid setting. In what follows, the findings are discussed taking into account the COVID-19 affected circumstances in which the study took place.

As was hypothesized based on existing research (Costa et al., 2021 ), results showed significant learning gains on the LRST cognitive measure in both conditions, pointing out that the learning environments in general succeeded in improving students’ research skills. The current study did not find any significant differences in these learning gains between both conditions. Controlling for a priori differences between the conditions on the LRST pretest measure, students in the need support condition did exceed students in the baseline condition on the two-pager task. However, overall, the scores on the research skills task were quite low, pointing to the fact that students still seem to struggle in writing a research proposal. This task can be considered more complex (van Merriënboer & Kirschner, 2018 ) than the research skills test, as students are required to combine their conceptual and procedural knowledge in one assignment. Indeed, in the qualitative feedback, students indicate that they understand the theory and are able to apply the theory in basic exercises, but that they struggle in integrating their knowledge in a research proposal. Future research could set up more extensive interventions explicitly targeting students’ progress while writing a research proposal, for example using development portfolios (van Merriënboer et al., 2006 ).

The effect of the intervention on the motivational outcome measures was investigated. Since we experimentally manipulated need support, this study hypothesized that students in the need supportive condition would show higher scores for autonomous motivation, value/usefulness and need satisfaction; and lower scores for controlled motivation, amotivation and need frustration compared to students in the baseline condition (Deci & Ryan, 2000 ). However, the analyses showed that students in the conditions did not differ on the value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration and relatedness frustration measures. In contrast to what was hypothesized, students’ in the baseline condition reported higher relatedness satisfaction compared to students in the need supportive condition. No differences were found in students’ autonomous motivation and controlled motivation. However, as was expected, students in the need supportive conditions did report lower levels of amotivation compared to students in the baseline condition. Still, for the current study, one could question the role of the need support in this respect, as the current intervention did not succeed in manipulating students’ need experiences. In what follows, possible explanations for these findings are outlined in light of the existing literature.

Need experiences

A first observation based on the findings as described above is that the intervention did not succeed in manipulating students’ need satisfaction, need frustration and value/usefulness in an expected way. One effect was found of condition on relatedness satisfaction, but in the opposite direction of what was expected. We did not find a conclusive explanation for this unanticipated finding, but we do argue that the COVID-19 related measures at play during the intervention could have impacted this result. This will be reflected upon later in this discussion (limitations). In both conditions, students seem to be averagely satisfied regarding autonomy and competence in the 4C/ID based learning environments. This might be explained by the fact that 4C/ID based learning environments inherently foster students’ perceived competence because of the attention for structure and guidance, and the fact that the use of authentic tasks can be considered autonomy supportive (Bastiaens & Martens, 2007). However, we see that students experience low relatedness satisfaction in both conditions. The fact that the learning environment was organized entirely online might have influenced this result. While one might also partly address this low relatedness satisfaction to the COVID-19 circumstances at play during the study, this hypothetical explanation does not hold entirely since also in a previous non COVID-affected study in this research trajectory (Maddens et al., under review ), students’ relatedness satisfaction was found to be low. This finding, combined with findings from students’ qualitative feedback clearly indicating relatedness frustration, we argue that future research could focus on the question as how to provide need for relatedness support in 4C/ID based learning environments. On a more general level, this raises the question how opportunities for discussions and collaboration can be included in 4C/ID based learning environments. For example, organizing ‘real classroom interactions’ or performing assignments in groups (see also the suggestion of van Merriënboer & Kirschner, 2018 ), might be important in fostering students’ relatedness satisfaction (Salomon, 2002 ) . As argued by Wang et al. ( 2019 ), relatedness support is clearly understudied, for a long time often even ignored, in the SDT literature. Recently, relatedness is beginning to receive more attention, and has been found a strong predictor of autonomous motivation in the classroom (Wang et al., 2019 ).

Possibly, the need support provided in the learning environment was insufficient or inadequate to foster students’ need experiences. However, as the implementations were based on the existing literature (Deci & Ryan, 2000 ), this finding can be considered surprising. In addition, we derive from the qualitative feedback that students seem to value the need support provided in the learning environment. These contradictory observations are in line with previous research (Bastiaens et al., 2017 ), and call for further investigation.

Autonomous motivation, controlled motivation, amotivation

A second observation is that, in both conditions, students seem to hold low autonomous motivation and low controlled motivation towards learning research. On average, also students’ amotivation is low. The fact that students are not amotivated to learn about research can be considered reassuring. However, the fact that students experience low autonomous motivation causes concerns, as we know this might negatively impact their learning behavior and intentions to learn (Deci & Ryan, 2000 ; Wang et al., 2019 ). However, this result is based on mean scores. Future research might look at these results at student level, in order to identify individual motivational profiles (Vansteenkiste et al., 2009 ) and their prevalence in upper secondary behavioral sciences education.

A third observation is that students’ autonomous and controlled motivation were not affected by the intervention. Since the intervention did not succeed in manipulating students’ need experiences, this finding is not surprising. In addition, this is in line with Bastiaens et al.’ ( 2017 ) study, not finding motivational effects of providing need support in 4C/ID based learning environments. However, the current study did confirm that—although still higher than at pretest level, see below—students in the need supportive condition reported lower amotivation compared to students in the baseline condition. As no amotivational differences were observed at pretest level, this might indicate that students’ self-reported motivation (autonomous and controlled motivation) and/or needs do not align with students’ experienced motivation and needs. As was mentioned, this calls for further research.

