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Scientific Method Doodle Notes

Subject: Biology

Age range: 11-14

Resource type: Assessment and revision

Last updated

22 February 2019

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Help your students learn the scientific method. Teach them the steps of the scientific method, vocabulary related to designing experiments, how to write a hypothesis and much more!

Scientific method doodle notes are designed to compliment your unit of study on the properties of matter and include plenty of space to add additional notes. PLUS 4 bonus pages that can be used by your students to design their own experiments. The worksheets walk your students through the process of designing an experiment to help facilitate successful scientific inquiry.

This product contains 22 pages – 9 blank student note pages and 9 teacher answer key pages.

Content covered includes:

• What is science
• History of the scientific method
• Steps of the scientific method
• Experimental design and variables
• Practice writing hypotheses
• Observations versus Inferences

4 page worksheet for students to design their own experiments

What are Doodle Notes?

Doodle notes are a visual note-taking strategy that encourages students to draw, doodle, colour, and embellish their in-class notes to increase content retention. Giving students permission to doodle as they listen and learn in class helps to keep them engaged in the lesson and improves overall focus.

How to use Doodle Notes:

• Project the notes using a document camera or onto the whiteboard and fill them in with your class while talking through the information.
• Use them with a regular PowerPoint lesson and let students fill in the notes as they view the PowerPoint.
• Have students read the material from a source and fill in their doodle notes on their own.

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Doodle Vocabulary Notebook Set Up Guide

Creating a vocabulary doodle notebook will help your students practice science vocabulary specifically aligned to the Next Generation Science Standards. Vocabulary science doodle notes make great bellringer or independent practice activities! Read on to learn how to create a custom doodle notebook that matches the standards covered in YOUR course!

How do I create a doodle notebook with my students?

• Finding the correct pages for your curriculum
• Printing the doodle note pages you need
• Printing a notebook cover page
• Printing the directions for your students
• Using the interactive links
• Scaffolding the process for your class

How are the pages organized?

Each doodle note page lists the related Next Generation Science standard in the top right hand corner. You will need a copy of your curriculum to find the standards that your course covers before printing our your pages. For example, our 6th grade curriculum covers some Life, a little Earth and Space and a bit of Physical!

What topics do you teach? Do you cover only one discipline OR do you teach a mix of LS, ESS and PS?

If you aren’t totally sure of your standards, or teach in a state that is NOT using NGSS, then use the bundle guide to help you figure out which pages match your topics.

If you are new to Captivate Science Doodle Notes, be sure to read this post about the life science and physical science doodle notes. It gives some more background about how each page is set up and offers a free sample. The entire set of NGSS notes can be purchased here at a discount.

Printing Tips:

It’s time print your pages. I like to use composition notebooks (they stack nicely in bins) but some people like spiral notebooks (allow for larger pages and more room for large student writing). If you choose composition notebooks you will need to print your pages at 85% to fit the page.

I recommend gluing in pages as you go. For example, our first unit covers 4 standards. I have my students glue in the page(s) they will need for the next week or so. It just seems more manageable than gluing them all at the start of the year!

You can have kids make their own cover, decorate the cover with stickers and pictures and/or print a cover! I like to have kids glue the cover on the first page of the journal. I never have a lot of luck with gluing things to the outside hard cover of marble notebooks 🙂

Grab a PDF of free covers to use with your vocabulary doodle notes!

Print and glue in a set of directions for your students to reference. These explain how they will need to use the clickable links to find information to write and doodle their answers!

Interactive Links for Each Page

Now that your pages are printed, cover and directions are glued in, you are ready to access the interactive links! You will need a platform for sharing each link that you want kids to use.

Getting kids started: It is important to do some scaffolding for the first page or so. Once your students learn the ropes they will be able to work on their own. I like to model how do the first few words together. I suggest projecting the interactive page on your board and showing students how to navigate the information. Students will need to READ and watch videos to gather information and many of my kids need me to model that process (at first). I also like to have my doc camera out for the first few words. It helps to show them how to color, draw and write and what a quality example looks like (even though my actual drawing skills can be pretty funny sometimes!)

By creating a custom doodle note journal, you are helping your students build vocabulary for your specific NGSS standards. We know that kids need repeated practice to build understanding and this is another tool to support your students in their science growth!

We are glad you stopped by Captivate Science! Happy Doodling 🙂

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3D Pen Lessons

Stem: doodle-engineering-challenge (scientific method).

In this lesson, pairs of students will use the 3Doodler in an attempt to build the tallest structure in the class. In addition to the 3Doodler, students will be given either toothpicks or straws as construction materials. The 3Doodler will be used to adhere the building materials together. Students will use higher-level thinking skills to make predictions in order to form a hypothesis, record materials, observations, results, and analyze structures to determine the key to building the tallest tower.

had experience using the 3Doodler to weld objects together.

with constructing towers using blocks.

practicemeasuring in inches with a ruler.

build a structure using the 3Doodler and wither toothpicks or straws.

record a hypothesis about the height of their intended structure in inches in a lab format.

record materials to be used in a lab journal format.

record observations in a lab journal format.

record data in a lab journal format.

record conclusions in a lab journal format.

analyze and synthesize information to determine the key to building a tall structure.

3Doodler (1 per pair)

Toothpicks or Straws (52 per pair)

Doodle-Tower-Lab Worksheet

Instructions

Bring the students together for a group discussion.

Share the goal: During this session, paired students will use the 3Doodler in an attempt to build the tallest structure in the class.

Depending on the grade level, you may prefer having students work with toothpicks with rounded tips or paper straws.

*Note that straws must be paper, not plastic.

Demonstrate how to begin the structure with a foundation composed of a flat shape, Students may create a triangular, rhombic, or square-shaped base for their towers. Weld each of the corners together using the 3Doodler, then begin adding levels and working up.

*Older students may require less discussion before attempting the challenge.

*Younger students may require help from an aide, teacher or older students.

Project your computer or tablet on the screen for students to view the Doodle-Tower-Lab Worksheet . Go over with them how they are supposed to fill it in.

Review how to measure inches with a ruler.

Divide students into pairs and hand out the Doodle-Tower-Lab Worksheet . Instruct students to complete questions 1 and 2 before getting started.

Hand out the construction materials and the 3Doodlers. Circle to assist and assess as students work.

When students have completed their construction, prompt them to finish the Doodle-Tower-Lab Worksheet with their partners.

Possible Extensions

Reference 1

Reference 2

Reference 3

analyze - examine methodically and in detail the constitution or structure of (something, especially information), typically for purposes of explanation and interpretation.

balance - an even distribution of weight enabling someone or something to remain upright and steady.

collaboration - to work jointly with others or together especially in an intellectual endeavor.

conclusion - a judgment or decision reached by reasoning.

construction - the building of something, typically a large structure.

data - factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.

design - to prepare the preliminary sketch or the plans (for a work to be executed), especially to plan the form and structure of an object, building, bridge, etc...

engineering - the art or science of making practical application of the knowledge of pure sciences, as physics or chemistry, as in the construction of engines, bridges, buildings, mines, ships, and chemical plants.

hypothesis - a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation.

observation - an act or instance of noticing or perceiving.

problem-solving - the process or act of finding a solution to a problem.

STEM - science, technology, engineering, and mathematics, considered as a group of academic or career fields.

synthesize - combine (a number of things) into a coherent whole.

Educational Standards

Participate in collaborative conversations with diverse partners about grade level topics and texts with peers and adults in small and larger groups.

Students will discuss how to build a sturdy, tall structure with their partners, their teacher, and and their peers.

Build on others' talk in conversations by linking their comments to the remarks of others.

Students will build on the talk of others during the group discussion and through the discussions with their partner throughout this project.

Ask questions, make observations, and gather information about a situation people want to change to define a simple problem that can be solved through the development of a new or improved object or tool.

Students will attempt to build the tallest structure. Students will gather information, discuss their results and then attempt to develop an improved tower.

Decompose (break down) a larger problem into smaller sub-problems with teacher guidance or independently.

Students will break down the process of building the tallest tower through the Scientific Thinking process of recording a hypothesis, observations, data, and conclusions.

Use technology to seek feedback that informs and improves their practice and to demonstrate their learning in a variety of ways.

Students will use a 3Doodler to construct towers of various heights and designs.

Create original works or responsibly repurpose or remix digital resources into new creations.

Students will construct an original design plan with a 3Doodler.

Use collaborative technologies to work with others, including peers, experts or community members, to examine issues and problems from multiple viewpoints.

Students will work with a partner and with their peers throughout the doodling, problem solving and creation processes.

More Lesson Plans

Benefits of Doodling while Learning and Studying

It is 10 in the morning. I stare at the board in my Algebra Two class trying to focus as much as possible. We continue doing notes and I notice my hand creating doodles of eyes, trees, and random shapes while simultaneously taking in the lesson my math teacher is going through. By the end of class, my paper was covered in little drawings, yet I still understood the math lesson.

Doodling! From scribbling on a piece of paper, to a final result that came from something completely unexpected, doodling may not always be an intentional act of art but it can be a significant one. This art practice has been around for thousands of years, and has been recently used in classrooms - not just art classrooms, though. 

Though not all teachers allow for doodling because they find it distracting for students who doodle and their peers, doodling can still be used at home to enrich one’s learning. 

Recent studies have shown that doodling can aid students in many aspects of school, like learning and memorization. This blog will go over the benefits of doodling for students when trying to memorize and focus on concepts whilst studying and how doodling can be incorporated into students' study routines. There are a magnitude of study methods that are easily adaptable to adding in the creative touch of doodling for students who decide that this is a viable method!

Voicemail study

A psychologist named Jackie Andrea did a study in 2009 involving 40 people monitoring a short, dull voicemail. 20 people doodled and the other 20 did not. These people had no idea that they were being tested in their memories once the voicemail finished. The end result was that the 20 people who doodled had recalled 29% more information than the group who did not. The group that doodled was more engaged and surpassed the other group in active recall testing (Pillay). 

As displayed by the study, people who doodled had recalled memories with a far higher success rate than those who did not. There is still a question though: how? How does doodling help with memory?

