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Br J Educ Psychol. 2023;93(Suppl. 2):353–367. wileyonlinelibrary.com/journal/bjep 353
1Department of Educational Development and
Research, Faculty of Health, Medicine, and Life
Sciences, School of Health Professions Education
(SHE), Maastricht University, Maastricht, The
Netherlands
2Psychology Department, Faculty of Humanities,
Bina Nusantara University, Jakarta, Indonesia
3Department of Physiology, Faculty of Health,
Medicine, and Life Sciences, School of Health
Professions Education (SHE), Maastricht University,
Maastricht, The Netherlands
Correspondence
Felicitas Biwer, Department of Educational
Development and Research, School of Health
Professions Education (SHE), Faculty of Health,
Medicine, and Life Sciences, Maastricht University,
Maastricht, The Netherlands.
Email: f.biwer@maastrichtuniversity.nl
Abstract
Background: During self-study, students need to moni-
tor and regulate mental effort to replete working memory
resources and optimize learning results. Taking breaks during
self-study could be an effective effort regulation strategy.
However, little is known about how breaktaking relates to
self-regulated learning.
Aims: We investigated the effects of taking systematic or
self-regulated breaks on mental effort, task experiences and
task completion in real-life study sessions for 1 day.
Sample: Eighty-seven bachelor's and master's students
from a Dutch University.
Methods: Students participated in an online intervention
during their self-study. In the self-regulated-break condition
(n = 35), students self-decided when to take a break; in the
systematic break conditions, students took either a 6-min
break after every 24-min study block (systematic-long or
‘Pomodoro technique’, n = 25) or a 3-min break after every
12-min study block (systematic-short, n = 27).
Results: Students had longer study sessions and breaks
when self-regulating. This was associated with higher levels
of fatigue and distractedness, and lower levels of concen-
tration and motivation compared to those in the system-
atic conditions. We found no difference between groups in
invested mental effort or task completion.
Conclusions: Taking pre-determined, systematic breaks
during a study session had mood benefits and appeared
to have efficiency benefits (i.e., similar task completion in
shorter time) over taking self-regulated breaks. Measuring
how mental effort dynamically fluctuates over time and how
effort spent on the learning task differs from effort spent on
regulating break-taking requires further research.
INVITED ARTICLE
Understanding effort regulation: Comparing
‘Pomodoro’ breaks and self-regulated breaks
Felicitas Biwer1 | Wisnu Wiradhany2 |
Mirjam G. A. oude Egbrink3 | Anique B. H. de Bruin1
DOI: 10.1111/bjep.12593
Received: 27 June 2022 Accepted: 10 February 2023
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
© 2023 The Authors. British Journal of Educational Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.
BIWER et al.
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INTRODUCTION
Learning costs effort. During self-study, when students write a summary, reread a text or try to retrieve
information from memory, they need to invest mental effort. This mental effort investment also helps
students to monitor (e.g., ‘do I understand this topic?’) and control (e.g., ‘I do not understand it yet and
will read further’) their learning. Moreover, to maintain a high level of mental effort invested in their
learning tasks, students need to regularly replete working memory resources. Taking breaks helps to
recover depleted working memory, for example, through active rest in between learning tasks (Chen
et al., 2018) or through a relaxation pause (Lee et al., 2021). However, this self-regulation of breaks comes
with a cost and might require additional effort (Seufert, 2018). For example, the decision if and when to
take a break requires additional metacognitive resources, and switching between the task and break can
cause additional load (Lee et al., 2021; Seufert, 2018). In this study, we investigated the effects of taking
externally regulated (so-called ‘systematic’) breaks or self-regulated breaks on effort regulation.
Effort regulation and opportunity costs
Mental effort refers to the amount of resources devoted by the learner to manage task demands (Paas
et al., 1994). The concept of self-management of mental effort suggests that mental effort can be modi-
fied and dealt with by students in various ways (de Bruin & van Merriënboer, 2017; Eitel et al., 2020; Mirza
et al., 2019). Effort regulation can be considered as the decision of the learner to start, maintain or stop
investing a certain amount of effort in a task (de Bruin et al., 2020).