Theoretical relationships

In line with previous research (Wang et al., 2019 ), multiple regression analyses revealed that students’ need satisfaction (on all three measures) contributed positively to students’ autonomous motivation. In addition, also students’ perceived value/usefulness contributed positively to students’ autonomous motivation. Students’ competence frustration and autonomy frustration contributed positively to students’ amotivation, and students’ value/usefulness contributed negatively to students’ amotivation. Students’ autonomy frustration contributed positively to students’ controlled motivation. While all the aforementioned relationships are in line with the expectations (Deci & Ryan, 2000 ; Wang et al., 2019 ), an unexpected finding is that students’ relatedness satisfaction contributed positively to students’ controlled motivation. This contradicts previous research (Wang et al., 2019 ), reporting that relatedness contributes to controlled motivation negatively. However, previous research (Wang et al., 2019 ) did find controlled motivation to be positively related to pressure . Although we did not find a conclusive explanation for this unanticipated finding, one possible reason thus is that students who contacted their peers in the online learning environment (and thus felt more related to their peers), might have experienced pressure because they felt like their peers worked faster or in a different way. Indeed, in the qualitative feedback, we noticed that some students indicated they ‘rushed’ through the online learning environment because they noticed a peer working faster. This finding calls for further research.

Overall, the results indicate that the observed need variables contributed most to students’ autonomous motivation, compared to (reversed relationships in) students’ amotivation and students’ controlled motivation. As such, when targeting students’ motivation, fostering students’ autonomous motivation based on students’ need experiences seems most promising. This is in line with previous research (Wang et al., 2019 ) reporting high correlations between students’ needs and students’ autonomous motivation, compared to students’ controlled motivation. We also investigated the relationships between students’ motivation and students’ cognitive outcomes. In line with a previously conducted study in this research trajectory (Maddens et al., under review ), but in contrast to what was hypothesized based on the existing literature (Deci & Ryan, 2000 ; Grolnick et al., 1991 ; Reeve, 2006 ) we found that nor students’ autonomous motivation, nor students’ controlled motivation contributed to students’ scores on the research skills test. However, we did find that students’ amotivation contributed negatively to students’ LRST scores. As such, when targeting students’ cognitive outcomes in educational programs, one might pay explicit attention to preventing amotivation. This is in line with previous research conducted in other domains, reporting that amotivation plays an important role in predicting mathematics achievement (Leroy & Bressoux, 2016 ), while this relationship was not found in other motivation types. Related to research skills, the current research suggests that preventing competence frustration and autonomy frustration, and fostering students’ experiences of value/usefulness might be especially promising to reach this goal.

Initially, we did not plan any analyses investigating the direct relationships between students’ needs and students’ cognitive outcomes, partly because previous research (Vallerand & Losier, 1999 ) suggests that the relationships between need satisfaction and (cognitive) outcomes are mediated by the types of motivation. To this end, we investigated the relationships between students’ needs and students’ motivation, separately from the relationships between students’ motivation and students’ cognitive outcomes. However, because of potential issues with the motivational measures (see earlier), which possibly hampers the interpretation of the relationships between students’ needs, students’ motivation, and students’ cognitive outcomes, we decided to also directly assess the regression weights of students’ needs and students’ perceived value/usefulness, on students’ cognitive outcomes. Results revealed that, in line with the expectations, students’ perceived value/usefulness contributed positively to students’ LRST scores and two-pager scores, which potentially stresses the importance of value/usefulness, not only for motivational purposes, but also for cognitive purposes. This is in line with previous research (Assor et al., 2002 ), establishing relationships between fostering relevance and students’ behavioral and cognitive engagement (which potentially leads to better cognitive outcomes). In contrast to the expectations, students’ relatedness satisfaction was found to be negatively related to students’ scores on the LRST and the two-pager. However, again, this surprising finding is best interpreted in light of the COVID-10 pandemic (see earlier).

Limitations

This study faced some reliability issues given the time frame in which the study took place. Due to the COVID-19-restrictions at play at the time of study, the study plan needed to be revised several times in collaboration with teachers in order to be able to complete the interventions. In addition, it is very likely that students’ motivation (and relatedness satisfaction) was influenced by the COVID 19-restrictions. For example, due to the restrictions, in the last phase of the intervention, students could only be present at school halftime, and therefore, some students worked from home while others worked in the classroom. In the qualitative feedback, students reported several COVID-19 related frustrations (it was too cold in class because teachers were obligated to open the windows; students needed to frequently disinfect their computers…). Also the teachers mentioned that students suffered from low well-being during the COVID-19 time frame (see further), and as such, this affected their motivation. Although all efforts were undertaken in order for the study to take place as controlled as possible, results should be interpreted in light of this time frame. The impact of the COVID-19 pandemic on students’ self-reported motivation has been established in recent research (Daniels et al., 2021 ). Overall, one could question to what extent we can expect an intervention at microlevel (manipulating need support in learning environments) to work, when the study takes place in a time frame where students’ need experiences are seriously threatened by the circumstances.

Decreasing motivation

Students’ motivation evolved in a non-desirable way in both conditions. This unexpected finding (decreasing motivation) might be explained by four possible reasons: a first explanation is that asking students to fill out the same questionnaire at posttest and pretest level might lead to frustration and lower reported motivation (Kosovich et al., 2017 ). Indeed, students spent a lot of time working in the online learning environment, so filling out another motivational questionnaire on top of the intervention might have added to the frustration (Kosovich et al., 2017 ). A second explanation is that students’ motivation naturally declines over time (which is a common finding in the motivational literature, Kosovich et al., 2017 ). A third explanation is that students, indeed, felt less motivated towards research skills after having completed the online learning environment. For example, the qualitative data indicated that a lot of students acknowledged the fact that the learning environment was useful, but that personally, they were not interested in learning the material. In addition, students indicated that the learning material was a lot to process in a short time frame, and was new to them, which might have negatively impacted their motivation. The latter (students indicating that the learning material was extensive) might indicate that students experienced high cognitive load (Paas & van Merriënboer, 1994; Sweller et al., 1994 ) while completing the learning environment. A fourth explanation is that, due to the COVID19-restrictions, students lost motivation during the learning process. A post-intervention survey in which we asked teachers about the impact of the COVID-19 restrictions on students’ motivation indicated that some students experienced low well-being during the COVID-19 pandemic, and thus, this might have hampered their motivation to learn. In addition, a teacher mentioned that COVID-19 in general was very demotivating for the students, and that students had troubles concentrating due to the fact they felt isolated. As was mentioned, the impact of COVID-19 on students’ motivation has been well described in the literature (Daniels et al., 2021 ). Although, in the current study, we cannot prove the impact of these measures on students’ motivation specifically towards learning research skills, it is important to take this context into account when interpreting the results.

