thesis about professional development of teachers

Iowa Reading Research Center

How to Make the Most of Teacher Professional Development

Lisa Didion is an assistant professor of special education at the University of Kansas. Her primary research interests include data literacy and teacher professional development (PD). Specifically, Lisa investigates progress monitoring and self-monitoring in reading interventions. She received a PhD in special education from The University of Texas at Austin and an MA in special education from Vanderbilt University. Previously, Lisa was an elementary special education teacher for students with learning, behavioral, and physical disabilities. 

Lisa co-authored the article “ Teacher Professional Development and Student Reading Achievement: A Meta-Analytic Review of the Effects ,” which we highlight as the Research Article of the Month for August 2024 . 

In this blog post, we interview Lisa to learn more about her research and experience with teacher PD and her tips for making the most of PD opportunities. 

Iowa Reading Research Center (IRRC): What is your background in teaching and education?

Lisa Didion: I was a special education teacher for six years at a Title 1 school. My research focuses on creating high-quality professional development, data-based decision-making, and promoting self-determination to improve outcomes for students with or at risk for disabilities.

IRRC: What is teacher professional development?

Lisa Didion: Teacher professional development refers to any learning opportunity provided to teachers to enhance their skills and knowledge. PD can take various forms, such as whole group sessions, coaching, professional learning communities (PLCs), small group sessions, online courses, or summer workshops. 

IRRC: How is teacher professional development typically delivered (e.g., format and length)?

Lisa Didion: PD can be delivered in numerous ways, depending on the desired format and intensity, which includes the duration of the PD sessions over time and the frequency of meetings designed to teach new skills or knowledge. On average, we tend to see PD programs last around 50 hours, and research suggests that 40 to 100 hours of PD may be most effective at improving teacher skills. However, the level of PD quality is more important than the length and format. It matters more that PD provides teachers an opportunity to be active in their learning in PD that is content-focused. 

IRRC: Can teacher professional development have a positive effect on student reading achievement? Why?

Lisa Didion: Absolutely! When teachers engage in PD that allows them to actively practice new skills, receive models, and get feedback through coaching, it can significantly improve their instructional practices. Teachers who adopt evidence-based practices can positively influence student performance, leading to improved reading outcomes. This impact is particularly strong for PD focused on skill development rather than PD focused merely on enhancing teachers' beliefs or knowledge about a topic.

IRRC: What sparked your interest in studying teacher professional development?

Lisa Didion: As a teacher, I often found myself in PD sessions that were not worth my time. The PD developers did not tailor the content to the specific needs of my school and students. I wanted support and was eager to learn new evidence-based practices to enhance my students' academic and behavioral outcomes but was disappointed by the content provided by my district. Through my research, I realized how little we understood about what makes PD effective. We are now beginning to better understand that high-quality PD involves the same instructional practices we value for students, such as active participation, modeling, feedback, and opportunities to practice.

IRRC: What are some tips you would provide school administrators on how to make teacher professional development most beneficial?

Lisa Didion:

• Relevance: Ensure that PD aligns with both district and teacher needs. It's crucial to design PD based on teachers' (and students') needs to avoid wasting time on irrelevant or already well-understood material.
• Clear learning goals: Establish clear teacher learning goals to guide the development of PD. Align these goals with specific content areas (e.g., reading, math) or skills (e.g., classroom management, data-based decision-making), and remain flexible as you learn more about teachers' needs and current performance levels.
• Coaching support: Provide coaching that includes clear models of the skills, opportunities for teachers to practice these skills with feedback, and self-reflection.
• Intensity: Research suggests it may take at least 40 hours of PD spread over time for teachers to improve their skills.
• Focus on student outcomes: Specific to reading, PD has a greater impact on student outcomes in code-focused skills (e.g., phonics, phonological awareness, fluency) than meaning-focused outcomes (e.g., comprehension, vocabulary). Teachers may need more time and support to improve their instruction in comprehension and vocabulary.
• Effective scheduling: Avoid overloading teachers with PD at the start of the school year. Instead, allow them time to prepare their classrooms, as well as collaboratively planning and goal setting. Instead, use PLC time for sustained PD and coaching support that directly addresses teachers' needs.