The science behind doodling

The creative process of doodling helps calm the amygdala, which is located in the medial temporal lobe in the brain (Free Press Journal). This part of the brain is involved with fear function, as in the fight or flight responses. It also processes threatening stimuli (Baxter and Croxson). Doodling can calm down a specific part of our brains, allowing us to study with greater success. 

Additionally, psychological distress is consistently lessened by doodling. Doodling is a way to fill in the gaps in your thought processes and glue these processes together. Doodling locates lost memories and brings them back during active recall. Doodling also improves focus by lowering cortisol levels which decreases stress (Roberts). With less stress, it becomes much easier to focus.

As a student, I know that it can be difficult to concentrate with distractions in your surroundings. However, by doodling a simple picture you are able to refocus and zero in on the assignment at hand. 

Specific doodles

There is a common debate regarding which types of doodles should be used while studying. The types of doodling that students should focus on are repetitive designs that are meaningless and completed at the student’s own pace (Perles). 

In contrast, specific doodles should be avoided while trying to focus while studying. For example, doodling your surroundings or a person in front of you would be distracting.

Doodling and ADHD

Doodling can be appealing for people with ADHD. For students with ADHD, doodling can help with staying focused on studying and not zoning out, as doodling requires constant stimulation to create an art piece. Doodling is a simple way to multitask within the bounds of studying.

Doodling can even mimic typical ADHD medication effects, increasing levels of dopamine and norepinephrine through the use of neurotransmitters (Neel). As doodling is often considered fidgeting, people with ADHD can reap the benefits of studying by doodling while focusing on a primary task.

How to doodle while studying

There really is no way to add doodling into how you study, as we all use different techniques. Still, it is easier to doodle while doing paper based assignments. There are two distinct methods that can be applied to technology and paper based studies. 

• Technology studying: Have a separate sheet of paper next to your laptop/device. While you are reviewing information and practicing, feel free to curate a page full of doodles. Whether that just be scribbling or actual designs is your choice! This way you will not have to figure out doodling all over a computer screen.
• Paper studying: For studying on paper, you can either doodle on the same pieces of paper or switch to a separate piece of paper, like the method above for technology. It may be difficult to stay organized with many papers though, so try to keep your space as clear as possible. You could even use a different colored piece of paper, such as construction paper. Or, if you want to keep your doodling to a minimum, you can use a smaller piece of paper, such as a sticky note or index card.

Doodling is a valuable practice that can be used for studying. It helps students stay calm, makes it easier for students to focus for longer increments, and improves active recall. It doesn't matter the type of paper you use, the writing material you use, or how long you go doodling through your studies,you just have to do what is best for you! And, again, remember that there's no right or wrong way to doodle. Happy doodling and studying! 

Works Cited

Baxter, M. G., and P. L. Croxson. “Facing the Role of the Amygdala in Emotional Information Processing.” Proceedings of the National Academy of Sciences , vol. 109, no. 52, Dec. 2012, pp. 21180–81, doi:https://doi.org/10.1073/pnas.1219167110.

“Mental Health: 6 Reasons Why Doodling Is the Best Way to Destress and Unlock Your Unconscious Creativity.” Free Press Journal , 2023, www.freepressjournal.in/lifestyle/mental-health-6-reasons-why-doodling-is-the-best-way-to-destress-and-unlock-your-unconscious-creativity.

Neel, Dr Monica. “Mindful Living - Doodling.” Thomasneel.com , 2014, thomasneel.com/live-an-artful-life-archive/29-live-an-artful-life/36-mindful-living-doodling#:~:text=The%20value%20of%20doodling%20may.

Perles, Keren. “The Power of Doodling | Education.com.” Www.education.com , 2014, www.education.com/magazine/article/power-of-doodling/.

Pillay, Srini. “The ‘Thinking’ Benefits of Doodling - Harvard Health Blog.” Harvard Health Blog , 12 Dec. 2016, www.health.harvard.edu/blog/the-thinking-benefits-of-doodling-2016121510844.

Roberts, Caroline. “Doodlers Rejoice -- You’re Probably Paying Better Attention at Meetings than Anybody.” CNET , 2019, www.cnet.com/health/doodling-can-help-you-pay-attention/.

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Research Article

Note-taking for the win: Doodling does not reduce boredom or mind-wandering, nor enhance attention or retention of lecture material

Emily Krysten Spencer-Mueller, Mark J. Fenske

This is a preprint; it has not been peer reviewed by a journal.

https://doi.org/ 10.21203/rs.3.rs-2786955/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Doodling and fidgeting—traditionally viewed in educational contexts as markers of inattention and poor classroom behaviour—have more recently been considered as possible routes to improve performance by reducing boredom and its negative impact on memory. However, there is a surprising lack of well-controlled studies examining this possibility, despite the widespread adoption of fidget toys and doodling exercises within classroom settings. Here we report two experiments (total N = 222) that assess the impact of doodling on boredom, attention, mind-wandering, and subsequent recall of auditory information. In Experiment 1, participants first listened to a 15-minute section of a lecture known to induce boredom. Immediately thereafter they were asked to jot down important information from a short voicemail that they listened to while either doodling (adding shading to shapes) or doing nothing in between note-taking. In Experiment 2, participants listened to a 45-minute section of the same lecture under one of four conditions: structured doodling (i.e., shade in shapes), unstructured doodling, note-taking, or listen-only. Thought probes assessed self-perceived levels of state boredom, mind-wandering, and attention throughout the lecture. Across studies, doodling neither reduced boredom or mind-wandering nor increased attention or retention of information compared to other conditions. In contrast, attention and test performance were highest (and boredom and mind-wandering lowest) for those focused solely on note-taking.

Introduction

At some point in your life, you have likely felt the aversive experience of boredom, which results from various factors that affect an individual’s ability to engage in a satisfying activity (Eastwood, Frischen, Fenske, & Smilek, 2012). Evidence suggests that boredom is related to decreased attention (Gerritsen, Toplak, Sciaraffa & Eastwood, 2014; Malkovsky, Merrifield, Goldberg & Danckert, 2012; Hamilton, Haier & Buchsbaum, 1983; Hunter & Eastwood, 2016; Carriere, Cheyne & Smilek, 2008; Seib & Vodanovich, 1998; Damrad-Frye & Lard,1989; Fisher,1998), increased instances of mind-wandering (Pachai, Acai, LoGiudice & Kim, 2016; Seli, Carriere, Smilek, 2015; Steinberger, Moeller & Schroeter, 2016; Critcher & Gilovich, 2010), and can hinder performance on a variety of tasks and tests (Pekrun, Hall, Goetz & Perry, 2014; Fitea & Fritea, 2013; Tze, Daniels & Klassen, 2016). Students are at a higher rate of suffering the negative impacts of boredom due to its overwhelming presence and persistence in the academic environment (Sinclair, Jang, Azevedo, Lau, Taub & Mudrick, 2018). During such periods of boredom, there may be observable increases in fidgeting behaviour— an onset and an increase in unnecessary body movements (Mehrabian & Friedman, 1986). A form of fidgeting—task-irrelevant doodling— may be a functional method of reducing the negative impact of boredom on memory (Andrade, 2010). However, there is a surprising lack of well-controlled studies examining the possibility that doodling or fidgeting is beneficial, despite the adoption of fidget toys and doodling exercises within many classroom settings. Such educators may have thereby put the cart before the horse by adopting methods that lack empirical support regarding their effectiveness (for review, see Kriescher et al., 2022). The research reported here focused on testing competing hypotheses regarding the effect of doodling during episodes of boredom to get a better understanding of its impact on learners.

Boredom, Mind-wandering, and Attention 

                Boredom is a commonly-experienced state that is thought to occur when we are unable to fully engage our attention with either external environmental stimuli or internal stimuli such as our thoughts or feelings (Eastwood et al., 2012). According to this perspective, when we experience difficulty becoming attentionally engaged in a satisfying way, we typically attribute that experience and the corresponding feelings of boredom to the inadequacies of the environment. Attention is therefore a critical component of the experience of boredom (Eastwood et al., 2012). Indeed, the less attentive an individual is in an otherwise-relevant situation, the higher chance they have of perceiving that situation as boring (Gerritsen et al., 2014). Likewise, when the circumstance involves a task that is difficult to attend to, the attentional difficulty predicts levels of boredom (Carriere, Cheyne & Smilek, 2008). 

                While difficulty with attentional engagement may lead to boredom, it is also worth considering how an activity that is perceived to be boring may, in turn, impact the ongoing focus of attention. Rather than struggling to stay focused on an external task that elicits boredom, switching attention to internal thoughts may allow your mind to save cognitive resources for information that is ultimately more useful (Pachai et al., 2016). The resulting mind-wandering may be either intentional (i.e., deliberately letting your thoughts drift from the situation or task) or unintentional (i.e., your thoughts unintentionally drift from the circumstance or task) (Seli et al., 2015). Unintentional mind-wandering is thought to be a marker of boredom to the extent that when people realize their mind has involuntarily drifted, they take that as an indication that it occurred because they must have been bored (e.g., Critcher & Gilovich, 2010). Thus, any activity that acts to reduce boredom may also be expected to have a corresponding impact in reducing levels of mind-wandering.

                Because the experience of boredom is quite common for many students, it can be a persistent problem for educators trying to help students learn (Sinclair et al., 2018). This is underscored by the possibility that boredom-related failures of attention and increases in mind-wandering can impact performance (e.g., Fritea & Frieta, 2013; Tze, Daniels, & Klassen, 2016; Eren & Coskun, 2016; Pekrun et al., 2014; Goetz et al., 2012), including in specific educational settings. A study conducted by Pekrun, Hall, Goetz, and Perry (2014), for example, found a reciprocal relation between boredom and exam performance for university students. They found that the level of course-related boredom was linked to worse exam scores, not only in that course (see also Fitea & Fritea, 2013) but in other classes as well. Moreover, Pekrun et al. (2014) observed that poor exam performance was predictive of subsequent levels of boredom in future scenarios. To evaluate the relationship between boredom and academic performance more directly, Tze, Daniels, and Klassen (2016) conducted a meta-analysis of results from twenty-nine studies involving more than 19,000 students. Their analysis suggested that the negative impact of boredom on subsequent learning and achievement was greatest when experienced in class, rather than when studying elsewhere. Such links between in-class boredom and impaired learning emphasize the importance of educators being able to detect when a student is experiencing boredom. Having an easy-to-implement intervention that can then reduce boredom and the associated negative consequences would also be of great value.