Mental effort is often considered as costly or aversive. These costs are weighed against the benefits
of exerting effort (Inzlicht et al., 2018; Shenhav et al., 2017). For example, the cost of investing effort in
studying for an exam is outweighed by the benefits of getting a high grade. According to the opportu-
nity cost model (Kurzban et al., 2013), the perceived costs of investing effort are related to the next best
alternative and available opportunities. For example, by deciding to invest effort in studying for an exam,
a student might lose the opportunity to interact with their phone or go outside, which, therefore, consti-
tutes opportunity costs. According to Kurzban et al. (2013), the higher the opportunity costs are, the
higher the mental effort. For example, having the phone on the table constitutes a high opportunity cost,
and studying for an exam with the phone present would be perceived as more effortful than in absence
of the phone. Negative experiences that come up during the exertion of a task, such as boredom, fatigue
or distraction, can be thought of as a result of monitoring other opportunities and lead to disengage-
ment in the primary task by switching to more pleasant opportunities (Kurzban et al., 2013; Milyavskaya
et al., 2019). With the increasing preponderance of learning settings characterized by high learner auton-
omy and low teacher guidance (e.g., in distance learning), opportunity costs are likely to be high. It is,
therefore, an important question how students can be supported in self-regulating their effort effectively.
Effort regulation and studying
Several studies have shown the intricacies of effort monitoring and regulation during self-study (Bowman
et al., 2010; Calderwood et al., 2014; May & Elder, 2018). One major challenge for students is to sustain
their effort investment in their learning tasks for a longer time. In a 3-h observational study, on average,
students were distracted 35 times and spent in total 25 min with distractions other than their learning
tasks (Calderwood et al., 2014). Research on task switching during computer work showed that people
switch tasks every 12 min (Gonzalez & Mark, 2004; Mark et al., 2005), and that 50% of task switches
KEYWORDS
break-taking, effort regulation, self-regulated learning
UNDERSTANDING EFFORT REGULATION 355
during work are self-initiated (Czerwinski et al., 2004). Self-interrupted tasks were less likely to be finished
than externally interrupted tasks (Mark et al., 2005). Moreover, Katidioti et al. (2016) showed that exter-
nally interrupted tasks were finished faster, indicating that they are less disruptive than self-interruptions.
Self-initiated breaks tended to lead to a delayed increase of arousal, which led to slower task resumption
(Katidioti et al., 2016). In sum, self-initiated task-switching might impair students' self-regulation of effort
and decrease their performance (Bowman et al., 2010; May & Elder, 2018).
To help students to sustain effort in their learning tasks and deal with unplanned task-switching or
getting distracted by more attractive opportunities (e.g., texting on the phone), taking system-regulated
breaks could be a promising effort regulation strategy. System-regulated breaks could alleviate the load
of deciding if, when and for how long to take a break. Lee et al. (2021) introduced the conceptualization
of primary load (that is caused by domain-specific performance) and secondary load (that is caused by
general processes, such as self-regulation). Thus, the self-monitoring and self-regulation of break-taking
can cause additional, secondary cognitive load (Lee et al., 2020). Self-regulated break-taking might add
secondary load on the student as she/he has to decide about the right moment to take a break while
being exposed to other opportunities, making a break potentially more attractive and continuing with the
learning task more difficult. In contrast, system-regulated breaks may reduce the secondary load and thus
restore working memory resources towards processing and managing primary cognitive load, which will
ultimately benefit learning.
To our knowledge, research on the amount and timing of system-regulated breaks is very limited. From
a task-switching perspective, short systematic breaks might be beneficial in interrupting students' urge to
switch to other, more attractive opportunities. As people tend to switch tasks about every 12 min (Mark
et al., 2005), short systematic breaks might be important for sustaining effort. When system-regulated
breaks would occur every 12 min, participants might be able to shift that urge towards the short systematic
breaks and might experience lower opportunity costs during the study blocks.
Another commonly known, but underinvestigated technique of systematic breaks is the “Pomodoro
technique” (Cirillo, 2018), recommended in several applications and study-skills books (e.g., Pancholi, 2022;
Scroggs, 2023). This technique promotes longer study sessions of 25 min while avoiding any distractions,
followed by a 5-min break in which any alternative actions (e.g., social media, going outside) are allowed.
This is repeated four times before being followed by a longer break. When taking these short or long
systematic breaks, students are assumed to be able to invest a higher level of mental effort in the learning
task, as opportunity costs are postponed towards the systematic breaks. Furthermore, systematic breaks
might decrease the total time for participants to complete their learning tasks, as they might switch back
to their work faster after having taken a break (Katidioti et al., 2016). In contrast, having to self-regulate
the break-taking might increase mental effort, thus making it more difficult to work on the learning tasks.