Students’ learning behavior

Based on students’ qualitative feedback, we have reasons to believe that students did not always work in the learning environment as we would want them to do. Thus, students did not interact with the need support in the intended way (‘instructional disobedient behavior’: Elen, 2020 ). For example, several students reported that they did not always read all the material, did not make use of the forum, or did not notice certain messages from the researcher. However, the current research did not specifically look into students’ learning behavior in the learning environment. In learning environments organized online, future researchers might want to investigate students’ online behavior in order to gain insights in students’ interactions with the learning environment.

This study aims to contribute to theory and practice. Firstly, this study defines the 4C/ID model (van Merriënboer & Kirschner, 2018 ) as a good theoretical framework in order to design learning environments aiming to foster students’ research skills. However, this study also points to students’ struggling in writing a research proposal, which might lead to more specific intervention studies especially focussing on monitoring students’ progress while performing such tasks. Secondly, this study clearly elaborates on the operationalizations of need support used, and as such, might inform instructional designers in order to implement need support in an integrated manner (including competence, relatedness and autonomy support). Future interventions might want to track and monitor students’ learning behavior in order for students to interact with the learning environment as expected (Elen, 2020 ). Thirdly, this study established theoretical relationships between students’ needs, motivation and cognitive outcomes, which might be useful information for researchers aiming to investigate students’ motivation towards learning research skills in the future. Based on the findings, future researchers might especially involve in research fostering students’ autonomous motivation by means of providing need support; and avoiding students’ amotivation in order to enhance students’ cognitive outcomes. Suggestions are made based on the need support and frustration measures relating to these motivational and cognitive outcomes. For example, fostering students’ value/usefulness seems promising for both cognitive and motivational outcomes. Fourthly, although we did not succeed in manipulating students’ need experiences, we did gain insights in students’ experiences with the need support by means of the qualitative data. For example, the irreplaceable role of teachers in motivating students has been exposed. This study can be considered innovative because of its aim to inspect both students’ cognitive and motivational outcomes after completing a 4C/ID based educational program (van Merriënboer & Kirschner, 2018 ). In addition, this study implements integrated need support rather than focusing on a single need (Deci & Ryan, 2000 ; Sheldon & Filak, 2008 ).

Acknowledgements

This study was carried out within imec’s Smart Education research programme, with support from the Flemish government.

Appendix: Overview test instruments

• Instructions 2-pager (Maddens, Depaepe, Raes, & Elen, under review)

Write a research proposal for a fictional study.

In a Word-document of maximum two pages…

• You describe a research question and the importance of this research question
• You explain how you would answer this research question (manner of data collection and target group)
• You explain what your expectations are, and how you will report your results.

To do so, you receive 2 hours.

Post your research proposal here.

Good luck and thank you for your activity in the RISSC-environment!

Declarations

The authors declare that they have no conflict of interest.

All ethical and GDPR-related guidelines were followed as required for conducting human research and were approved by SMEC (Social and Societal Ethics Committee).

1 Fischer et al. ( 2014 ) refer to these research skills as scientific reasoning skills.

2 In Flanders, during the time of study, four different types of education are offered from the second stage of secondary education onwards (EACEA, 2018) (general secondary education, technical secondary education, secondary education in the arts and vocational secondary education). Behavioral sciences is a track in general secondary education.

3 For a complete overview on the design and the evaluation of this learning environment, see Maddens et al ( 2020b ).

4 During the time of study, the COVID-19 restrictions became more strict: students in upper secondary education could only come to school half of the time. Therefore, some students completed the last modules of the learning environment at home.

5 The BPNSNF-training scale is initially constructed to evaluate motivation related to workshops. The phrasing was adjusted slightly in order for the suitability for the current study. For example, we changed the wording ‘during the past workshop…’ to ‘while completing the online learning environment…’.

6 In the current study, we would label the items categorized as ‘intrinsic motivation’ in ASRS (finding something interesting, fun, fascinating or a pleasant activity) as ‘integration’. In SDT (Deci & Ryan, 2000 ; Deci et al., 2017 ), integration is described as being “fully volitional”, or “wholeheartedly engaged”, and it is argued that fully internalized extrinsic motivation does not typically become intrinsic motivation, but rather remains extrinsic even though fully volitional (because it is still instrumental). In the context of the current study, in which students learn about research skills because this is instructed (thus, out of instrumental motivations), we think that the term integration is more applicable than pure intrinsic motivation in self-initiated contexts (which can be observed for example in children’s play or in sports).

7 Levene’s test for homogeneity of variances was significant for the outcome “two-pager”. However, we continued with the analyses since the treatment group sizes are roughly equal, and thus, the assumption of homogeneity of variances does not need to be considered (Field, 2013 ). Levene’s test for homogeneity of variances was non-significant for all the other outcome measures.

8 Cohen’s D is calculated in SPSS by means of the formula: D = M 1 - M 2 Sp

Condition x autonomous motivation pretest Value/usefulness: p  = 0.251; autonomous motivation: p  = 0.269; controlled motivation: p  = 0.457; amotivation: p  = 0.219; autonomy satisfaction: p  = 0.794; autonomy frustration: p  = 0.096; competence satisfaction: p  = 0.682; competence frustration: p  = 0.699; relatedness satisfaction: p  = 0.943; relatedness frustration: p  = 0.870.