The Possible Functional Role of Fidgeting and Doodling During Boring Episodes

                In a learning environment, the onset and rise in the unnecessary body movements known as fidgets— defined by Mehrabian and Friedman (1986) as the engagement or manipulation of objects (e.g., pens, fidget toys) or your own body (e.g., tapping your foot)— is traditionally viewed as a critical marker for the onset of boredom (Seli et al., 2014). Fidgeting as an indication of one’s boredom became a noteworthy topic in the scientific literature as early as 1885 when Galton observed fidgets in bored individuals during a lecture:

When the audience is intent each person forgets his muscular weariness and skin discomfort, and he holds himself rigidly in the best position for seeing and hearing…But when the audience is bored the several individuals cease to forget themselves and begin to pay much attention to the discomforts attendant on sitting long in the same position. (Galton, 1885, p.174)

What is interesting about Galton’s analysis is the notion that attention has a lot to do with the point at which people begin to fidget. It may be that when the individual’s task-related attention begins to decline, they become increasingly aware of their current level of boredom. As attention increasingly shifts from the task to unrelated thoughts, it seems the body increasingly begins to wander (Seli et al., 2014). This potential link between boredom, mind-wandering and fidgeting suggests it may be possible to know when an individual is experiencing boredom and failures of attention based on their outward behaviour. Interventions aimed at reducing boredom and its negative cognitive consequences may therefore be most effective when targeted at periods showing increases in task-irrelevant movements. 

Fidgeting has often been viewed in educational contexts as a marker of inattention and poor classroom behaviour. Research on naturalistic fidgets (e.g., swaying in your chair) assessed this view by evaluating whether fidgeting is indeed an indication of inattention. Farley et al. (2013), tested this perspective using both self-report measures of attentiveness during lecture viewing and observations of participants’ fidgeting behaviour. Their results suggest that fidgeting impacted the ability to retain lecture material over and above the effect of inattention, thus implying that fidgeting was not merely a marker of inattention. They suggested that fidgeting may have helped to offset the negative distress of having to sustain attention for a prolonged period. However, while boredom could have been the form of distress thought to be offset by fidgeting here, it was not specifically measured or manipulated in this study, nor was the potential boredom-reducing benefit of fidgeting for helping students subsequently re-engage task-focused attention. 

In addition to its characterization in terms of attention, boredom has also been defined as being a “state of nonoptimal arousal” that occurs when there is a difference between the physiological activity required for an individual’s peak performance and the stimulation available within their environment (see Eastwood et al., 2012 for review). A state of boredom can thereby arise during either insufficient or excessive levels of arousal. According to this perspective, boredom with low arousal may manifest as apathy and tiredness, whereas boredom with high arousal may manifest more as frustration and agitation.  The Optimal Stimulation theory developed by Hebb (1955) was used by Kercood and Banda (2012) to explain how organisms achieve ‘stimulatory’ homeostasis through stimulation-seeking activity (i.e., fidgeting, physical activity). Accordingly, individuals are motivated to achieve an optimal level of arousal and will seek out ways to achieve that level. Fidgeting may thereby be functional in helping an individual regulate arousal levels to a point that allows them to re-engage their attention (Hartando, Krafft, Iosif & Schweitzer, 2015; Mead & Scibora, 2016; Stalvey & Brasell, 2006; Kercood & Banda, 2012; Andrade, 2010). Research on doodling published by Andrade (2010) provided preliminary support for such a theory.

Doodling is a commonly used form of task-irrelevant behaviour, which involves creating marks or drawings of things that are entirely unrelated to the information to which an individual is supposed to be attending (Meade, Wammes & Fernandes, 2019). More recently, interest in the potential usefulness of doodling as a learning technique has increased following Andrade’s (2010) demonstration of its ability to reduce boredom and increase recall of information. Participants in Andrade’s study were all asked to listen to a two-and-a-half-minute voicemail that mentioned the names of multiple people and multiple cities. The doodling group was also asked to shade in a set of shapes on a sheet of paper while listening to the voicemail, whereas the control group was asked to only listen. Both groups were asked to write down each of the people’s names mentioned in the voicemail as soon as they heard them. A surprise test was given afterward in which participants were asked to recall, not only all of the names they had written down but also all of the city names they had heard. Andrade reported that the participants who doodled while listening to the voicemail recalled significantly more of the voicemail information than those in the control group. Because participants in this study had just completed a much longer study before taking part in this experiment, Andrade speculated that the doodling-related improvement might have been due to doodling increasing levels of arousal and thereby reducing boredom and mind-wandering. 

Unfortunately, however, boredom was never intentionally induced or measured in Andrade’s (2010) study. Any differences in the level of attention or mind-wandering for participants in the different groups were also not formally considered.  Moreover, the results of recent research (Boggs, Cohen & Marchand, 2017) that focused on the effects of doodling on mind-wandering are inconsistent with those reported by Andrade (2010). Specifically, Boggs et al. (2017) did not see any benefits of having participants do structured doodling (shading in shapes) when compared to performance when participants only listened in their task, while unstructured doodling (creating free-form images) actually hindered performance relative to the other conditions (see also Meade, Wammes & Fernandes, 2019). These conflicting findings, when considered together with information sources that continue to promote the effectiveness of fidgeting and doodling for combatting boredom and enhancing attention (e.g., Rotz & Wright, 2021) it is important to confirm whether doodling is indeed beneficial. 

The Current Study

The present research tests competing hypotheses about the extent to which different methods of doodling may be helpful for cognitive functioning during an experience of boredom. The ‘fidgeting reduces boredom and increases attention’ hypothesis posits that doodling is a beneficial form of fidgeting that can reduce boredom and increase attention to promote better learning (Andrade, 2010). In contrast, the ‘fidgeting reflects inattention’ hypothesis maintains that doodling is merely an indication of the mind taking a mental break, thereby reflecting the absence of task-focused attention (mind-wandering; Boggs et al., 2017) and would therefore be linked to relatively poor learning. Distinguishing between these two possibilities is important for understanding how the mechanisms subserving attentional, behavioural, and affective engagement operate together and for resolving the conflict in existing evidence about the extent to which doodling is a viable method for educators and students to implement in the context of learning. 

Experiment 1 is a replication and extension of the study conducted by Andrade (2010) to examine whether doodling can reduce feelings of boredom and levels of mind-wandering while improving task-focused attention and memory for associated information. Our main extension to this study was the addition of a manipulation to induce boredom, and a measurement to ensure the manipulation was successful. In Experiment 2, we build on the results of Experiment 1 by using a more ecologically valid approach in which participants listen to an actual lecture, and then complete a multiple-choice exam that tests retention of lecture material. Like Boggs et al. (2017), we included a listen-only control condition, as well as a note-taking condition, a structured-doodle condition, and an unstructured-doodle condition. To anticipate, our results— consistent with the fidgeting-reflects-inattention hypothesis—indicated that doodling neither reduced boredom or mind-wandering in either experiment, nor increased attention or retention of information when compared to the other conditions. None of our results were consistent with the fidgeting-reduces-boredom-and-increases-attention hypothesis.

Experiment 1

Experiment 1 examined the effect of adding a secondary task (structured doodling) compared to no additional task on subsequent recall ability. This experiment thereby represented an attempt to conceptually replicate and extend Andrade’s (2010) doodling study using similar methods and procedures. Participants reported their feelings of boredom both before and after listening to a boredom-inducing lecture. Participants listened to a voicemail recording immediately thereafter while either doodling and note-taking, or just note-taking, and were then asked to recall information from the voicemail. Our experiment was not an exact replication of the methods of Andrade (2010) due to our addition of the boredom-induction procedure and measure of state boredom, which we deemed necessary to ensure participants were bored before the main voicemail-listening task. The fidgeting-reduces-boredom-and-increases-attention hypothesis, and the results reported by Andrade (2010), predict that participants in the doodle condition should perform better on the subsequent memory test than those in the control condition. In contrast, the fidgeting-reflects-inattention hypothesis predicts that the addition of doodling would reduce attention to the voicemail and—if anything—impair performance on the subsequent memory test when compared to that of the control condition.

Participants and Design

                Participants consisted of 50 undergraduate students at the University of Guelph (age: M =18.9, SD =1.80; self-reported gender: 44 female, 6 male). Participants were recruited from the Department of Psychology participant pool and received course credit for their participation. Once in the lab, participants gave informed consent in writing. All materials, methods, and procedures used in the experiment were approved by the University of Guelph Research Ethics Board (REB protocol #16-12-398). Participants were randomly assigned to a control condition or a doodle condition. 

                 Recorded Lecture . Participants listened to a 15-minute recorded lecture titled “The Dark Ages” from an introductory Ancient Greek History course using a pair of over-ear noise-cancelling headphones. The audio was obtained from OpenYale Courses (http://oyc.yale.edu) and was used to induce boredom. Our previous use of this video in unrelated pilot experiments has revealed that it typically elicits increases in feelings of boredom for our Psychology students.

                 Voicemail. Participants listened to a 2.5-minute voicemail through the same headphones. The voicemail script was adopted from Andrade’s (2010) study and was slightly modified. The script was kept the same except for the names of places were altered to reflect the local geographical area. The speaker was a female who talked in a slow, monotone voice. 

Multidimensional State Boredom Scale-8. (MSBS-8; Hunter, Dryer, Cribbie & Eastwood, 2015) Participants self-reported their feelings of boredom both before and after they watched the lecture video by completing the MSBS-8, which is a shortened version of Fahlman et al.’s (2001) longer measure of state boredom. The MSBS-8 includes items such as “I feel bored”, and “I wish I was doing something more exciting” that are presented with a sliding scale that ranges from 1(strongly disagree) to 7(strongly agree). Scores are summed across items to give a total boredom score, with higher scores indicating greater levels of subjective boredom (max score of 56). 