The present study
In this study, we examined how to support students in regulating their effort during self-study. Students
were randomly assigned to one of three conditions: systematic short, systematic long or self-regulated
breaks. More specifically, we compared the effects of two systematic break conditions, in which students
were instructed to systematically take breaks during their study session, with a self-regulated break condi-
tion, in which students were instructed to take breaks whenever they decided to take these. The length
of study blocks and breaks for the systematic short break condition were based on literature on inter-
ruption and task switching and set on 12 min per study block and 3 min per break (Mark et al., 2005). For
the systematic long break condition, we used the Pomodoro technique (Cirillo, 2018) including 24-min
study blocks followed by 6-min breaks. The latter has to our knowledge never been investigated empiri-
cally, but is highly recommended in study-skills books or applications that promote the effectiveness of
this technique to systematically structure self-study sessions. Specifically, during an authentic session of
self-studying, we examined the effects of taking systematic versus self-regulated breaks on task experi-
ences as indicators of opportunity costs, mental effort and task completion.
BIWER et al.
356
Opportunity cost hypothesis
Our first hypothesis was that participants in the systematic break conditions would have more positive
task experiences (being more energized, concentrated, interested and motivated to continue studying,
and less fatigued, distracted and bored) compared to the self-regulated break condition, reflecting lower
opportunity costs in the systematic conditions (Kurzban et al., 2013). We expected this effect to be
larger in the systematic short condition than in the systematic long condition, as the systematic short
breaks would interrupt the urge to switch between learning task and other opportunities more effectively
(Calderwood et al., 2014).
Mental effort hypothesis
Secondly, we hypothesized that the overall invested mental effort in the learning tasks would be higher
in the systematic break conditions than in the self-regulated break condition, as having to self-regulate
the break-taking might cause secondary load (Lee et al., 2021; Seufert, 2018). We further expected that
students in the self-regulated break condition would experience working on the learning tasks as more
difficult and that students in both systematic break conditions would be able to concentrate more on the
learning tasks.
Task completion hypothesis
Third, we expected participants in the systematic break conditions to have completed more of their learn-
ing tasks in their study session than those in the self-regulated break condition, as externally interrupted
tasks might be finished faster than self-interrupted tasks (Katidioti et al., 2016).
Students' experiences
We also explored what students did during the breaks and examined students' experiences during the
different break interventions by analysing their open responses to three questions about how students
experienced the intervention and how it differed from their usual study pattern using the qualitative
approach of thematic analysis.
METHODS
Participants and design
All participants had indicated their interest to participate in the current research during a previous survey
study, which took place in the setting of emergency remote education during the COVID-19 pandemic
(Biwer et al., 2021). Subsequently, they were invited via e-mail. One-hundred-and-sixty-one university
students registered for participation, gave their informed consent and completed a pre-questionnaire
about their self-regulated learning in emergency remote education during the COVID-19 pandemic and
demographic questions. As participants answered this pre-questionnaire a week before they participated in
this study, we assume that these questions did not affect the results of the intervention or to similar extent
for all participants across conditions. These students were invited to participate in the current online
intervention in the week after they completed the pre-questionnaire. Of the 161 registered students, 110
finally agreed to participate in the current intervention study. These students studied at a medium-sized
university in the Netherlands with a mean age of 21.4 years (SD = 2.7), 85% female. They were randomly
assigned to one of three conditions: self-regulated breaks (n = 38), systematic long breaks (n = 34) or
UNDERSTANDING EFFORT REGULATION 357
systematic short breaks (n = 38). They were instructed to open an online learning environment (via Qual-
trics) during their self-study over a period of 1 week (Monday until Friday). In the systematic long and
short break conditions, participants were asked to study in blocks of 24 or 12 min, respectively, and then
take a break for 6 or 3 min respectively. In the self-regulated break conditions, participants could study and
take a break whenever they wanted to. Due to high missing values and drop-out rates after the first day
and few participants studying every day, we decided to only take the first day into account for the current
study. Ninety-two participants completed that first day. With regard to the total time spent in the learning
environment, five extreme outliers were identified by inspecting the box-plot and excluding z-scores with
an absolute value of >3.29, showing that these scores belong to the most extreme .1% of the reference
distribution (Tabachnick et al., 2007). Thus, the final sample consisted of 87 participants: self-regulated
breaks (n = 35), systematic long breaks (n = 25), and systematic short breaks (n = 27). See Figure 1 for an
overview of the selection of the final sample.
The sample included 68 bachelor's degree students and 19 master's degree students from six different
faculties: Faculty of Health, Medicine, and Life Sciences (28%), Faculty of Business and Economics (18%),
Faculty of Science and Engineering (18%), Faculty of Psychology and Neuroscience (14%), Faculty of
Social Sciences (12%) and Faculty of Law (8%). In the week of the current study, 40% of the participants
had to study for exams, 39% had to prepare for tutorial meetings, 20% were writing their thesis and 53%
were working on other assignments (several students worked on more than one study task).
Measures
Study time
Participants indicated their intended study time at the start of the study session (‘How long do you plan
to study today? (in minutes)’). Actual study time was measured within the online environment per study
block (free for the self-regulated condition and fixed for the systematic conditions), while the actual
duration of breaks was measured too (free for the self-regulated condition and fixed for the systematic
conditions).