Condition x controlled motivation pretest Value/usefulness: p  = 0.882; autonomous motivation: p  = 0.270; controlled motivation: p  = 0.782; amotivation: p  = 0.940; autonomy satisfaction: p  = 0.815; autonomy frustration: p  = 0.737; competence satisfaction: p  = 0.649; competence frustration: p  = 0.505; relatedness satisfaction: p  = 0.625; relatedness frustration: p  = 0.741.

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Empowering students to develop research skills

February 8, 2021

This post is republished from   Into Practice ,  a biweekly communication of Harvard’s  Office of the Vice Provost for Advances in Learning

Terence D. Capellini, Richard B Wolf Associate Professor of Human Evolutionary Biology, empowers students to grow as researchers in his Building the Human Body course through a comprehensive, course-long collaborative project that works to understand the changes in the genome that make the human skeleton unique. For instance, of the many types of projects, some focus on the genetic basis of why human beings walk on two legs. This integrative “Evo-Devo” project demands high levels of understanding of biology and genetics that students gain in the first half of class, which is then applied hands-on in the second half of class. Students work in teams of 2-3 to collect their own morphology data by measuring skeletons at the Harvard Museum of Natural History and leverage statistics to understand patterns in their data. They then collect and analyze DNA sequences from humans and other animals to identify the DNA changes that may encode morphology. Throughout this course, students go from sometimes having “limited experience in genetics and/or morphology” to conducting their own independent research. This project culminates in a team presentation and a final research paper.

The benefits: Students develop the methodological skills required to collect and analyze morphological data. Using the UCSC Genome browser  and other tools, students sharpen their analytical skills to visualize genomics data and pinpoint meaningful genetic changes. Conducting this work in teams means students develop collaborative skills that model academic biology labs outside class, and some student projects have contributed to published papers in the field. “Every year, I have one student, if not two, join my lab to work on projects developed from class to try to get them published.”

“The beauty of this class is that the students are asking a question that’s never been asked before and they’re actually collecting data to get at an answer.”

The challenges:  Capellini observes that the most common challenge faced by students in the course is when “they have a really terrific question they want to explore, but the necessary background information is simply lacking. It is simply amazing how little we do know about human development, despite its hundreds of years of study.” Sometimes, for instance, students want to learn about the evolution, development, and genetics of a certain body part, but it is still somewhat a mystery to the field. In these cases, the teaching team (including co-instructor Dr. Neil Roach) tries to find datasets that are maximally relevant to the questions the students want to explore. Capellini also notes that the work in his class is demanding and hard, just by the nature of the work, but students “always step up and perform” and the teaching team does their best to “make it fun” and ensure they nurture students’ curiosities and questions.

Takeaways and best practices

• Incorporate previous students’ work into the course. Capellini intentionally discusses findings from previous student groups in lectures. “They’re developing real findings and we share that when we explain the project for the next groups.” Capellini also invites students to share their own progress and findings as part of class discussion, which helps them participate as independent researchers and receive feedback from their peers.
• Assign groups intentionally.  Maintaining flexibility allows the teaching team to be more responsive to students’ various needs and interests. Capellini will often place graduate students by themselves to enhance their workload and give them training directly relevant to their future thesis work. Undergraduates are able to self-select into groups or can be assigned based on shared interests. “If two people are enthusiastic about examining the knee, for instance, we’ll match them together.”
• Consider using multiple types of assessments.  Capellini notes that exams and quizzes are administered in the first half of the course and scaffolded so that students can practice the skills they need to successfully apply course material in the final project. “Lots of the initial examples are hypothetical,” he explains, even grounded in fiction and pop culture references, “but [students] have to eventually apply the skills they learned in addressing the hypothetical example to their own real example and the data they generate” for the Evo-Devo project. This is coupled with a paper and a presentation treated like a conference talk.

Bottom line:  Capellini’s top advice for professors looking to help their own students grow as researchers is to ensure research projects are designed with intentionality and fully integrated into the syllabus. “You can’t simply tack it on at the end,” he underscores. “If you want this research project to be a substantive learning opportunity, it has to happen from Day 1.” That includes carving out time in class for students to work on it and make the connections they need to conduct research. “Listen to your students and learn about them personally” so you can tap into what they’re excited about. Have some fun in the course, and they’ll be motivated to do the work.

Home > Blog > Tips for Online Students > The Best Research Skills For Success

Tips for Online Students , Tips for Students

The Best Research Skills For Success

Updated: June 19, 2024

Published: January 5, 2020

Every student is required to conduct research in their academic careers at one point or another. A good research paper not only requires a great deal of time, but it also requires complex skills. Research skills include the ability to organize, evaluate, locate, and extract relevant information.

Let’s learn how to develop great research skills for academic success.

What is Research?

We’ve all surely heard the term “research” endlessly. But do you really know what it means?

Research is a type of study that focuses on a specific problem and aims to solve it using scientific methods. Research is a highly systematic process that involves both describing, explaining, and predicting something.

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What are research skills.

Research skills are what helps us answer our most burning questions, and they are what assist us in our solving process from A to Z, including searching, finding, collecting, breaking down, and evaluating the relevant information to the phenomenon at hand.

Research is the basis of everything we know — and without it, we’re not sure where we would be today! For starters, without the internet and without cars, that’s for sure.

Why are Research Skills Important?

Research skills come in handy in pretty much everything we do, and especially so when it comes to the workforce. Employers will want to hire you and compensate you better if you demonstrate a knowledge of research skills that can benefit their company.

From knowing how to write reports, how to notice competition, develop new products, identify customer needs, constantly learn new technologies, and improve the company’s productivity, there’s no doubt that research skills are of utter importance. Research also can save a company a great deal of money by first assessing whether making an investment is really worthwhile for them.

How to Get Research Skills

Now that you’re fully convinced about the importance of research skills, you’re surely going to want to know how to get them. And you’ll be delighted to hear that it’s really not so complicated! There are plenty of simple methods out there to gain research skills such as the internet as the most obvious tool.