IRRC: Do you have any memorable professional development experiences as a teacher or as a presenter?

Lisa Didion: My favorite PD experience as a teacher was with a presenter who called herself the "Behavior Doctor." She provided dozens of classroom management strategies that I could use the very next day. It was a valuable use of my time, as I could immediately brainstorm and implement a plan in my classroom. 

This is the approach I strive to achieve in the PD sessions I deliver. I want teachers to leave with actionable goals and new skills they can start using right away. If done effectively, PD is crucial to support teachers and students for improved outcomes.

• professional development
• professional learning communities

Research Article of the Month: August 2024

Six More Educators Earn Dyslexia Specialist Endorsement

Inaugural Group of Educators Earns Dyslexia Specialist Endorsement

The development of statistical reasoning in primary school students

While formal statistical practices are not generally accessible to students in the primary years of schooling, the principles underpinning statistical thinking and reasoning—such as posing questions, collecting data, comparing groups, and representing and inferring from data—are relevant in primary mathematics (Watson et al., 2018). Recent Australian studies by English (2012, 2013, 2018), Fielding-Wells (2014, 2018 a,b), Kinnear (2013, 2018), Makar (2014, 2016, 2018), Mulligan (2015) and Watson (2018) have focused on primary school students’ capacities to engage in data modelling and on statistical reasoning more broadly. An early years’ approach to the teaching of statistics involves including students’ personal experiences, encourages self-collected data sets, and emphasises the reasoning process rather than outcomes or conclusions (Doerr, et al., 2017). How young students’ develop and apply the modelling and refinement process is not clearly understood however, especially when working with an abstract or complex data set. This thesis aimed to gain a more coherent understanding of the developmental aspects of Grade 1 through 4 students’ statistical reasoning and metarepresentational competence with explicit emphasis upon predictive reasoning.

Three interconnected design studies on model-based reasoning and predictive reasoning were conducted with 46 Australian students drawn from one cohort of a single, independent, metropolitan primary school. In the first design study, nine high-ability Grade 1 students created a word-based model for categorisation of self-portraits drawn by students in other grades, and assessed the model using three reasoning tasks. Of interest were the features of the modelling process observed in Grade 1 students, and how students’ used test data collected from the model to inform judgements regarding its efficacy and limitations. The second design study focused on predictive reasoning. How Grade 2 students used the variability of the temperature table to inform their predictions, how they justified predictions and their use of probabilistic language was the focus. Ten high-ability Grade 2 students, including seven students retained from the previous study, predicted maximum monthly temperatures from a temperature table then plotted their predictions against background temperature readings using TinkerPlots TM .

For both design studies, student predictions, representations and explanations were coded using three levels of statistical reasoning: idiosyncratic, transitional and quantitative (Leavy, 2008). Seven of the Grade 1 students were observed using data-based reasoning when justifying and revising their decisions. Six of the Grade 2 students made predictions similar to other monthly values in the data table, increasing to nine students after plotting the predictions with TinkerPlots TM . All ten students used probabilistic language when describing the data set, including terms such as outliers, clusters and range.

Following this pilot work, the main study employed 46 students from Grade 3, and 44 of the same students from Grade 4 in a longitudinal teaching experiment. Students predicted maximum monthly temperatures for the current year using a data table containing past maximum temperatures, represented the data table using informal freehand inscriptions or graphing and described their predictive strategies in verbal and written form. Data were collected at the beginning of Grade 3 and the beginning and end of Grade 4 using the same tasks. Data were coded using a data lenses framework (Konold et al., 2015) in Grade 3 and a framework for analysis of structural features (Awareness of Mathematical Pattern and Structure [AMPS]) (Mulligan & Mitchelmore, 2009) in Grades 3 and 4. Most Grade 4 students (87%) made predictions within the historical range, relative to half in Grade 3 (54%). Representations included co-ordinate graphing including column, line and dot plots and were more sophisticated in Grade 4, with 57% demonstrating data transnumeration, while in Grade 3 they were predominately idiosyncratic or copies of the data table. Grade 4 students were more likely (79%) than Grade 3 (51%) to use and describe predictions based on extraction, clustering, aggregation, noticing seasonal trends and range, identifying causal and random variation, and observing measures of central tendency. Large individual differences emerged: three developmental pathways are illustrated through case studies of high, average, and low ability students. This range suggests that pathways for predictive reasoning are somewhat flexible or idiosyncratic.