Task experiences
At the start of the study session, each time before taking a break and at the end of the study session, all
participants were asked to rate their current task experiences (‘How are you feeling right now?’) on a scale
from 1 (not at all) to 5 (very much) regarding fatigue, energy, distraction, concentration, boredom, interest
and motivation (based on Milyavskaya et al. (2019)).
FIGURE 1 Selection of participants and drop-out.
BIWER et al.
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Mental effort
At the end of the study session, participants rated their invested mental effort (‘How much mental effort
did you invest in working on your learning tasks?’), concentration (‘How much did you concentrate during
this learning session?’) and perceived difficulty (‘how difficult was it for you to work on your learning
tasks?’) on a scale from 1 (very, very low) to 9 (very, very high) (Paas, 1992).
Task completion
At the start of the study session, participants were asked to name up to five learning tasks they aimed to
work on during the session. At the end of the session, participants were asked to rate to which extent
they completed each learning task (0%–100%). Furthermore, participants were asked to indicate their
satisfaction, enjoyment and feeling of control during the study session on a scale from 1 (not at all) to 5
(very much).
Break activity
At the end of each break, participants were asked to indicate what they did during the break in a
multiple-choice format with the option to add other activities. The listed activities were: drink water, have
a snack, go to the bathroom, drink coffee, do some form of physical activity, check social media, check
and send personal text messages, listen to music, surf the web, talk to someone and go outside for some
fresh air (based on Fritz et al., 2011).
Experiences of break intervention
At the end of the study session, we asked participants how they experienced the break-schedule with three
open questions: “How was the break-taking schedule different from your usual studying?”, “In what way
did the intervention influence the way you studied?”, and “Do you have any other comments or sugges-
tions regarding the intervention?”.
Procedure
Data collection took place in June 2020 during the COVID-19 lockdown. At the start of the pre-questionnaire,
all participants provided informed consent. They completed the pre-questionnaire online using their own
digital devices. Afterwards, participants were randomly assigned to one of the three conditions (see above).
All participants received the instruction to open the online learning environment when starting their study
session. At the start, all participants first indicated how long they intended to study that day, which learning
tasks they intended to work on and rated their current experiences. In the self-regulated break condition,
participants were instructed to study as long as they wanted and to click the button ‘break’, whenever they
took a break. At the end of each break, participants had to click the button ‘continue to study’. The time
before the first break or between breaks is termed ‘study block’. For the systematic break conditions, a
timer on the screen counted backwards, indicating the time left to study per block or time left for their
break per block. A sound informed participants in these conditions when they had to take a break or
continue studying. Each time before taking a break (in between the sound notification and the actual start
of the break), all participants were asked to rate their current task experiences, and after the break, partic-
ipants indicated what they did during that break. After 120 min, all participants were notified that their
study session had ended; some participants already ended the study session earlier. They were all asked
UNDERSTANDING EFFORT REGULATION 359
to indicate mental effort during the complete study session. Furthermore, participants answered to what
extent they completed their learning tasks, indicated final task experiences, and whether they intended to
continue studying that day; they also answered the open questions. Participants could then continue stud-
ying using the online environment if they wanted to. See Figure 2 for an overview of the study procedure
and Figure 3 for an impression of the online environment for the self-regulated break-condition.
Data analysis
Statistical Package for Social Sciences (SPSS 25) was used for all statistical analyses with an alpha level
of .05. As effect size measure, we used partial eta squared for ANOVAs and Cohen's d for t-tests
FIGURE 2 Study procedure. Note: Study procedure for all conditions (self-regulated breaks, systematic long breaks and
systematic short breaks).
FIGURE 3 Impression of the online environment for the self-regulated break condition.
BIWER et al.
360
(Cohen, 1988). In case of violation of the homogeneity of variance assumption, degrees of freedom were
adjusted with Welch's test. Hypothesized group differences regarding mental effort and task completion
were analysed with two-step planned contrasts, first comparing the self-regulated break condition with
both systematic conditions (−2, 1, 1) and then comparing both systematic conditions (0, −1, 1). Task
experiences before taking a break were averaged over the study session, incorporating all measurements
after a study block and before a break. The number of measurement points thus differed between condi-
tions depending on the number of breaks. Open-ended responses regarding students' experiences during
the break-taking intervention were examined by the first author using an inductive approach to thematic
analysis (Braun & Clarke, 2006). We analysed their answers separately per condition and across conditions
to identify overarching and condition-specific themes including steps of open (generating codes for data
categories), axial (identifying main themes and related categories) and selective (connecting categories)
coding. Atlas.ti 22 was used to analyse the qualitative data.