Gaining new research skills however is not limited to just the internet. There are tons of books, such as Lab Girl by Hope Jahren, journals, articles, studies, interviews and much, much more out there that can teach you how to best conduct your research.

Utilizing Research Skills

Now that you’ve got all the tools you need to get started, let’s utilize these research skills to the fullest. These skills can be used in more ways than you know. Your research skills can be shown off either in interviews that you’re conducting or even in front of the company you’re hoping to get hired at .

It’s also useful to add your list of research skills to your resume, especially if it’s a research-based job that requires skills such as collecting data or writing research-based reports. Many jobs require critical thinking as well as planning ahead.

Career Paths that Require Research Skills

If you’re wondering which jobs actually require these research skills, they are actually needed in a variety of industries. Some examples of the types of work that require a great deal of research skills include any position related to marketing, science , history, report writing, and even the food industry.

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How students can improve research skills.

Perhaps you know what you have to do, but sometimes, knowing how to do it can be more of a challenge. So how can you as a student improve your research skills ?

1. Define your research according to the assignment

By defining your research and understanding how it relates to the specific field of study, it can give more context to the situation.

2. Break down the assignment

The most difficult part of the research process is actually just getting started. By breaking down your research into realistic and achievable parts, it can help you achieve your goals and stay systematic.

3. Evaluate your sources

While there are endless sources out there, it’s important to always evaluate your sources and make sure that they are reliable, based on a variety of factors such as their accuracy and if they are biased, especially if used for research purposes.

4. Avoid plagiarism

Plagiarism is a major issue when it comes to research, and is often misunderstood by students. IAs a student, it’s important that you understand what plagiarism really means, and if you are unclear, be sure to ask your teachers.

5. Consult and collaborate with a librarian

A librarian is always a good person to have around, especially when it comes to research. Most students don’t seek help from their school librarian, however, this person tends to be someone with a vast amount of knowledge when it comes to research skills and where to look for reliable sources.

6. Use library databases

There are tons of online library resources that don’t require approaching anyone. These databases are generally loaded with useful information that has something for every student’s specific needs.

7. Practice effective reading

It’s highly beneficial to practice effective reading, and there are no shortage of ways to do it. One effective way to improve your research skills it to ask yourself questions using a variety of perspectives, putting yourself in the mind of someone else and trying to see things from their point of view.

There are many critical reading strategies that can be useful, such as making summaries from annotations, and highlighting important passages.

Thesis definition

A thesis is a specific theory or statement that is to be either proved or maintained. Generally, the intentions of a thesis are stated, and then throughout, the conclusions are proven to the reader through research. A thesis is crucial for research because it is the basis of what we are trying to prove, and what guides us through our writing.

What Skills Do You Need To Be A Researcher?

One of the most important skills needed for research is independence, meaning that you are capable of managing your own work and time without someone looking over you.

Critical thinking, problem solving, taking initiative, and overall knowing how to work professionally in front of your peers are all crucial for effectively conducting research .

1. Fact check your sources

Knowing how to evaluate information in your sources and determine whether or not it’s accurate, valid or appropriate for the specific purpose is a first on the list of research skills.

2. Ask the right questions

Having the ability to ask the right questions will get you better search results and more specific answers to narrow down your research and make it more concise.

3. Dig deeper: Analyzing

Don’t just go for the first source you find that seems reliable. Always dig further to broaden your knowledge and make sure your research is as thorough as possible.

4. Give credit

Respect the rights of others and avoid plagiarizing by always properly citing your research sources.

5. Utilize tools

There are endless tools out there, such as useful websites, books, online videos, and even on-campus professionals such as librarians that can help. Use all the many social media networks out there to both gain and share more information for your research.

6. Summarizing

Summarizing plays a huge role in research, and once the data is collected, relevant information needs to be arranged accordingly. Otherwise it can be incredibly overwhelming.

7. Categorizing

Not only does information need to be summarized, but also arranged into categories that can help us organize our thoughts and break down our materials and sources of information.

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What are different types of research, 1. qualitative.

This type of research is exploratory research and its aim is to obtain a better understanding of reasons for things. Qualitative research helps form an idea without any specific fixed pattern. Some examples include face-to-face interviews or group discussions.

2. Quantitative

Quantitative research is based on numbers and statistics. This type of research uses data to prove facts, and is generally taken from a large group of people.

3. Analytical

Analytical research has to always be done from a neutral point of view, and the researcher is intended to break down all perspectives. This type of research involves collecting information from a wide variety of sources.

4. Persuasive

Persuasive research describes an issue from two different perspectives, going through both the pros and cons of both, and then aims to prove their preference towards one side by exploring a variety of logical facts.

5. Cause & Effect

In this type of research, the cause and effects are first presented, and then a conclusion is made. Cause and effect research is for those who are new in the field of research and is mostly conducted by high school or college students.

6. Experimental Research

Experimental research involves very specific steps that must be followed, starting by conducting an experiment. It is then followed by sharing an experience and providing data about it. This research is concluded with data in a highly detailed manner.

7. Survey Research

Survey research includes conducting a survey by asking participants specific questions, and then analyzing those findings. From that, researchers can then draw a conclusion.

8. Problem-Solution Research

Both students and scholars alike carry out this type of research, and it involves solving problems by analyzing the situation and finding the perfect solution to it.

What it Takes to Become a Researcher

• Critical thinking

Research is most valuable when something new is put on the table. Critical thinking is needed to bring something unique to our knowledge and conduct research successfully.

• Analytical thinking

Analytical thinking is one of the most important research skills and requires a great deal of practice. Such a skill can assist researchers in taking apart and understanding a large amount of important information in a short amount of time.

• Explanation skills

When it comes to research skills, it’s not just about finding information, but also about how you explain it. It’s more than just writing it out, but rather, knowing how to clearly and concisely explain your new ideas.

• Patience is key

Just like with anything in life, patience will always take you far. It might be difficult to come by, but by not rushing things and investing the time needed to conduct research properly, your work is bound for success.