                Participants were tested in-person in individual testing rooms in a laboratory setting. Before beginning the experiment, participants first provided informed consent and answered basic demographic questions. The first phase of the experiment involved the boredom-induction procedure, consisting of the providing their pre-lecture state boredom rating (MSBS-8), listening to the first 15 minutes of the lecture audio, and then providing their post-lecture boredom ratings (MSBS-8). Immediately thereafter, participants were asked to listen to a short voicemail and pretend that the speaker is a friend who has called to invite them to a party. Participants were randomly assigned to either the doodle condition or the control. For the doodle condition, participants were provided with a regular sheet of white paper containing alternating rows of squares and circles. The right column of the page had a 4.5 cm wide column of blank space. Participants in the doodle condition were asked to shade in the shapes while listening to the voicemail. They were asked to write on the right-hand side of the paper the names of the people mentioned in the voicemail as going to the party. In reference to the shading of the shapes, participants were told “It does not matter how neatly or how quickly you do this – it is just something to help relieve the boredom” (Andrade, 2010). The control condition was given a regular blank sheet of white paper and told to write the names of people going to the party, and not to write anything else. Participants in both conditions were given a regular pencil to write down names and were told they would not be tested on their ability to recall this information. Once completed, participants’ response sheets were collected, and they were asked to wait in the room until the experimenter returned (they had to wait for 1 minute). Each participant was then asked whether they expected a memory test and to orally recall the names of people going to the party (attended information) as well as the names of any places mentioned in the voicemail (unattended information). The experimenter recorded each of the recalled names and places during the memory test. The ordering of the questions was counterbalanced across participants. 

                An independent samples t-test was used to compare the summed MSBS-8 scores from before the lecture for the control group (M= 32.68, SD= 7.45) and the doodling group ( M =31.72, SD = 6.62). This confirmed that there was no significant difference between the groups in levels of state boredom at the beginning of the experiment,  t (48) .48, p =.63, d =.14. To ensure participants were bored before the voicemail listening task, summed MSBS-8 scores from before and after the lecture were analyzed in a paired samples t-test. Results suggest that participants were significantly more bored after listening to the lecture video ( M = 38.46, SD = 8.10), than they were prior to the video ( M = 32.2, SD = 7); t (49) = -5.55, p <.001, d = .-79).  

                There were eight names of people going to the party for the participants to recall (while ignoring the names of people who were not going to the party) and eight names of places to recall. Any other names recalled were scored as a false alarm, and 1 point for every false alarm was deducted from their recall score for that category. This is the same approach used by Andrade (2010) to ensure that participants were not just recalling names of people who were mentioned in the voicemail but not as one of the people going to the party, or were not just generating random names. The average numbers of correct-recalls and false alarms for names and places, as well as the resulting memory scores are shown in Table 1 for the doodle and control groups. As can be seen in the table, and as detailed below, memory performance was no better for the participants who doodled than for those who did not. 

Table 1. Means and standard deviations for correct recall, false alarms and overall memory scores for names and places in the control and doodle groups. ( n = 25 per group)

Submission of the correct-recall rates to a 2 (Group: doodling, control) x 2 (Stimulus-type: names, places) mixed-factors ANOVA, for example, revealed that there was no significant difference in the overall correct-recall performance of the doodlers compared to that of the control group (Group main-effect:  F (1,48) = 1.47, p = .23, η p 2 = .03). Although significantly more party-goer names were recalled than places, overall, (Stimulus-type main effect: F (1,48) = 59.07, p <.001, η p 2 = .55), the magnitude of this effect did not significantly depend on whether participants were in the doodle group or the control group (Group-by-Stimulus-type interaction: F (1,48) = .34, p =.56, η p 2 = .007).

Submission of false-alarm numbers to a separate 2 (Group: doodling, control) x 2 (Stimulus-type: names, places) mixed-factors ANOVA, likewise revealed that there was no significant difference in the average numbers of false reports by the doodlers compared to that of the control group (Group main-effect: F (1,48) = .25, p = .62, η p 2 = .005). Although significantly more party-goer names were incorrectly reported than places, overall, (Stimulus-type main effect: F (1,48) = 12.23, p =.001, η p 2 = .20), the magnitude of this effect did not significantly depend on whether participants were in the doodle group or the control group (Group-by-Stimulus-type interaction: F (1,48) = .40, p =.53, η p 2 = .008).

After subtracting false alarms from correct-recalls for reported party-goer names and places, the resulting total memory scores were submitted to another 2 (Group: doodling, control) x 2 (Stimulus-type: names, places) mixed-factors ANOVA. This revealed that the average total-memory performance for the doodle group was not significantly different from that of the control group (Group main-effect: F (1,48) = .55, p = .46, η p 2  = .01). Although total-memory scores were again significantly higher for party-goer names than for places, overall, the magnitude of this effect did not significantly depend on whether participants were in the doodle group or the control group (Group-by-Stimulus-type interaction: F (1,48) = .52, p =.48, η p 2 = .01.).

                The fidgeting-reduces-boredom-and-increases-attention hypothesis and the results reported by Andrade (2010) predicted that participants in the doodle condition of Experiment 1 should perform better on the subsequent memory test than those in the control condition. They did not. While the addition of doodling did not significantly impair performance on the subsequent memory test when compared to that of the control condition, the nominally-lower memory performance for those in the doodle condition than those in the control condition is more in line with the fidgeting-reflects-inattention hypothesis, which predicted that the addition of doodling would—if anything— reduce attention to the voicemail when compared to that of the control condition.

Previous studies have suggested that doodling may be a helpful intervention strategy in a boring situation by distracting the individual away from their boredom and thereby increasing the ability to recall information (Andrade, 2010). However, our results suggest that this may not be accurate, as there was no benefit of doodling (versus not doodling) for subsequent memory performance under conditions in which participants were shown to be experiencing elevated levels of boredom. Our failure to replicate Andrade’s (2010) finding that doodling aids recall ability for voicemail material may be due to the possibility that their participants were not experiencing boredom. To the extent that elevated boredom is associated with difficulties with task-focused attention, the potential for low levels of boredom in Andrade’s (2010) study could mean that their participants had available attentional resources to be better able to engage with the material while doodling than our participants, who may have had boredom-related difficulties with attentional engagement. If this is true, it may indicate that doodling is not effective at offsetting boredom or its negative consequences for learning.

                However, while we took steps to ensure there were elevated levels of boredom for participants going into the voicemail task in Experiment 1, this study alone cannot explain exactly how boredom may have played a role in determining the observed results. In a typical learning environment, boredom tends to occur during the actual task not prior. Additionally, most educational settings require task-focused attention for much longer than two-and-a-half minutes. Thus, the length of the task we adapted from Andrade (2010) is far too short to be reflective of a real-life scenario, and may therefore not have been long enough to provide an opportunity for doodling to be fully effective in offsetting boredom-related disengagement with the material. It is also impossible to know whether doodling could have been functional for offsetting boredom or any of its negative correlates in Experiment 1— beyond subsequent memory performance—  because there was no measure of task-focused attention or off-task mind-wandering. We address this in Experiment 2 by manipulating doodling behaviour during a longer lecture-listening task in which we measured in-task levels of attention, mind-wandering and boredom, as well as subsequent retention of the lecture material.

Experiment 2

Experiment 2 builds on Experiment 1 by assessing the impact of doodling on boredom, mind-wandering, attention and retention in a more ecologically-valid lecture-listening task. The importance of including in-task measures of mind-wandering and attention is highlighted by the findings of previous research showing both links between mind-wandering and fidgeting behaviour (Carriere, Seli & Smilek, 2013), and how our body also becomes restless as our mind becomes restless (Seli et al., 2014). However, while these prior studies make clear a connection between fidgeting behaviours and difficulties maintaining task-focused attention, they did not include measures of boredom or associated memory performance, nor did they manipulate fidgeting behaviours to directly assess their impact on mind-wandering or attention. Experiment 2 therefore also extends this prior work by manipulating different types of fidgeting behaviours to directly assess their impact on boredom, mind-wandering and attention during a lecture-listening task, as well as the associated effects on retention of lecture material. 

 Our decision to include a manipulation of different types of fidgeting behaviours— the same ‘shade in shapes’ structured form of doodling used in Experiment 1, an ‘anything goes’ unstructured form of doodling, a note-taking condition, and a ‘listen-only’ control condition—was inspired by Boggs et al. (2017) who used these same conditions. Unstructured doodling more closely represents the type of doodling used in real-world settings, while note-taking represents another activity frequently used in real-world settings. Participants in Boggs et al.’s study were randomly assigned to one of these four conditions while they listened to a 5-minute fictional conversation between two friends, after which they completed a quiz that tested their ability to recall information from that conversation. Boggs et al. found that unstructured doodling while listening led to significantly worse recollection of the conversation content than the structured doodling or note-taking. They interpreted this learning impairment as being due to the additional attentional demands during unstructured doodling of having to decide what to doodle, which may have thereby reduced participants capacity to encode details of the conversation. However, the authors urged caution regarding this interpretation because they did not include a measure of attention in their study. Experiment 2 therefore also extends this prior work by using the same doodling conditions as Boggs et al. along with in-task measures that allow a direct assessment of their impact on attention, mind-wandering, and boredom during a lecture-listening task, as well as the associated effects on retention of lecture material. 