RESULTS
Study time and self-regulated break-taking
See Table 1 for descriptive statistics about intended and actual study times and Table 2 for average study
and break lengths. Regarding intended study time, there were no significant differences between the
self-regulated and systematic break groups, F(1, 84) = 1.15, p = .287, ηp
2 = .01. Students intended to study
significantly longer than they actually did, t(86) = 8.44, p < .001, d = .90, but there were also no signifi-
cant differences between groups regarding the difference between intended and actual study time, F(2,
84) = 1.41, p = .249.
On average, participants in the self-regulated break condition had significantly longer study blocks and
breaks compared to both the systematic long and systematic short break conditions (study blocks: F(1,
84) = 52.8, p < .001, ηp
2 = .39, breaks: F(1, 84) = 28.37, p < .001, ηp
2 = .25). Regarding the frequency of
breaks, participants in the self-regulated break condition took one (34%), two (29%), three (29%) or four
(8%) breaks during the 120-min study session, against four breaks in the systematic long and eight breaks
in the systematic short break condition. There was no significant difference between groups in total study
time spent in the online study environment, F(2, 84) = 2.89, p = .061, ηp
2 = .06 (see Table 1). Furthermore,
TABLE 1 Study times (minutes).
Intended study time Actual study time Total break time
M SD M SD M SD
Self-regulated breaks (n = 35) 161.51 91.93 82.52 39.54 28.05 26.77
Systematic long (n = 25) 222.00 114.89 93.34 8.08 23.30 1.99
Systematic short (n = 27) 151.63 120.09 74.27 26.43 18.58 6.63
Total (N = 87) 175.83 110.78 83.07 30.04 23.75 17.71
Note: No significant differences between groups.
TABLE 2 Average study block length and break length (minutes).
Time study block Time break
M SD M SD
Self-regulated breaks (n = 35) 43.96 25.64 14.67 13.71
Systematic long (n = 25) 24.05 .23 6.01 .01
Systematic short (n = 27) 12.01 .01 3.01 .01
Total (N = 87) 28.33 21.18 8.56 8.61
UNDERSTANDING EFFORT REGULATION 361
there was no significant difference between groups with regard to whether they wanted to continue study-
ing (self-regulated break: 62%, systematic long: 88%, systematic short: 63%) or not, χ
2 = 5.35 (2), p = .069
after the 120 min. As control, we had asked participants of both systematic break conditions to rate the
extent to which they adhered to the break-instructions on a scale from 1 (never) to 5 (always), with M = 4.0
(SD = .97) and no differences between the systematic long and systematic short condition; p = .502.
Task experiences
The overall average task experiences just before taking a break differed significantly between the three
groups, F(14, 1164) = 3.12, p < .001, ηp
2 = .04. Participants in the self-regulated break condition were
significantly more fatigued, t(238.33) = −2.20, p = .029, d = −.46, more distracted, t(240.67) = −3.45,
p < .001, d = −.71, less concentrated, t(249.47) = 2.35, p = .020, d = .47 and less motivated to start studying
again, t(587) = 3.81, p < .001, d = .72, compared to both systematic conditions. Students in the systematic
short condition felt more interested before taking a break than those in the systematic long condition,
t(587) = 2.97, p = .003, d = .29. All other differences were not significant, all p's > .182. See Figure 4 for a
visualization of these results.
Mental effort
Planned contrasts revealed that there was no difference between groups in invested mental effort,
t(84) = .09, p = .931, but students in both systematic conditions felt more concentrated (M = 5.74,
SD = 1.30) than in the self-regulated condition (M = 4.94, SD = 1.89); t(55.30) = 2.17, p = .034, d = 1.02.
There were no significant differences between both systematic conditions in invested mental effort,
t(84) = .54, p = .590, nor in concentration, t(45.97) = .79, p = .432. Perceived difficulty, was significantly
higher in the self-regulated break condition than in both systematic conditions, t(84) = −2.03, p = .046,
d = −.89, without differences between both systematic conditions, t(84) = −1.10, p = .273. See Figure 5
for a visualization of these data.
FIGURE 4 Average task experiences just before the breaks. Note: Ratings on a scale from 1 (not at all) to 5 (very much).