• Time management

Time is the most important asset that we have, and it can never be returned back to us. By learning time management skills , we can utilize our time in the best way possible and make sure to always be productive in our research.

What You Need to Sharpen Your Research Skills

Research is one of the most important tasks that students are given in college, and in many cases, it’s almost half of the academic grade that one is given.

As we’ve seen, there are plenty of things that you’ll need to sharpen your research skills — which mainly include knowing how to choose reliable and relevant sources, and knowing how to take them and make it your own. It’s important to always ask the right questions and dig deeper to make sure that you understood the full picture.

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Teaching Resources & Guides > How to Teach Science Tips > How to Develop Science Skills in Students  

How to Develop Science Skills in Students

Science study involves the development of important skills, including life skills .

The list below applies to learning any branch of science (chemistry, biology, physics), at any grade level. By focusing on developing these science process skills, you will help your student(s) not merely memorize the scientific method , but practice it, too.

It includes basic and integrated science skills.

Some of these skills, like observation, while instinctive, still need to be taught. Why? Distractions! They’re everywhere and left unchecked, interfere with learning.

These skills are fully transferrable to other subjects and life encounters.

Science skills for learning

Observing – This is the most fundamental of science skills. That’s because most students are born with five senses , which inform how they experience the world.

Observation requires students to note the “big picture” and the fine details.

Encourage your students to describe what they see in detail; this will help them identify properties and make more knowledgeable hypotheses. When studying botany, for example, have them do more than just note the color and shape of the flower. Have them count the petals, draw pictures of the leaves, and look at the pollen under a magnifying glass.

Cl assifying – This skill builds upon observation. Students can learn to separate and sort objects based on properties. Younger students can learn to sort using a single factor (e.g., number of legs: spiders have eight and insects have six), while older students can classify using several factors at once.

Teaching classification is also a great time to introduce new vocabulary words. You can encourage students to practice using these words by writing them in a science notebook, or, for younger students, by memorizing a song or poem using the new words. This is an excellent way to cross

Quantifying – One of the most valuable skills needed for science study is the ability to measure accurately.

You can start by teaching young students how to use a ruler and a measuring cup. As they grow older, they will acquire more complex measuring skills using mathematical equations and advanced equipment.

Predicting – This skill derives from your students being able to spot patterns in past experiments or existing evidence (i.e., from the natural world).

Predicting is an educated guess about what’s likely to happen when you introduce changes.

Before performing any experiment, ask your children what they think will happen and have them write down their guesses. Explain that this is called making a hypothesis . Guide younger students by asking questions such as: How many are in the jar? How much does this weigh? What will happen if we add something else? Advanced students will be capable of more in-depth predictions or hypotheses, based on what they know already.

Controlling variables – Many different factors can affect the outcome of an experiment. You can help students understand this by discussing potential factors before starting. This provides context.

After doing an experiment, encourage them to change one variable factor and try again.

Interpreting – This skill is closely related to inferring, which means coming to a conclusion after analyzing information. Interpreting, is inferring, from a point of view. Two students may interpret an experiment’s results differently.

Students should try to understand results, based on the records they keep. Their interpretation should align with the trend or big picture of the experiment.

If students are not sure why an experiment turned out the way it did, you can direct them to do more research.

Communicating – This skill touches every other one. Students must be able to transmit information through words, charts, diagrams, and other mediums.

You should emphasize to students the importance of using correct language when communicating with an audience (teachers/parents, family, friends/classmates).

Discuss with them, also, the importance of using accurate supporting mediums (charts, diagrams, etc.). As the saying goes, a picture is worth a thousand words. Audience members will often look at the pictures from a project without reading the words. That can lead them to one or more incorrect takeaways.

Forming conclusions – This skill is connected to interpreting. Students cannot make conclusions hastily; they must be reached through careful reasoning.

When forming conclusions, have your students look back at their predictions and compare them with the actual results. Make sure they take all the information they gathered into account as they draw a conclusion.

Science skills involved in the scientific method

Many of these skills can be taught by using the scientific method .  The four steps of the scientific method are to make observations, make a hypothesis, test your hypothesis, and make a conclusion. Each step of the scientific method may include many science skills, such as interpreting data while forming a conclusion, or controlling variables while testing a hypothesis.

These skills are best taught through hands-on science : activities, experiments, and projects. The skills at the top of the list are the easiest to master and can be introduced to young children through nature studies. Teach the more challenging skills by using successively more difficult experiments over time.

While not all skills may be taught at once, a good science lesson will incorporate several of these skills. Remember, as a teacher, you should always move from material that is concrete or familiar, to material that is more complex or abstract. Start with observing and move towards predicting a result, interpreting what happened, or forming a conclusion. These skills can be reinforced on a regular basis, making scientists out of any learner.

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Graduate student’s love of science leads to prestigious national fellowship

Kaylee Petraccione receives a National Institute of Health award to study a virus with pandemic potential.

• Felicia Spencer

17 Sep 2024

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Kaylee Petraccione lost her hearing after a vaccination in 2021, but maintains a passion for pursuing research that may one day help rationally design a new vaccine. And it’s led to her winning a national-level grant.

“I got a vaccine booster before the semester started, and within a very short period of time I developed a full body rash and really bad tinnitus,” said Petraccione, a doctoral student studying biomedical and veterinary sciences. “I went to bed and woke up at 5 the next morning screaming because I couldn’t hear anything. When you wake up deaf, it’s really scary and terrifying.”

Today, Petraccione is focused on understanding the molecular mechanisms enabling the Rift Valley fever virus to cause disease. In July, this work earned her the Ruth L. Kirschstein National Research Service Award Individual Predoctoral Fellowship to Promote Diversity in Health-Related Research award from the National Institute of Health (NIH).

“NIH F31 fellowships are extremely competitive and prestigious awards,” said Kylene Kehn-Hall, professor of biomedical sciences and pathobiology and Petraccione’s mentor. “Kaylee being awarded this fellowship speaks to her excellence as a candidate and the quality of her research. This is an enormous accomplishment, and I am extremely proud of her.”  