The fidgeting-reduces-boredom-and-increases-attention hypothesis predicts that participants in both the structured-doodling or unstructured-doodling conditions should show lower levels of in-task boredom and mind-wandering, and higher levels of in-task attention and subsequent memory for lecture content, than those in the control condition. In contrast, the fidgeting-reflects-inattention hypothesis predicts that the addition of either doodling condition would reduce attention to the lecture, increase in-task boredom and mind-wandering and—if anything—impair performance on the subsequent memory test when compared to that of the control condition. Further, consider Boggs et al.’s (2017) speculation that unstructured doodling places additional demands on attention, due to the extra thought and effort required to generate doodle content, relative to structured doodling. If this is correct, then the unstructured-doodling condition in our experiment should also show reduced in-task attention, more mind-wandering, and worse memory for lecture content relative to the structured doodling condition. In terms of in-task boredom, however, it may be possible that unstructured doodling is relatively interesting and engaging when compared to the experience of structured doodling or passive listening. If so, it might produce lower levels of in-task boredom and mind-wandering than these other conditions, regardless of whether it results in less task-focused attention or relatively poor memory for lecture content. 

As for the note-taking condition, writing down information as you hear it may aid the encoding of that information. This could explain both Boggs et al.’s (2017) finding that note-taking leads to better memory performance than passive listening or unstructured doodling, and Meade, Wammes & Fernandez’s (2019) finding that writing down words as they were heard also resulted in better memory for those words than for words that were heard during unstructured doodling. However, the extent to which any difference in memory performance for note-taking compared to the other conditions is associated with differences in boredom, mind-wandering or attention remains unclear as these prior studies did not include measures of these factors.

Participants in Experiment 2 completed several questionnaires to allow us to examine whether there is a relation between an individual’s tendency to doodle (DSAQ: Doodle Spontaneous Activity Questionnaire; developed for this study based on Carriere et al., 2013), their tendency to fidget (SAQ: Spontaneous Activity Questionnaire; Carriere et al., 2013), and the extent to which they routinely experience boredom (BPS: Boredom Proneness Scale; Farmer & Sundberg, 1986) or attention-related difficulties in terms of mind-wandering (MWQ: Mind-Wandering Questionnaire; Mrazek, Phillips, Franklin & Broadway, 2013), attention-related cognitive errors (ARCES: Attention-Related Cognitive Errors; Carriere, Cheyne & Smilek, 2008), or lapses in mindful attention (MAAS-LO: Mindful Attention Awareness Scale-Lapses Only; Carriere et al., 2008, cf. Brown & Ryan, 2003). Of the potential relations between individual-difference measures, we were particularly interested in the extent to which those who self-report being high in fidgeting behaviour (SAQ), also report being high in doodling behaviour (DSAQ).  If doodling is just a specific form of the more general category of fidgeting, then DSAQ scores and SAQ scores should be positively correlated.

Obtaining a measure of trait mind-wandering (MWQ) for each participant, along with a measure of self-reported doodling frequency (DSAQ) was intended to help test our competing hypotheses and thereby address the discrepancies between prior work implying that doodlers mind-wander less (Andrade, 2010) and subsequent results suggesting doodlers mind-wander more (Boggs et al., 2017). Specifically, the fidgeting-reduces-boredom-and-increases-attention hypothesis predicts negative correlations between the self-reported tendency to doodle (DSAQ) and measures of boredom proneness (BPS), the tendency to experience attentional failures (ARCES) and lapses in attention (MAAS-LO). In contrast, the fidgeting-reflects-inattention hypothesis predicts positive correlations between DSAQ scores and both ARCES scores and MAAS-LO scores. The questionnaires we included to look at individual differences in self-reported doodling, fidgeting, attentional lapses/cognitive errors, boredom and mind-wandering also allowed us to assess whether there are any particular subsets of individuals for whom fidgeting/doodling may be especially effective, as has previously been suggested (e.g., those who routinely experience attention-related difficulties; Kercood & Banda, 2012; Rotz & Wright, 2005; Zentall, 1975). Including these measures also allows an assessment of the speculation by Boggs et al. (2017) that doodling might have differing cognitive impacts based on whether a given individual already has a tendency to doodle. 

Participants

                Participants consisted of 172 (43 per group, age: M = 19.08, SD = 2.46; self-reported gender: female = 145, male = 27) undergraduate students at the University of Guelph. They were recruited using the Department of Psychology participant pool and received course credit for their participation. The University of Guelph Research Ethics Board approved this study (REB protocol #16-12-398).

Materials: Mass testing

Spontaneous Activity Questionnaire. (SAQ; Carriere et al., 2013). The SAQ was completed by participants during mass testing to get a measure of an individual overall tendency to fidget. The questionnaire has a total of 8 questions with excellent internal consistency (α = 0.94). Examples of the SAQ questions are “I fidget while I am in deep thought” and “I fidget when I am worried about something”. Answers for the SAQ range from 1 (never) to 7 (always), with higher scores (max score is 56) indicating an individual have a greater tendency to fidget.   

Boredom Proneness Scale. (BPS; Farmer & Sundberg, 1986). The BPS was completed during mass testing to measure an individual’s propensity to experience boredom. Unlike the other boredom questionnaires that look at boredom at that specific time, the BPS looks at how often the individual experiences boredom in his/her daily life. The BPS includes 28 items with questions such as “I find it easy to entertain myself” and “It takes me more stimulation to get me going than most people”. The BPS has acceptable reliability (α   = .79). Participants may answer True (1 point), False (0 points) or not answer at all (Note: items 1, 7, 8, 11, 13, 15, 18, 22, 23 and 24 are reversed scored). The higher the sum of the responses (max score is 28) is indicative that the individual has a higher trait tendency to experience boredom. 

Mind-wandering Questionnaire.  ( MWQ; Mrazek, Phillips, Franklin & Broadway, 2013). The MWQ is completed during mass testing to measure an individual’s overall tendency to mind-wander. The questionnaire consists of 5 items with good internal consistency (α   = .85). Responses range from 1 (almost never) to 6 (almost always). Examples of the questions included in the MWQ are “I do things without paying full attention” and “I mind-wander during lectures or presentations. Higher scores (max score of 30) indicate a higher propensity to mind-wander. Previous research has shown that there is a relation the tendency to experience boredom measured by the BPS and instances of mind-wandering, suggesting that higher boredom proneness is related to more off-task thought (Isacescu, Struk & Danckert, 2017).

Materials: In-session

                 Recorded lecture. Participants listened to the first 45-minutes of the recorded lecture titled “The Dark Ages” from an introductory Ancient Greek History course (same as Experiment 1). 

                 Thought probes. To obtain in-task measures of state boredom, mind-wandering and task-focused attention, we presented thought probes at four different points throughout the recorded lecture (9, 18, 27, and 36 minutes). For each probe, the lecture audio would stop playing and participants would be asked to use a Likert-scale to rate their level of boredom (“How bored were you prior to the probe?”: 1 = “Not at all bored” to 7 = “Extremely bored”), mind-wandering (“Where was your attention focused just before the probe?”: 1 = “Not at all on task” to 7 = “Completely on task”), and attention (How much attention were you paying prior to the probe?”: 1 = “Not paying attention at all” to 7 = “Full attention”). 

                 Multidimensional State Boredom Scale-8  (MSBS-8 ). Participants indicated the level of boredom they were experiencing both before and after listening to the recorded lecture by completing the MSBS-8 (Hunter, Dryer, Cribbie & Eastwood, 2015) using Qualtrics online-survey software (same as experiment 1). 

                 Mindful Attention Awareness Scale – Lapses Only (MAAS-LO). To assess individual differences in participants propensity to experience attentional lapses, participants were asked to complete the 12-question MAAS-LO (Carriere et al., 2008), which is a version of the Mindful Attention Awareness Scale originally developed by Brown & Ryan (2003) that was modified by Carriere et al. to focus exclusively on attentional lapses. The MAAS-LO has good reliability (α   = 0.83) as shown in a variety of studies (e.g., Carriere, Seli & Smilek, 2013; Cheyne, Carriere, Smilek, 2006; Carriere, Cheyne & Smilek, 2008). Examples of the MAAS-LO questions include “I find myself doing things without paying attention” and “I rush through activities without really being attentive to them”. Responses are made using a Likert-scale ranging from 1 (almost never) to 6 (always). The sum of the responses provides a score that indexes attentional lapses, with higher scores indicating greater tendencies to experience attentional lapses (max score of 72). Previous research has shown that the tendency to fidget measured by the SAQ is positively correlated with the attentional lapses measured by the MAAS-LO (Carriere et al., 2013). Moreover, the tendency to routinely experience boredom measure by the BPS has been shown to positively correlate with the attentional lapses measured by the MAAS-LO (Carriere, Cheyne & Smilek, 2008), such that higher instances of boredom are related to increased attentional lapses. Participants completed the MAAS-LO via Qualtrics online survey software immediately after the lecture-listening task.  

                 Attention-Related Cognitive Errors Scale. (ARCES; Cheyne, Carriere & Smilek, 2006). To assess individual differences in participants propensity to experience cognitive failures due to attention lapses, participants were asked to complete the 12-question ARCES. Examples of the ARCES questions are “I have gone to the fridge to get one thing (e.g., milk) and taken something else (e.g., juice)” and “I have lost track of a conversation because I zoned out when someone else was talking”. Responses are made using a Likert-scale ranging from 1 (never) to 5 (very often). The sum of the responses provides a score that indexes the cognitive consequences of attentional lapses, with higher scores indicating greater tendencies to experience attention-related cognitive errors (max score of 60). ARCES scores have been shown to be positively correlated with MAAS-LO scores, suggesting that there is a relation between the increased lapses in attention measured by the MAAS-LO and the increased cognitive consequences of such lapses measured by the ARCES (Carriere, Seli & Smilek, 2013; Cheyne, Carriere, Smilek, 2006; Carriere, Cheyne & Smilek, 2008). Previous research has shown that the tendency to fidget measured by the SAQ is positively correlated with the cognitive consequences of attentional lapses measured by the ARCES (Carriere et al., 2013). Moreover, the tendency to routinely experience boredom measure by the BPS has been shown to positively correlate with the cognitive consequences of attentional lapses measured by the ARCES (Carriere, Cheyne & Smilek, 2008), such that higher instances of boredom are related to increased attention-related cognitive errors. Participants completed the ARCES via Qualtrics online survey software immediately after the lecture-listening task.