Significant univariate effects between both systematic conditions and the self-regulated condition are marked with *p < .05;
***p < .001. Error bars represent standard error of the mean.
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
fatigued* energizeddistracted*** concentrated*bored interested motivated to
start
studying***
self-regulated systematic long systematic short
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362
Task completion
On average, participants formulated 3.1 learning tasks at the start of the study session (M = 3.1,
SD = 1.12), no differences between groups (p = .776). These learning tasks varied regarding specificity
and achievability, for example, ‘Watch the lecture about inflammation of fat in diabetes’ and ‘Make notes
of the lecture’ versus ‘Write discussion for thesis’ or ‘Literature research’. At the end of the study session,
there was no difference in overall task completion between the conditions, F(2, 86) = 1.85, p = .164. On
average, participants achieved 48.3% (SD = 29.1%) of their learning tasks. There were no differences
between conditions regarding satisfaction, enjoyment or feeling of control during the study session (all
p's > .14).
Break activity
Averaged over the first three breaks, the most common break activities were ‘drink water’ (20.9%), ‘check
social media’ (20.1%), ‘check and send personal text messages’ (19.5%) and ‘go to the bathroom’ (14.5%).
Less common were ‘go outside’ (5.6%) and ‘do some physical activity’ (6.2%).
Students' experiences during the intervention
For an overview of all themes, the related codes and example quotes, see the Appendix 1. A salient theme,
specifically in the self-regulated break condition, was feeling watched and becoming ‘more aware’ of one's
own study and break habits. One participant mentioned that it held ‘[him/her] accountable to have a timer
counting while [he/she] worked or took breaks’. It also helped to ‘study more persistently, and made
[them] think more about how long the breaks during study sessions are’. Participants in the self-regulated
break condition also mentioned that the intervention ‘made [them] try not to take as many breaks, in order
FIGURE 5 Mental effort, as indicated after study session. Note: Ratings on a scale from 1 (very, very low) to 9 (very, very
high). Significant univariate effects between both systematic conditions and the self-regulated condition are marked with *p < .05.
Error bars represent standard error of the mean.
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
How much mental effort did you
invest in working on your learning
tasks?
How difficult was it for you to
work on your learning tasks?
How much did you concentrate
during this learning session?
self-regulatedsystematic long systematic short
**
UNDERSTANDING EFFORT REGULATION 363
to not hit the “pause” button as often’. At the same time, some participants mentioned that they realized
they took breaks that were ‘too long’.
In the systematic break conditions, the externally regulated breaks were experienced as disrupting the
work flow, making it difficult to concentrate on the learning tasks again after a break. At the same time,
regular breaks were also experienced as ‘refreshing’, helping to focus during study blocks: ‘Sometimes I
wanted to continue what I was doing and I [had to] take the break. Other times I was relieved to pause for
a while and start a task with a refreshed mind’. Also, students mentioned that especially the short breaks
made them more motivated to concentrate during the learning tasks: ‘I didn't let myself get distracted
that easily because I was thinking: “It's only 12 min, I'll do this in the next break”’. Furthermore, in
both systematic conditions participants mentioned that they would usually study in longer study sessions
with fewer, but longer breaks. Participants in all conditions mentioned that formulating learning tasks
and evaluating their progress improved their monitoring and regulation, as, for example, one participant
mentioned: ‘Additionally, it really made me think of what exactly I am supposed to study at times and how
well I accomplished my goals after the study session.’.
DISCUSSION
This study examined the effect of taking systematic long, systematic short or self-regulated breaks during
an authentic self-study session on task experiences, mental effort and task completion. First, we found
that students took fewer breaks (2.1 breaks during 120 min) in the self-regulated break-taking condi-
tion than in the two systematic conditions (four and eight breaks, respectively). When self-regulating
break-taking, study blocks and breaks were longer. In line with our first hypothesis, this was associated
with higher levels of fatigue and distractedness, and lower levels of concentration and motivation to
continue studying just before taking a break, compared to the systematic conditions. The only difference
between both systematic conditions was that students in the systematic short condition were more inter-
ested in studying just before the breaks compared to those in the systematic long condition. In relation
to the concept of opportunity costs (Kurzban et al., 2013), these results indicate that opportunity costs
were perceived lower in the systematic conditions. As illustrated by the qualitative data, systematic breaks
might have helped to postpone distractions to the break, fostering concentration and motivation to start
studying again.
Second, we found no differences regarding overall invested mental effort. However, partially support-
ing our hypothesis, students in both systematic conditions reported to be more concentrated during the
study session and experienced working on the learning tasks as less difficult than those in the self-regulated
condition. This suggests that when the secondary load, needed to monitor and regulate break-taking, is
alleviated, students can concentrate more and perceive working on the learning tasks as less difficult.
However, because we only measured overall effort at the end of the study session, we cannot be sure
whether this difference was indeed due to higher costs of self-regulation, making it more difficult to
concentrate during the learning tasks, or by the longer study time in the self-regulated break condition on
the other hand, or both. Future research is needed to disentangle these possible explanations.