According to the NIH, the goal of the award is to enhance diversity of scientists for research careers in the biomedical, behavioral, and clinical sciences, including those with disabilities and those from disadvantaged backgrounds. For Petraccione, the grant will support her ultimate goal of closing the knowledge gap regarding the viral pathogenesis of the disease to enable some form of therapeutic intervention or vaccine.

Petraccione said there is currently no Food and Drug Administration-approved treatment or vaccine for the Rift Valley fever virus, which is endemic to sub-Saharan Africa and currently spreading to the Arabian Peninsula. The virus is transmitted through mosquito bites and can spread through aerosol particles or contact with bodily tissues or fluids from an infected person or animal.

Since the virus was identified in the Rift Valley of Kenya in 1931, herds of livestock have been infected, causing health and economic hardships. In livestock, severe infections result in nearly 100 percent death rates in the young, almost 100 percent rates of abortions in pregnant females, and a 20 to 30 percent death rate in adults. In humans, severe cases can cause hemorrhagic fever and encephalitis in less than 2 percent of those infected, yet most people recover within a week of infection.

“The mosquitoes in the U.S. and Europe are competent to carry this virus, so it’s very easy for this virus to spread here, and it’s a high priority pathogen concern of the U.S. government and the World Health Organization,” said Petraccione, who is an affiliate member of the Infectious Disease Interdisciplinary Graduate Education Program . “All it would take is for an infected animal to come here on a boat, and then all of a sudden the mosquitoes start carrying the virus and it will wipe out our herds too. The virus is so dangerous I have to wear a powered air purifying respirator to protect myself.”

Petraccione’s desire to help others has been a driving force ever since she began her post secondary education at Coastal Carolina University. Originally from Schenectady, New York, Petraccione discovered her love for molecular biology and research with a professor who was a Virginia Tech graduate. 

“Neither of my parents have a four-year degree,” Petraccione, who began her Ph.D. in Virginia Tech’s Molecular and Cellular Biology Graduate Program in August 2021. “I push myself to be the best that I can be because I want to better myself and my future, and my family as well.” 

Because of her own challenges, Petraccione is passionate about incorporating outreach into her career, and she already has a head start. 

“I went into a local low-income school to teach preschoolers and kindergartners about viruses,” Petraccione said. “I made a little 3D model of the virus I work with and had them pass it around to learn about transmission and how fast virus systems spread and how to prevent giving each other viruses.”

Petraccione plans to stay in academia and pursue a postdoctoral position where she can continue to help others.

Lindsey Haugh

• Biomedical and Veterinary Sciences
• Center for Emerging, Zoonotic, and Arthropod-borne Pathogens
• College of Science
• Disease Prevention
• Fralin Life Sciences Institute
• Graduate Education
• Graduate Research
• Infection and Immunology
• Reduce Inequalities
• Virginia-Maryland College of Veterinary Medicine

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NIMH Research and Science Track at 2024 APA Annual Meeting

75th Anniversary

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As part of ongoing 75th Anniversary celebrations, the National Institute of Mental Health (NIMH) is hosting the Research and Science track at the 2024 American Psychiatric Association (APA) Annual Meeting   . This year’s meeting theme focuses on confronting addiction and charting the course from prevention to recovery. NIMH's research track highlights some of the major initiatives NIMH has undertaken or been involved with over the years. 

If you plan to attend the meeting, please join us at the following sessions presented by NIMH leadership and staff.

NIMH Director’s presentation

75 Years of the National Institute of Mental Health: Current and Former Directors Perspective  

Wednesday, May 8, from 10:30 a.m. – 12:00 p.m. ET Location: Rooms 3D04, 3D09, Javits Center Chair: Megan Kinnane Presenters: Joshua A. Gordon, M.D., Ph.D.; Steven Hyman, M.D.; Thomas R. Insel, M.D. As part of the 75 th anniversary celebration of the National Institute of Mental Health (NIMH), this session will consist of a panel discussion between three former NIMH directors and the current NIMH director. Each director will describe what they sought to achieve during their directorships, how NIMH contributed to psychiatry during their tenures, and their vision for the future of mental health research as we enter the next 25 years. Audience members are encouraged to come with questions.

Additional NIMH presentations

Expanding 988 Suicide and Crisis Lifeline and Crisis Services and Research: What Psychiatrists Need to Know  

Sunday, May 5, from 3:45 – 5:15 p.m. ET Location: Room 1E10, Javits Center Chair: Stephen O’Connor, Ph.D. Presenters: Anita Everett, M.D.; Matthew Goldman, M.D., M.S.; Jonathan Purtle, Dr.Ph., M.P.H, M.Sc.; Victor Armstrong, M.S.W.

ACLP Presidential Symposium: Hot Topics in Consultation-Liaison Psychiatry Across the Lifespan  

Monday, May 6, from 8:00 – 9:30 a.m. ET Location: Room 1A21, Javits Center Chair: Maryland Pao, M.D. Presenters: Haniya Raza, D.O., M.P.H., Mark A. Oldham, M.D., Durga Roy, M.D.

Cutting Edge Mental Health Disparities Research: Current State and Future Directions  

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• Monday, May 6, 9:15 – 10:30 a.m. ET and 11:45 a.m. – 4:00 p.m. ET
• Tuesday, May 7, 9:15 – 10:30 a.m. ET and 11:45 a.m. – 1:30 p.m. ET

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Visit the American Psychiatric Association website   to register.

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Rats and people have long coexisted. Now research may find out a lot more about them Gary Hershorn/Getty Images hide caption

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Master of Science in Finance

Prepare for your career in finance in the heart of Chicago.