                 Doodle Spontaneous Activity Questionnaire.  (DSAQ) . To assess individual differences in participants propensity to doodle, we modified the SAQ (Carriere et al., 2013) to focus specifically on doodling behaviour. We simply changed each instance of “I fidget” in the questions to “I doodle”. Examples of the modified DSAQ questions are “I doodle while I am in deep thought” and “I doodle when I am worried about something”. Responses are made using a Likert-scale ranging from 1 (never) to 7 (always). The sum of the responses provides a score that indexes the propensity to engage in doodling, with higher scores indicating greater tendencies to doodle (max score is 49). If doodling is just a specific form of the more general category of fidgeting, then DSAQ scores and SAQ scores should be positively correlated.

                 Retention questions. Participants were asked to complete a set of 28 multiple-choice questions to measure their retention of lecture material. The questions asked had to do with specific lecture content (i.e., “Where do we see civilization for the first time in the Aegean Sea area?”. 

Participants were required to complete an online mass-testing series of questionnaires to be eligible for participation.   Once in the lab, informed consent was obtained in writing. The participant was then asked to complete a demographic survey and the MSBS-8 using Qualtrics online survey software. Participants were told they would be listening to the audio of a university lecture through headphones and that the headphones were not to be removed. They were also told that questions would occasionally appear on the screen during the lecture audio that would require them to read the question and make an honest response using the numbers on the keyboard. Participants were informed that there would be a memory test at the end of the lecture, so they should pay close attention to the material. At this point, they were given instructions based on their randomly assigned condition and then the audio was started.

 The control condition was not given any secondary task to do while listening. The note-takers were instructed to take notes while listening to the lecture anytime they were not answering questions on the screen (thought probes). Participants were given five sheets of blank white paper with two pencils and a pen to use. The structured doodlers were instructed to shade in shapes while listening to the lecture anytime they were not answering questions on the screen (thought probes). They were not to do anything else or write notes. Participants were given five sheets of paper with alternating shapes on them, with two pencils and a pen to use. Lastly, the unstructured doodlers were instructed to doodle while listening to the lecture anytime they were not answering questions on the screen (thought probes). Participants were given five sheets of blank white paper with two pencils and a pen and told they could doodle anything, except lecture material or write notes. to use. Upon completion, the participants completed the MSBS-8, DSAQ, MAAS-LO, ARCES and multiple-choice retention test via Qualtrics. Participants were then debriefed on the purpose of the study and thanked for their participation.   

Pre-experiment levels of boredom 

                To confirm that there were no pre-existing differences in the level of boredom being experienced by our different experimental groups, we submitted the summed pre-task MSBS-8 scores to a one-way ANOVA with Group (Listen-only, Note-taking, Structured-doodle, and Unstructured-doodle) as the between-subjects factor. The analysis confirmed that there were no significant group differences in levels of state boredom prior to the experiment, F (3,168) = 0.27, p  = 0.85, η 2 = 0.0048. 

In-task measures of boredom, mind-wandering, and attention 

                To assess the effects of the different types of fidgeting behaviours on in-task levels of boredom, mind-wandering, and attention throughout the lecture-listening task, we conducted a separate 4 (Group: Listen-only, Note-taking, Structured-doodle, and Unstructured-doodle) x 4 (Time: 9, 18, 27, and 36 minutes) mixed-factors ANOVA for each of these measures. As detailed below, there was no benefit on any of these measures for either type of doodling, relative to the listen-only control. There was, however, a distinct advantage for the note-takers relative to all other groups that was specific to levels of mind-wandering and task-focused attention.

                 Boredom. As shown in Figure 1, self-reported state boredom increased over time for all groups. This main effect of Time was significant, F (3, 504) = 22.07, p < .001, η p 2 = .12. Figure 1 shows that the overall levels of boredom over time were similar for the different groups, which was reflected in the lack of a significant main effect of Group, F (3,168) = .94, p = .42, η p 2 = .02, and the lack of a significant Time-by-Group interaction,  F (9, 504) = 1.24, p  = .27, η p 2 = .02. As a further test of the possibility that different fidgeting behaviours might differentially influence the experience of boredom, we submitted the post-task MSBS-8 scores to a one-way ANOVA with Group (Listen-only, Note-taking, Structured-doodle, and Unstructured-doodle) as the between-subjects factor. This confirmed that there were also no significant group differences in levels of state boredom after the listening task, F (3,168) = 1.03, p  = 0.38, η 2 = 0.018. 

Mind-wandering. As shown in Figure 1, self-reported levels of mind-wandering increased over time for all groups. This main effect of Time was significant, F (3,504) = 36.94, p <.001, η p 2 = .18. However, Figure 1 also shows that the overall levels of mind-wandering over time were noticeably lower for the Note-taking group than for the other groups, which was reflected in a significant main effect of Group, F (3,97) = 11.49, p < .001, η p 2 = .17 .  Post hoc tests using the Bonferroni correction revealed that the Note-taker group mind-wandered significantly less than the Listen-only control group,  ( t  = -4.96, p < .001, d = -0.83), Structured-doodle group ( t  = -4.60, p < .001, d = -0.77), and Unstructured-doodle group ( t  = -4.79, p < .001, d = -0.81), none of which significantly differed from each other (for each comparison, t  < 0.37, p > .99,   d < 0.06). Moreover, the extent to which note-taking reduced levels of mind-wandering relative to the other conditions did not change over time, as reflected by the lack of a significant Time-by-Group interaction, F (9,504) = .55, p = .84, η p 2 = .02.

Attention. Whereas boredom and mind-wandering increased over time, Figure 1 shows that the self-perceived amount of attention paid to the lecture-listening task decreased over time. This main effect of Time was significant,  F (3, 504) = 37.81, p <.001, η p 2 = .18. Moreover, as with levels of mind-wandering, Figure 1 also shows that the levels of attention paid over time were noticeably different for the Note-taking group than for the other groups, which was reflected in a significant main effect of Group, F (3,168) = 6.09 p = .001, η p 2 = .10.   Post hoc tests using the Bonferroni correction revealed that the Note-taker group reported paying significantly more attention than the Listen-only control group,   ( t  = 3.20, p < .001, d = 0.55), Structured-doodle group ( t  = 2.95, p = .022, d = 0.51), and Unstructured-doodle group ( t  = 3.98, p < .001, d = 0.68), none of which significantly differed from each other (for each comparison, t  < 1.04, p > .99,   d < 0.18). Moreover, the extent to which note-taking increased levels of attention relative to the other conditions did not change over time, as reflected by the lack of a significant Time-by-Group interaction, F (9,504) = 1.24, p = .27, η p 2 = .02.

The notion that fidget behaviours might reduce boredom and mind-wandering, and thereby boost task-focused attention and aid learning (or, conversely that fidget behaviours are an index of a lack of engagement and may thereby be linked to less task-focused attention and impaired learning), is based on the possibility that higher levels of boredom and mind-wandering and lower levels of attention all lead to impairments in the ability to encode and retain information encountered during a learning experience. To test this, we examined the extent to which levels of state boredom, mind-wandering, and task-focused attention were correlated, overall, with subsequent performance on the multiple-choice test of memory. This showed that the number of correct memory-test answers was indeed negatively correlated with overall levels of in-task boredom (average of the four ratings), r = -0.21, p = .006, post-task boredom (MSBS-8 score obtained after the lecture), r = -0.28, p < .001, and overall levels of mind-wandering (average of the four in-task ratings, r = -0.36, p < .001), while memory-test performance was positively correlated with levels of self-perceived attention paid to the lecture-listening task (average of the four in-task ratings, r = 0.34, p < .001). This is consistent with prior evidence that boredom and its corresponding difficulties with attentional engagement have clear negative consequences for academic outcomes (Pekrun, Hall, Goetz & Perry, 2014; Fitea & Fritea, 2013; Tze, Daniels & Klassen, 2016). It also underscores the potential value for learning contexts of any fidgeting-based intervention that may be effective in reducing boredom and mind-wandering, and increasing task-focused attention.  

Table 2. Average retention score (number correct answers on the 28-question multiple-choice test of memory for lecture content) for the Listen-only, Structured-doodle, Unstructured-doodle, and Note-taking groups ( n = 43 per group).

As shown in Table 2, while there was little difference among the average numbers of correct multiple-choice answers for the Structured-doodle, Unstructured-doodle and Listen-only control groups, the memory performance of the Note-taking group was notably higher. A one-way ANOVA with Group (Listen-only, Structured-doodle, Unstructured-doodle, and Note-taking) as the between-subjects factor confirmed that this main effect of Group was significant, F (3,168) = 9.55, p < .001, η p 2 = .15. Post hoc tests using the Bonferroni correction revealed that the Note-taker group remembered significantly more of the lecture content than the Listen-only control group, ( t  = 3.47, p < .001, d = 0.75), the Structured-doodle group ( t  = 4.42, p < .001, d = 0.95), and the Unstructured-doodle group ( t  = 4.79, p < .001, d = 1.03). There were no significant differences among the Structured-doodle, Unstructured-doodle and Listen-only control groups (for each comparison, t  < 1.32, p > .56,   d < 0.29). In other words, neither type of doodling led to any better retention of the lecture content than passively listening. 

Individual differences

Descriptive statistics for all of our individual difference measures are shown in Table 3. We analyzed our individual-difference measures to assess whether there are any particular subsets of individuals for whom fidgeting/doodling may be especially effective and to test our competing hypotheses regarding the cognitive-affective correlates of self-reported tendencies to doodle and fidget.

Table 3. Descriptive statistics (SD = Standard deviation) for all individual-difference measures, including trait mind-wandering (MWQ), tendency to experience attentional lapses (MAAS-LO) and attention-related cognitive errors (ARCES), boredom proneness (BPS), doodling behaviour (DSAQ), and fidgeting behaviour (SAQ). 