Third, we did not find that students in the systematic conditions completed more of their self-
set learning tasks than those in the self-regulated condition. Hence, our data do not support the idea
that externally interrupted tasks are finished to a greater extent than self-interrupted tasks (Katidioti
et al., 2016). This may have been due to the great variety in terms of specificity and achievability of the
learning tasks. Although some learning tasks (e.g., ‘reading chapter 1’) were specific and achievable within
120 min, other learning tasks (e.g., ‘write the introduction of my thesis’) were more complex and less likely
to be completed within one study session. However, regarding the shorter study time in the systematic
condition, students in the systematic condition seemed to be more efficient, finishing tasks to a similar
extent in less time.
In this field study, we investigated students studying in an authentic online learning environment with
their own study materials. Studying mental effort in such an authentic setting is unique and complex, and
BIWER et al.
364
calls for more research in educationally relevant settings. While we deem the authenticity of the learning
environment as a strength of this study, it also comes with several limitations. First, we had little control
on what students actually did during their self-study session and relied on their adherence to the instruc-
tions. Second, we had no objective learning performance measure, as students worked on their own
relevant, but different learning tasks. We investigated the effect of break-taking in this self-study context,
but the self-set learning tasks varied greatly regarding their specificity and achievability. While the lack of
objective performance measures is a limitation of this study, the authenticity of the learning environment
and the personal relevance of the learning tasks for the participants is a strength. To account for session
heterogeneity, future research should consider adding objective learning tasks. Third, the self-reported
measurements of effort were limited in their capacity to capture the dynamicity of the different types
of mental effort that play a role in effort-regulation. We operationalized effort in terms of students'
overall rating of invested effort, concentration and perceived difficulty. However, it is unclear to what
extent the often-used item of how much effort is invested is actually capturing invested mental effort
and how it distinguishes from concentration (Seufert, 2018). Furthermore, the effect of break-taking on
effort regulation can be two-sided. On the one hand, a relaxation break in between a study session should
replete working memory resources and reduce mental effort (Chen et al., 2018; Lee et al., 2021). On the
other hand, task-switching costs effort and the opportunity costs of taking a break might cause second-
ary load (May & Elder, 2018; Seufert, 2018). These counteracting effects might cancel each other out to
some extent. Future research should elucidate how to measure the different effects of working memory
repletion and task switching. Combining subjective ratings of mental effort with more objective measures
of effort, such as pupil dilation, may represent a promising way to achieve a more valid measurement
of mental effort (Ayres et al., 2021; Lee et al., 2021; Scheiter et al., 2020). This could help to disentangle
whether the longer study blocks or the higher secondary load, caused by self-regulation of the breaks, is
causing the students to feel more fatigued and less concentrated.
Another potential limitation may be that the breaks of students in the self-regulated condition were
not completely self-regulated. Students in this condition mentioned that studying in the online envi-
ronment, with a clock indicating the study or break time, made them more aware of their study and
break-behaviour. Students mentioned ‘feeling watched’, making them feel accountable. Some students in
the systematic conditions felt forced to take breaks and became reluctant to follow the strict schedule,
which may have been associated to the higher drop-out rates in these groups. From a self-determination
theory perspective (Deci & Ryan, 2000), systematic breaks might reduce students' feelings of autonomy
and competence, leading to higher drop-out rates. However, students in the systematic conditions also
reported becoming more aware of their break-taking behaviour. Especially in the context of this study,
during the COVID-19 lockdown, offering external regulation by an online system may have supported
students in their self-regulated learning.
To conclude, this study showed that self-regulation of break-taking was associated with less efficient
studying, higher levels of fatigue and distractedness and lower levels of concentration and motivation to
continue studying compared to conditions with systematic breaks. In addition, students in the systematic
break conditions experienced learning tasks as less difficult. Further investigation into ways to measure
mental effort is required to provide additional information on the underlying mechanisms between load
imposed by the regulation and repletion of working memory resources. Furthermore, the type and speci-
ficity of the learning tasks might play an important role in the optimal length of breaks and study sessions
and needs further investigation.
AUTHOR CONTRIBUTIONS
Felicitas Biwer: Conceptualization; data curation; formal analysis; investigation; methodology; project
administration; resources; software; validation; visualization; writing – original draft. Wisnu Wiradhany:
Conceptualization; data curation; formal analysis; investigation; methodology; software; supervision;
validation; writing – review and editing. Mirjam oude Egbrink: Conceptualization; methodology;
supervision; writing – review and editing. Anique de Bruin: Conceptualization; funding acquisition;
UNDERSTANDING EFFORT REGULATION 365
investigation; methodology; project administration; resources; supervision; validation; writing – review
and editing.