The Master of Science in Finance Program at UIC Business Liautaud Graduate School is a STEM designated program*, just steps from the Chicago Loop – the city’s financial district. As an MSF student, you will gain a solid foundation in the principles of modern finance and specialize your coursework in a sought after field. You will also have the opportunity to perform coursework in our Market Training Lab, a virtual trading center featuring state-of-the art industry software including Bloomberg terminals *(CIP Code 27.0305).

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Top 40 MSF in U.S. — QS World University Rankings

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For fall 2024 and spring 2025, domestic applicants and international applicants to the MS in Finance program are waived of the GMAT/GRE requirement. If you plan on submitting GMAT or GRE scores, please upload your score report in your application portal. If you plan on taking the GMAT or GRE at a later date, please indicate the test date and send your official scores to UIC.

Please note that competitive GMAT/GRE scores may help your chances of admission and being awarded merit aid.

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The MSF Program provides a strong foundation in the principles and practice of modern finance. The program introduces the latest concepts and issues affecting today’s financial professionals.

STEM-designation:  The program is STEM (Science, Technology, Engineering, and Mathematics) designated for its focus on advanced quantitative skills.

Fast-paced and flexible: Thirty-two credit hour degree program that can be completed in one year of full-time study or more than one year of part-time study. Students may be assigned additional prerequisite coursework depending upon their academic background. *

Areas of Specialization: The program offers tracks including:

• Corporate Finance
• Asset Management
• Commodities, Derivatives and  Financial Exchanges
• Banking & Capital Markets

Market Training Lab:  Virtual trading center featuring industry software products, and academic research data.

Financial Certification Preparation:  Preparation for professional exams.

More Information

• * For a full list of requirements, including prerequisites, visit the UIC Catalog .
• For a full list of offered courses, visit the Course Catalog .
• For information about tuition and fees, visit the Tuition Page .

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The MSF program is meticulously designed to be the perfect blend of concepts in finance and their applicability to the real world. The benefits of this program include the range of electives that allow you to tailor the program according to the career path you aspire. Faculty members have worked in the finance industry for years, and I have developed my professional skills by learning from them. Overall, my experience at UIC has been memorable and I have built valuable and long-lasting connections. Shivani Brahme, Analyst, Goldman Sachs  |  MSF ’21

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94% of recent graduates (Class of 2022) had been employed within six months of graduation earning an average starting salary of $82,000.

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• Master of Science in Finance and Master of Business Administration
• Master of Science in Finance and Master of Science in Business Analytics
• Master of Science in Finance and Master of Science in Management Information Systems

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Warren arnold, özgür arslan-ayaydin, ehsan azarmsa, terry badger, dominique badoer, gib bassett, andy bateman, andriy bodnaruk, hsiu-lang chen, featured courses heading link copy link, fin 594 financial communication.

Effective communication is a critical skill for a well-rounded financial professional. This course will provide a foundation in written, verbal and visual communication. Instruction will be from a practitioner’s perspective with a focus on honing skills that students will use in their everyday work life.

Those skills go beyond simply being able to write a report about current macroeconomic conditions or put together a slide deck for a roadshow. Learning to write means learning to organize, to analyze, to summarize and to persuade. It means resisting the urge to just dump data or to fall into opaque jargon. The elements behind effective writing directly translate to effective speaking, not to mention self-confidence and credibility in the workplace.

As with other aspects of the industry, financial communication is subject to significant regulatory oversight, particularly when the communication is directed toward the public. Instruction will reflect this reality, with students learning how to create content that meets required compliance standards

For more information, contact Terry Badger.

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Standard finance theory assumes that market participants are rational, an assumption that leaves basic facts of the stock market unexplained. By assuming that some investors aren’t fully rational, behavioral finance better explains some financial phenomena. This course will focus on two areas: limits to arbitrage and how rational traders react to irrational traders, and investor psychology and the deviations from full rationality we can expect to find in financial markets.

For more information, contact Andriy Bodnaruk .

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Learn how securities are traded in modern electronic financial markets. You’ll uncover the design, operation, and regulation of trading processes with a special emphasis placed on trading simulations related to agency trading, liability trading and market making. This course is suited to people who plan to work or are interested in securities exchanges and high-frequency trading.

The MSF program has been the most influential to my academic and professional development. As a former international student, UIC MSF faculty and advisors were the most helpful in my transition to the American academic and business culture. One of the most valuable aspects of the UIC MSF program is how multi-faceted the curriculum is. During the program, I was able to focus both on financial strategy and business processes as well as the analysis-heavy topics such as Financial Analysis and Portfolio Management. The holistic approach to teaching with hands-on experience in the form of multiple projects is truly unique. I graduated from UIC not only with a degree in Finance but with plenty of meaningful, life-long connections with other students, professors, and mentors. Jane Gromova, Finance Transformation Consultant, Deloitte  |  MSF ’21

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The Graduate Finance and Investment Group sponsors talks by Chicago finance professionals on recent developments in Finance.

Students participate in investment challenge competitions against other universities.

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• Financial Analyst
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• Commercial Banking

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UIC Business offers the resources you need to take your career to new heights.

The MSF Program offers resources to enhance your educational experience including talks by executives from Chicago financial exchanges and trading firms. The market trading lab introduces software used by industry professionals.

Business Career Center

Job search facilitated by the Business Career Center . Professional guidance enables an efficient job search tailored to career goals.

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I wholeheartedly recommend MSF program to any aspiring student who aims to be a financial professional in the world. My experience at UIC was nothing short of exceptional. The program is meticulously designed, offering a comprehensive curriculum that covers both fundamental and advanced financial concepts. As a former UIC MSF exchange student and then UIC MSF degree student, I was always supported by Department of Finance and the College of Business Administration. The professors and faculty are not only experts in their fields but also deeply committed to their students’ success, providing valuable insights and mentorship throughout the program. My time during the MSF program has been instrumental in my career growth, and I am confident that it will continue to produce successful finance professionals for years to come. Bo-Yi Ke, Finance Data Scientist, CNH Industrial  |  MSF’22