 Participant subsets . Perhaps our failure to observe an overall benefit of doodling for mid-task levels of boredom, mind-wandering, and task-focused attention, or for subsequent memory performance is due to the possibility that doodling and other forms of fidgeting only have cognitive-affective benefits for certain types of people. Fidgeting and doodling have been purported, for example, to be particularly helpful for individuals with attention-related difficulties (e.g., Kercood and Banda, 2012; Rotz & Wright, 2005).  The possibility that fidgeting/doodling may help to reduce boredom suggests that such behaviours might also be of particular value to individuals who are prone to experience boredom, or to those who, through experience, have come to routinely engage in fidgeting or doodling. We therefore identified the subsets of participants who had scores higher than the median of all participants on the individual-difference measures of mind-wandering (MWQ), attentional lapses (MAAS-LO), attention-related cognitive errors (ARCES), boredom proneness (BPS), doodling behaviours (DSAQ), and fidgeting behaviours (SAQ). For each of these participant-subsets, we then conducted separate 3 (Group: Listen-only, Structured-doodle, and Unstructured-doodle) x 4 (Time: 9, 18, 27, and 36 minutes) mixed-factors ANOVAs to assess the effect of the different types of doodling behaviours on in-task levels of boredom, mind-wandering, and attention. Note that, to focus solely on the potential benefits of doodling, per se , we omitted all note-taking participants from these additional analyses. The effect of doodling condition on subsequent retention of lecture content was also assessed for each subset of participants using a one-way ANOVA with Group (Listen-only, Structured-doodle, and Unstructured-doodle) as the between-subjects factor. The results for the main effect of Group from these ANOVAs are shown in Table 4. 

Table 4. Average mid-task ratings of Boredom, Attention, Mind-wandering, and subsequent Retention scores (# correct out of 28) for the subsets of participants with scores that were higher than median of all participants in their tendencies to experience Mind-wandering, Attentional lapses, Attention-related cognitive errors, Boredom, Doodling, and Fidgeting. F -values and η 2 p -values are reported for the main effect of Group (Listen-only control, Structured-doodle, Unstructured-doodle) for each measure obtained from each participant subset. ** p < .01

                As can be seen in Table 4, there was a significant main effect of doodling condition on the mid-task level of boredom experienced by the subset of participant who self-reported high levels of attention-related cognitive errors (ARCES). Inspection of the corresponding means and confidence intervals shows that boredom was lower for these high-ARCES participants that engaged in structured doodling, compared to those who engaged in unstructured doodling or just passively listened. The high-ARCES participants that engaged in structured doodling also reported nominally lower levels of mind-wandering and nominally higher levels of self-perceived attention, compared to those who engaged in unstructured doodling or just passively listened, although they also had nominally lower retention scores than either of these other conditions. None of the other measures for any of the other subsets of participants showed a significant effect of doodling condition. Thus, the lower level of mid-task boredom for high-ARCES participants is the only indication from our additional individual-difference analyses that any type of doodling could have any type of beneficial effect. To the extent that structured doodling was effective in reducing mid-task boredom for this specific subset of participants, it is noteworthy that this was not accompanied by a corresponding benefit for learning/remembering lecture content.

Individual differences: Cognitive-affective correlates of fidgeting/doodling  

                To test our competing hypotheses regarding the cognitive-affective correlates of self-reported tendencies to doodle and fidget, we calculated the correlation between each of our individual-difference measures, including trait mind-wandering (MWQ), attentional lapses (MAAS-LO), attention-related cognitive errors (ARCES), boredom proneness (BPS), doodling behaviour (DSAQ), and fidgeting behaviour (SAQ), as well as between our self-reported measures of cognitive-affective states, including boredom before the listening tasks (pre-task MSBS1), during the task (average of Boredom ratings obtained at each of the four timepoints) and after the task (post-task MSBS1), self-perceived levels of attention (average of Attention ratings obtained at each of the four timepoints), mind-wandering (average of Mind-wandering ratings obtained at each of the four timepoints), plus the score on the subsequent lecture-retention exam. These are reported in Table 5.

                As shown in Table 5, our measures of individual difference in the tendency to doodle (DSAQ) and the tendency to fidget (SAQ) were positively correlated, which is consistent with the view that doodling is a specific form of the more general category of fidgeting. However, the fact that these measures were only moderately correlated makes clear that these measures do not merely tap into the exact same behavioural traits. Indeed, whereas the tendency to fidget was significantly positively correlated with the tendency to mind-wander, the tendency to doodle was not.

Table 5. Pearson Product-Moment Correlations for all individual-difference measures, including trait mind-wandering (MWQ), attentional lapses (MAAS-LO), attention-related cognitive errors (ARCES), boredom proneness (BPS), doodling behaviour (DSAQ), and fidgeting behaviour (SAQ), as well as self-reported cognitive-affective states, including boredom before the listening tasks (pre-task MSBS1), during the task (average of Boredom ratings obtained at each of the four timepoints) and after the task (post-task MSBS1), self-perceived levels of attention (average of Attention ratings obtained at each of the four timepoints), mind-wandering (average of Mind-wandering ratings obtained at each of the four timepoints), plus the score on the subsequent lecture-retention exam. 

                            * p < .05, ** p < .01, *** p < .001

The fidgeting-reduces-boredom-and-increases-attention hypothesis predicts negative correlations between the self-reported tendency to doodle (DSAQ) and measures of boredom proneness (BPS), the tendency to experience lapses in attention (MAAS-LO) and the cognitive consequences of attentional errors (ARCES). In contrast, we found that DSAQ was not correlated with BPS, but was moderately-positively correlated with both MAAS-LO and ARCES. Inspection of Table 5 also shows that DSAQ scores were positively correlated with the levels of state boredom reported both before (pre-task MSBS1) and after (post-task MSBS2) the lecture-listening task. These significant positive correlations between the tendency to doodle, levels of state boredom, and tendencies to experience attentional lapses/errors is therefore more consistent with the fidgeting-reflects-inattention hypothesis. Likewise, we observed that the tendency to fidget (SAQ) was also positively correlated with both MAAS-LO and ARCES (as well as MWQ and the levels of state boredom reported both before and after the lecture-listening task) and negatively correlated with the average level self-perceived attention paid to the listening task, providing added converging support for the fidgeting-reflects-inattention hypothesis. These results are not consistent with the fidgeting-reduces-boredom-and-increases-attention hypothesis.

                It is worth noting that we observed a positive correlation between ARCES and MAAS-LO, thus providing a replication of their relationship (e.g., Carriere, Seli & Smilek, 2013; Cheyne, Carriere, Smilek, 2006; Carriere, Cheyne & Smilek, 2008). Additionally, the results show a positive relationship between the BPS and the ACRES and MAAS-LO measures of attentional difficulties as found in previous research  (e.g., Carriere, Cheyne & Smilek, 2008).

Discussion 

            Experiment 2 aimed to further examine the competing fidgeting-related hypotheses. Recall that the fidgeting-reduces-boredom-and-increases-attention hypothesis predicts that participants in both the structured-doodling or unstructured-doodling conditions should show lower levels of in-task boredom and mind-wandering, and higher levels of in-task attention and subsequent memory for lecture content, than those in the control condition. However, the results failed to support this hypothesis. In terms of boredom, group allocation made no difference to boredom scores at any time point. In fact, boredom increased significantly over time for all groups, importantly including those who doodled.  Similar results were seen for mind-wandering and attention self-reports over time, such that all groups reported increased mind-wandering and decreased attention as the lecture progressed. On top of that, the memory performance of doodlers was nominally worse than those who did nothing and significantly worse than those who took notes.  In terms of individual differences, this hypothesis would predict negative correlations between the self-reported tendency to doodle (DSAQ) and measures of boredom proneness (BPS), the tendency to experience attentional failures (ARCES) and lapses in attention (MAAS-LO). Yet, upon examination, this was not the case. The tendency to doodle was not associated with boredom proneness and was positively associated with attentional failures/lapses. 

                The counter hypothesis suggesting that fidgeting-reflects-inattention predicts that either doodling condition would reduce attention to the lecture, increase in-task boredom and mind-wandering and impair performance on the subsequent memory test when compared to that of the control condition. Specifically, those who are in the unstructured doodle should show reduced in-task attention, more mind-wandering, and worse memory for lecture content relative to the structured doodling condition.  The results did not support this hypothesis either. Although attention did decrease, and boredom and mind-wandering increased for those in the doodle conditions, these results did not significantly differ from those who were in the control condition. Beyond that, the memory test findings did not support this hypothesis as scores were slightly worse than the control, but not nearly large enough to be a significant difference. For individual differences, the hypothesis would suggest positive correlations between DSAQ scores and both ARCES scores and MAAS-LO scores. As stated previously, DSAQ was in fact positively correlated with attentional lapses and failures. The individual difference measures suggest that a higher tendency to doodle is associated with a higher tendency to experience lapses and/or failures in attention. As well as a higher tendency to doodle is related to higher state boredom. Thus, by examining individuals’ own tendency to doodle outside of a forced condition, we are better able to see how it relates to attentional engagement and state boredom.  

General Discussion

While traditionally viewed in educational contexts as markers of inattention and poor classroom behaviour doodling and fidgeting have more recently been considered as possible routes to improve performance. Across two experiments, we directly tested the competing hypotheses about the extent to which different methods of doodling may indeed be helpful for cognitive functioning: the ‘fidgeting reduces boredom and increases attention’ hypothesis (e.g., Andrade, 2010 ), which posits that doodling is a beneficial form of fidgeting that can reduce boredom and increase attention to promote better learning, and the ‘fidgeting reflects inattention’ hypothesis, which maintains that doodling is merely an indication of the mind taking a mental break, thereby reflecting the absence of task-focused attention (mind-wandering; Boggs et al., 2017 ) and is therefore linked to relatively poor learning. Across our studies, doodling neither reduced boredom or mind-wandering nor increased attention or retention of information when compared to conditions without doodling.