The design studies in this thesis demonstrated the advanced potential of some young students to reason statistically: Grade 1 students developed a viable word-based model using a complex data set, and Grade 2 students employed TinkerPlots TM to critique their data predictions. Levels of statistical reasoning in these students was higher than previously reported in studies of students in first and second grade such those by Makar (2016) and Lehrer and Schauble (2000b), as demonstrated through their use of data when justifying their reasoning.

The longitudinal study on student predictive reasoning and meta-representational competence contributes to a more in-depth or fine grained analysis of the possible developmental sequence of these capacities across Grades 3 and 4. Primary school students used contextual cues and data content when they make predictions, and appear to make realistic predictions from data tables prior to being able to describe viable prediction strategies, or to select data for representational purposes. However, other skills appear to develop unevenly— some students developing meta-representational competence and formal graphing prior to reasoning about their strategies, while other students developing reasoning strategies prior to meta-representational competence. Intermediate stages of transnumeration of data tables to formal graphs were described, providing a comprehensive longitudinal set of student representations from a single data set. The studies contribute to a growing body of research that investigates the predictive and data-modelling capacities of young students, and makes a distinct contribution by reporting on the use of TinkerPlots TM as a visualisation tool with second graders. The research supports the inclusion and extension of curriculum reform highlighting data-driven learning, and the development of statistical concepts that are integral to statistical literacy and mathematics learning. Research implications include arguments for more explicit outcomes in the Statistics and Probability strand of the mathematics curriculum on informal statistical inference and data exploration in the early years. This needs to be accompanied by newly developed professional development programs, resources and support for teachers’ acquisition of pedagogical content knowledge in statistical reasoning, and for primary school students to have extended opportunities for informal data representation prior to the introduction of formal graphing instruction.

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Awarding institution, degree type, department, centre or school, year of award, principal supervisor, additional supervisor 1, usage metrics.

• Other education not elsewhere classified

Education Specialist in School Psychology

The Education Specialist (Ed.S.) program in School Psychology is fully accredited by the  National Association of School Psychologists (NASP) until August 1, 2030 . It is designed to prepare students to meet the Department of Education requirements for certification in West Virginia and other states. The Ed.S. consists of 39 hours of required coursework and field experiences, in addition to a Master of Arts (M.A.) Degree in Psychology with an emphasis in School Psychology (36 hrs). For information about faculty, candidates, and their outcomes in the program, please refer to the Program Annual Report and Student Outcomes Data . This data may be of interest to current or prospective candidates.

You apply to the Master’s Degree in Psychology with School Psychology Emphasis first. After successfully completing 12 hours in the fall semester, you apply to the Ed.S. in School Psychology. You receive two degrees in our program after three years of full time enrollment! Students who apply to the Ed.S. School Psychology program and already possess graduate degrees in psychology, counseling, or education may be considered for advanced standing after a review of coursework, grades and test scores by the School Psychology faculty.

The School Psychology Ed.S. Program at Marshall University focuses on practitioner training for the delivery of comprehensive school psychological services primarily in school settings. Ed.S. students are trained in a “scientist practitioner” model that emphasizes the use of research findings in clinical decision-making, both in terms of creating interventions as well as evaluating their efficacy. Ed.S. students draw upon scientific literature to make decisions regarding best practice with a focus on empirically supported services.

Students receive education and training in the following ten NASP domains:

• Data-Based Decision Making
• Consultation and Collaboration
• Academic Interventions and Instructional Supports
• Mental and Behavioral Health Services and Interventions
• School-Wide Practices to Promote Learning
• Services to Promote Safe and Supportive Schools
• Family, School, and Community Collaboration
• Equitable Practices for Diverse Student Populations
• Research and Evidence-Based Practice
• Legal, Ethical, and Professional Practice

MA in Psychology with School Psychology Emphasis Plan of Study

Core Requirements:

• Biological Bases of School Psychology (SPSY 674)  or Biological Bases of Behavior (PSY 674)
• Intermediate Behavioral Statistics (PSY 517)
• Cognitive and Emotional Bases of School Psychology (SPSY 675) or Cognitive and Emotional Bases of Behavior (PSY 672)
• Cross-Cultural Psychology (PSY 526)
• Advanced Developmental Psychology (PSY 615) or Typical and Atypical Child Development (SPSY 616)
• Ethical and Legal Issues in Psychology (PSY 605)
• Data Based Decision Making II (SPSY 621)  or Psychometrics (PSY 506)
• Research Seminar (PSY 692)

Area of Emphasis Requirements:

• Schools (SPSY 601)
• School Consultation (SPSY 617)
• Instruction Methods and Behavior Modification (SPSY 618)
• Psychotherapy with Children (PSY 619) or Individual and Group Counseling (SPSY 619)

Total: 36 Credit Hours

Education Specialist in School Psychology Plan of Study

• Professional Competence II: Professional School Psych (SPSY 603)
• Data-Based Decision Making II (SPSY 622)
• Data-Based Decision Making III (SPSY 624)
• Practicum I (SPSY 738)
• Practicum II (SPSY 739)
• Counseling with Youth: Advanced Topics (SPSY 720)
• Application of Multi-Tiered Systems of Support (SPSY 710)
• Indirect Service Delivery II: Primary Prevention (SPSY 620)
• Research: Thesis (SPSY 750)  or Program Evaluation (SPSY 751)
• Internship (SPSY 745, 12 hours)

Ed.S. Program Requirements:

Students are admitted to the Ed.S. program with the expectation that they will complete the program within three to five years, depending on the number of classes in which they enroll each semester. Students are expected to enroll in all three semesters.

Failure to make the expected amount of progress may result in reassignment to another internship year. The internship requires a commitment to a school system for a full academic year and is 1200 hours (12 credit hours). A maximum of 18 students will be admitted to this final-year experience.

A minimum of half of this paid internship must occur within a school setting and meet stringent criteria specified by the program and the National Association of School Psychologists. In addition, students must defend a thesis or program evaluation and earn a passing score on the Praxis II Specialty Examination in School Psychology before graduation.

Total: 39 Credit Hours

Admission Requirements

Admission is competitive because of the limited number of available internships. Applicants should follow the admissions process described in this catalog or at the Graduate Admissions website at www.marshall.edu/graduate/admissions/how-to-apply-for-admission .

In addition, applicants must submit a sample of their professional writing (a scholarly paper on any subject) and a statement of their professional goals (1000 words or fewer). Submit all materials to Graduate Admissions.

Creating a diverse student body is a priority and minority applicants are encouraged to apply. A limited number of graduate assistantships are available. Students can be accepted into the school psychology program throughout the school year; however, school psychology competency classes begin in the fall semester of each academic year.

Lanai Jennings Program Director South Charleston GC 108 304-746-2067 [email protected]

Frequently Asked Questions

School Psychology is a professional field that blends psychology and education. This program is designed to help you obtain the skills you will need to become a successful School Psychologist. If you are interested in the fields of Psychology and Education, and you like working with children, then this program may be right for you.

Yes! You obtain a Master of Arts in Psychology with a School Psychology Emphasis and an Education Specialist in School Psychology after three years of full time enrollment in this program.

While our program is not totally online, for all of our courses, students can attend the majority of class meetings virtually. We do however, require students to come to campus an average of 3 times a semester.

The purpose of the Education Specialist in School Psychology Program at Marshall University is to prepare professional school psychologists to work within the social systems of schools to meet the following goals:

• Apply their knowledge of psychology and education in order to prevent or remove the barriers to optimal growth and development at the community, school, classroom, and individual child level.
• Apply the problem-solving process within a collaborative consultation model that embraces both direct and indirect service delivery.
• Ensure professional competence based on a solid foundation of ethical, legal, and responsible practice that respects human diversity and individual differences.
• Apply knowledge and skills in conducting and interpreting research applied to practice.
• Apply knowledge and understanding of the multiple systems that influence growth and development.
• Ensure a broad range of quality services in primary, secondary, and tertiary prevention to serve universal, targeted, and selected populations.
• Apply skills in program evaluation to improve service to individuals, families, schools, and communities.

Integrate technological applications to facilitate all the above goals.