CONFLICT OF INTEREST STATEMENT
None to declare.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author.
The data are not publicly available due to privacy or ethical restrictions.
ORCID
Felicitas Biwer https://orcid.org/0000-0003-4211-7234
Wisnu Wiradhany https://orcid.org/0000-0001-8707-3146
Mirjam G. A. oude Egbrink https://orcid.org/0000-0002-5530-6598
Anique B. H. de Bruin https://orcid.org/0000-0001-5178-0287
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APPENDIX 1
Themes Codes Example quote
Accountability Feeling watched “I felt watched, which motivated me to stick to the schedule (I normally
go to a learning space to feel the peer-pressure to keep studying)”
Self-regulated condition
Feeling of accountability “It also helped hold me accountable to have a timer counting while I
worked/took breaks” Self-regulated condition
Studying differently
than usual
Taking more breaks than
usual
“Also, I took a lot more breaks than I usually do. Additionally, it really
made me think of what exactly I am supposed to study at times
and how well I accomplished my goals after each study session”
Self-regulated condition
Usual break taking is
different
“In my usual way of studying, I just take a break when I am for example
bored.” Systematic short condition
Relaxation Forced breaks helped to
relax
“Usually I just study until a reading/chapter/task is done, taking no
breaks and pushing through. Being forced to take breaks gave a bit
more room in my head and made studying less exhausting.” Systematic
short condition
Difficulty to
self-regulate
breaks
Taking too long breaks “I noticed that sometimes I get carried away during a break and the break
takes way longer than I anticipated.” Self-regulated condition
UNDERSTANDING EFFORT REGULATION 367
Themes Codes Example quote
Task-switching costs Study periods too short “I felt as though the 12 minutes were too short because I spent nearly
5 minutes just trying to reorganize myself, read over any notes that I
had previously written, etc.” Systematic short condition
Disrupted work-flow “When I was just completely focused on the study task, I had to take a
break and every time it took me a while to get back in to studying and
into the study matter.” Systematic short condition
Too many breaks as
distracting
“For me, it sometimes felt that I was just in the flow of studying and
then I already had to stop again. However, the little breaks were nice,
but sometimes it felt too soon after I started to study.” Systematic long
condition
Goal-setting/Planning Goal setting as helpful “I made my goals way more explicit which was very helpful in the sense
that it guided me better and it was more satisfying to have something
explicit to have finished by the end of the day.” Systematic long condition
More monitoring of
goals
“It really made me think of what exactly I am supposed to study at times
and how well I accomplished my goals after each study session”
Systematic long condition
More concentration /
less distraction
Less distracted/more
focused
“I kept an eye on the time so I knew if I spent a lot of time on
something unimportant. I resisted the need to look on my phone
since I knew I would be able to check the messages in a couple of
minutes in the break.” Systematic long condition
Study more continuously “Limited time helped me to get focused and started easier (because I put
a mental time pressure of the allocated time).”; systematic short condition
More monitoring and
control
More aware about
break-taking
“I took less breaks compared to my usual way of studying, because I was
thinking actively about my break-taking.” Self-regulated condition
More aware about study
schedule
“Usually, I think (unless I am under a lot of time pressure) I do not
distinguish between breaks and study sessions that much; what
I mean is that I will go grab a fruit, drink, check my phone, etc.
throughout my entire study session instead of taking specific time to
do those things. Therefore, it brought to my attention how distracting
my usual schedule is.” Self-regulated condition
More structure “Much more structured. Usually, instead of allocating time blocks, I
decide on tasks that I need to complete in order to take a break. This
means that I usually take less, but longer breaks, thus it is hard to get
back to the ‘flow’ I was in before the break.” Systematic short condition
Taking less breaks
because of feeling
watched
“I have way much more distractions while studying at home and this
intervention made me try not to take as many breaks, in order to not
hit the “pause” button as often” Self-regulated condition
Taking more breaks
because of
intervention
“During this intervention I tried to take breaks regularly while normally I
tend to study without breaks.” Self-regulated condition
Feeling more efficient “Normally I study until I loose concentration. I want to keep going and
going because I want work done. Break taking like this makes me feel
like I have accomplished way more in terms of work done and energy
saved.” Systematic long condition
Motivation Intervention as external
motivator
“It motivated me to work faster; it allowed me to feel good about taking
breaks because I was “forced” to do so” Systematic short condition
Feeling more motivated
to study
“I thought ‘you have 24 minutes to do as much as you can because after
That you are taking a break’. So knowing I had ‘only’ 24 minutes really
gave me a boost actually, to put a lot of effort in my learning. Even
more so, after the 6-minute break I felt more motivated to continue
studying.” Systematic long condition
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