In Experiment 1 we sought to replicate and extend Andrade’s ( 2010 ) observation that doodling can reduce feelings of boredom and levels of mind-wandering while improving task-focused attention and memory for associated information. Instead, we found no evidence that doodling was any better than solely listening when it came to remembering task-relevant information. Indeed, participants who doodled did nominally worse on the memory assessment. The main difference between our study and Andrade’s study is that we used a boredom-induction procedure to ensure our participants were experience significantly elevated levels of state boredom prior to the listening task that contained the doodling manipulation. Thus, a potential reason why our results differ from those of her study could be that Andrade’s participants were not experiencing the same levels of boredom prior to the task and thus they did not have the same difficulty staying engaged in the task in the first place. Boredom is thought to be a pervasive affective state that arises when we want to, but are unable to, engage attention in a satisfying activity (Eastwood et al., 2012 ). A study conducted by Sinclair et al. ( 2018 ) looked at positive and negative emotions across time during a computer-based learning program. The results from their study suggest that of all the emotions measured (e.g., boredom, frustration, pride, etc.) boredom was the only state that caused concern for students because of the relatively small chance of being able to escape from that emotion. Thus, it could also be that a more effective way to alleviate and prevent boredom would be to change the task altogether, rather than to add doodling.

In Experiment 2, we sought to further contrast the fidgeting-reduces-boredom-and-increases-attention hypothesis against the fidgeting-reflects-inattention hypothesis using a more ecologically valid task in which we manipulated different types of fidgeting behaviours to directly assess their impact on boredom, mind-wandering and attention during a lecture-listening task, as well as the associated effects on retention of lecture material. We found that doodling neither reduced boredom or mind-wandering nor increased attention or retention of information compared to other conditions. In contrast, attention and test performance were highest (and boredom and mind-wandering lowest) for those focused solely on note-taking. Moreover, our inclusion of self-report measures of the tendency to doodle, fidget, experience boredom and attentional lapses and failures revealed that higher levels of self-reported doodling behaviours were associated with higher levels of attentional lapses, attentional failures, and state boredom, which resonates more with the fidgeting-reflects-inattention hypothesis than the fidgeting-reduces-boredom-and-increases-attention hypothesis.

We formally considered the possibility that our failure to observe an overall benefit of doodling for mid-task levels of boredom, mind-wandering, and task-focused attention, or for subsequent memory performance in Experiment 2 was due to doodling and other forms of fidgeting only having cognitive-affective benefits for certain types of people, such as those with attention-related difficulties, those prone to experience boredom, or those who routinely engage in fidgeting or doodling. We found no evidence that doodling reduced boredom or mind-wandering, or increased task-focused attention or subsequent memory performance for individuals who are relatively high in routinely experiencing attention-related difficulties, including mind-wandering or attentional lapses, or who are relatively high in boredom proneness. Doodling also provided no observable benefits for those who are relatively high in the extent to which they routinely engage in doodling or fidgeting behaviours. Indeed, the only group for whom we found any benefit of doodling was for participants with relatively high scores on the attention-related cognitive errors scale: participants in this subset that engaged in structured doodling experienced less boredom than those that engaged in unstructured doodling or who solely listened to the lecture. The doodling-related benefit for this participants subset, however, did not lead to better retention of the lecture material.

Experiment 2 provided additional evidence confirming the benefits of taking notes for retaining crucial information. These findings replicate Boggs et al. ( 2017 ) and Meade, Wammes & Fernandez’s (2019) findings showing individuals are better able to retain attended content if they take notes rather than simply listening or doodling while listening. Boggs et al. hypothesized that this finding was due to the fact that taking notes would enhance the encoding process. This seems very plausible as previous research has shown that note-taking has an instant positive effect on memory providing individuals with a deeper level of processing (i.e., the encoding effect; Di Vesta & Gray, 1972 ; Peper & Mayer, 1978 ). Boggs et al. speculated that this finding was also due to note-taking maintaining arousal levels and thus reducing boredom, which is consistent with the result of our study. Note-takers throughout the lecture reported paying more attention and experiencing mind-wandering less than the other groups. Although taking notes could not eliminate boredom altogether, it did appear to mitigate the negative consequences associated with boredom (e.g., reduced performance, reduced attention, increases in mind-wandering). Therefore, promoting note-taking in situations in which it is important for students to be able to later recall associated information appears be a more effective strategy than promoting doodling.

It is important to note that doodling in the lab may not produce the same effects as when doodling in real-world situations. Although we tried to emulate what it would be like in a regular classroom, there are a lot of variables that we cannot account for. In the lab, we take away the naturalism of doodling by placing participants in conditions and asking them to do a specific doodling-type task. It may be the case that students stagger their doodling by using it for short periods, or during moments when they know that the information taught is not as useful. As a result, we may not be able to replicate the specific type of doodling behaviour in the lab that is effective for a given participant in real-world settings. In most learning environments, students have the choice on whether and how they want to doodle, and maybe our providing strict guidelines on whether and how they doodle undermined the potential benefits. Beyond that, motivation could play a factor in the results. In the lab the consequences of not paying attention to the lecture material do not exist, the student completes the study and is able to leave while receiving the course credit. Whereas in a real-life lecture, the consequences of not keeping your attention engaged could be missing information about an upcoming assignment or content for an exam. Thus, students may lack the naturalistic motivation that happens when in a classroom. Future research would benefit from examining doodling in the classroom, rather than a lab, while probing students’ motivation to learn the material being presented.

Contrasting the fidgeting-reduces-boredom-and-increases-attention hypothesis and the fidgeting-reflects-inattention hypothesis is important for understanding how the mechanisms subserving attentional, behavioural, and affective engagement operate together and for resolving the conflict in prior evidence about the extent to which doodling is a viable method for educators and students to implement in the context of learning. With the overall consistently of our findings with the fidgeting-reflects-inattention hypothesis, our results suggest that the adoption of doodling exercises or other fidgeting-based interventions within classroom settings may not be an effective strategy for increasing classroom engagement or promoting learning.

Declarations

Acknowledgments 

This work was supported by the Natural Science and Engineering Research Council of Canada (Discovery Grant #401526).

Ethical Approval  

All methods and procedures were approved by Research Ethics Board at the University of Guelph.

Consent to participate

Informed consent was obtained from all individual participants included in the studies.

Consent to publish

Consent to publish was obtained from all individual participants included in the studies. 

Competing interests  

The authors have no competing interests to declare that are relevant to the content of this article.

Authors' contributions  

E.K.S-M designed the study, developed study materials, coordinated participant recruitment and data collection, cleaned and analyzed the data, interpreted results, and wrote the manuscript. M.J.F. secured funding, designed the study, interpreted results, and edited the manuscript.

This work was supported by the Natural Science and Engineering Research Council of Canada (Discovery Grant #401526). 

Availability of data and materials  

The datasets in the presented studies are available from the corresponding author upon reasonable request.

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September 24, 2024

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Researchers refute the validity of 'assembly theory of everything' hypothesis

by King's College London

Three new papers refute claims for the assembly theory of molecular complexity being claimed as a new "theory of everything."

First publicly posited in 2017, assembly theory is a hypothesis concerning the measurability of molecular complexity that claims to characterize life, explain natural selection and evolution, and even to redefine our understanding of time, matter, life and the universe.

However, researchers led by Dr. Hector Zenil from the School of Biomedical Engineering & Imaging Sciences (BMEIS), in collaboration with colleagues from King Abdullah University for Science and Technology (KAUST) and the Karolinska Institute in Sweden, have successfully demonstrated in a paper published in npj Systems Biology , that the same results can be achieved by using traditional statistical algorithms and compression algorithms.

In a second paper just published by PLoS Complex Systems , they have also mathematically proven that assembly theory is an equivalent to Shannon Entropy and therefore not a novel approach to any of those applications and is an implementation of a well-known and popular compression algorithm used behind ZIP compression and image encoding formats such as PNG.

The third paper, "Assembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms," is available on the arXiv preprint server.

"Our research demonstrated that the Assembly Index, the core component of assembly theory which determines the 'aliveness' of an object by the number of exact copies it possesses, as an original method, is not, and its conclusions are flawed," says Dr. Hector Zenil.

"When we applied traditional compression algorithms to molecular or chemical data, the same verified results were obtained as under assembly theory. This means that, rather than being a new framework, assembly theory is indistinguishable from other pre-existing measures of complexity. Yet, the original authors did not test for any other algorithms."

"Despite some vegetables and plants such as onions and ferns having up to 50 times longer genomes with their many numerous gene copies, it is difficult to argue that onions or ferns are more complex or alive than humans, like assembly theory would suggest based on such unidimensional index," says Prof Jesper Tegner.

"What truly defines life is not merely genetic length or number of components but the intricate relationship with their environment, the agency life exhibits, and its resilience in preserving its essential properties."

"Our analysis sheds light on the limitations of assembly theory's numerical indices, attempting to define 'aliveness' and life characteristics. What truly surprises me is the neglect of the crucial role of dynamic interactions in understanding life complexity. Even more alarming is the decision to propose a fixed life-detection threshold with no basis," says Dr. Narsis A. Kiani.

"The real breakthrough lies in building upon established knowledge, integrating seemingly diverse theories to unravel the complex multidimensional dynamics that shapes life rather than rehashing what we already knew with tools we had already developed."

While characterizing life is hard and still an open problem, it has been studied from many angles, from modular units by Gregor Mendel to thermodynamics by Erwin Schrödinger to Statistical Entropy by Claude Shannon to Algorithmic Information by Gregory Chaitin.

Equipped with all this knowledge and much more from complexity sciences and systems' biology, it is known today that one key aspect of life is that of open-endedness, the fact that life's agency seems not bounded to regular behavior or repetition in its adaptation and relationship to its environment.

Areas such as Algorithmic Information Dynamics (AID) led by Dr. Hector Zenil and his collaborators, are shedding light on how to find causal models for natural phenomena and mechanistic explanations for processes of living systems.

AID is fully based on the current combined knowledge of information theory and causal inference to this date and builds upon and bridges these fundamental areas used today to understand the world.

The methods behind AID already count for exact copies of modules but that is the most obvious first step and something Dr. Zenil reported before assembly theory as capable of separating organic compounds from non-organic as a function of molecular length.

Felipe S. Abrahão et al, Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution, PLOS Complex Systems (2024). DOI: 10.1371/journal.pcsy.0000014

Luan Ozelim et al, Assembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms, arXiv (2024). DOI: 10.48550/arxiv.2408.15108

Journal information: arXiv

Provided by King's College London

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