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SCHWERPUNKT
https://doi.org/10.1007/s11618-021-01002-x
Z Erziehungswiss
Learning during COVID-19: the role of self-regulated
learning, motivation, and procrastination for perceived
competence
Elisabeth Rosa Pelikan · Marko Lüftenegger · Julia Holzer ·
Selma Korlat · Christiane Spiel · Barbara Schober
Received: 31 July 2020 / Revised: 22 December 2020 / Accepted: 15 February 2021
© The Author(s) 2021
Abstract In March 2020 schools in Austria temporarily closed and switched to dis-
tance learning to contain the spread of the coronavirus (COVID-19). The resulting
situation posed great challenges to teachers, guardians and students (Huberand Helm
2020). Research has shown that perceived competence (Deci and Ryan 2000) affects
selfregulated learning (SRL), intrinsic motivation and procrastination, however few
studies have considered these variables in context of distance learning among ado-
lescents. This study investigated differences in students who perceived themselves as
high vs. low in competence with respect to these constructs. In an online question-
naire, 2652 Austrian secondary school students answered closed questions regarding
SRL, intrinsic motivation and procrastination as well as open-ended questions about
E. R. Pelikan, BSc MSc () · Ass.-Prof. Mag. Dr. M. Lüftenegger · J. Holzer, BEd BSc MSc ·
S. Korlat, BA MA · emer. Univ.-Prof. Mag. Dr. Dr. C. Spiel · Univ.-Prof. Dipl.-Psych. Dr. B. Schober
Educational Psychology, Department of Developmental and Educational Psychology, Faculty of
Psychology, University of Vienna, Universitätsstraße 7, 1010 Vienna, Austria
E-Mail: elisabeth.pelikan@univie.ac.at
Ass.-Prof. Mag. Dr. M. Lüftenegger
E-Mail: marko.lueftenegger@univie.ac.at
J. Holzer, BEd BSc MSc
E-Mail: julia.holzer@univie.ac.at
S. Korlat, BA MA
E-Mail: selma.korlat@univie.ac.at
emer. Univ.-Prof. Mag. Dr. Dr. C. Spiel
E-Mail: christiane.spiel@univie.ac.at
Univ.-Prof. Dipl.-Psych. Dr. B. Schober
E-Mail: barbara.schober@univie.ac.at
Ass.-Prof. Mag. Dr. M. Lüftenegger
Department for Teacher Education, Centre for Teacher Education, University of Vienna,
Porzellangasse 4, 1090 Vienna, Austria
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E. R. Pelikan et al.
challenges, successes and need for support in distance. Structural equation model-
ing was applied for the quantitative analysis which was complemented by thematic
analysis for the qualitative questions (Braun and Clarke 2006). Results showed that
students who experienced themselves as highly competent use SRL strategies (goal
setting and planning, time management, metacognitive strategies) more often and are
more intrinsically motivated than students with lower perceived competence. They
also procrastinate less. Furthermore, qualitative analysis revealed that although all
students face similar challenges (e.g., independent learning, time and task manage-
ment, learning on the computer, lack of contact with teachers and peers), students
who perceived themselves as highly competent seemed to cope better, and have
less need for support. Implications for distance learning and future research are
discussed.
Keywords COVID-19 · Intrinsic motivation · Perceived competence ·
Procrastination · Self-regulated learning
Lernen während COVID-19: Die Relevanz von selbstreguliertem
Lernen, intrinsischer Motivation und passiver Prokrastination für die
wahrgenommene Kompetenz
Zusammenfassung Im März 2020 wurden in Österreich Schulen vorübergehend
geschlossen und auf Lernen auf Distanz umgestellt, um die Ausbreitung des Corona-
virus (COVID-19) einzudämmen. Die daraus resultierende Situation stellte Lehrer,
Erziehungsberechtigte und Schüler*innen vor große Herausforderungen (Huber und
Helm 2020). Obwohl bisherige Forschung gezeigt hat, dass wahrgenommene Kom-
petenz selbstreguliertes Lernen (SRL), intrinsische Motivation und Prokrastination
beeinflusst, haben sich nur wenige Studien mit diesen im Kontext des Lernens auf
Distanz bei Jugendlichen befasst. Die vorliegendeStudie untersuchte die Unterschie-
de zwischen Schüler*innen, die sich selbst als hoch vs. niedrig kompetent wahrnah-
men und inwieweit diese Variablen hierfür eine Rolle spielen. In einem Online-
Fragebogen beantworteten 2652 österreichische Schüler*innen der Sekundarstufe
geschlossene Fragen zu SRL, intrinsischer Motivation und Prokrastination sowie of-
fene Fragen zu Herausforderungen und Erfolgen beim Lernen auf Distanz und dem
damit zusammenhängenden Unterstützungsbedarf. Für die quantitative Analyse wur-
de ein Strukturgleichungsmodell berechnet, welches durch eine thematische Analy-
se der qualitativen Fragen ergänzt wurde (Braun und Clarke 2006). Die Ergebnisse
zeigten, dass Schüler*innen, die sich selbst als hochkompetent erleben, häufiger
SRLStrategien (Zielsetzung und Planung, Zeitmanagement, metakognitive Strate-
gien) anwenden und höhere intrinsische Motivation aufweisen, als Schüler*innen
mit geringer wahrgenommener Kompetenz. Sie prokrastinieren außerdem weniger.
Darüber hinaus ergab die qualitative Analyse, dass, obwohl alle Schüler*innen mit
ähnlichen Herausforderungen konfrontiert sind (z.B. selbständiges Lernen, Zeit- und
Aufgabenmanagement, Lernen am Computer, mangelnder Kontakt mit Lehrern und
Gleichaltrigen), jene Schüler*innen, die sich selbst als hochkompetent wahrnehmen,
besser damit zurechtkommen und weniger Unterstützung benötigen. Implikationen
für das Lernen auf Distanz und zukünftige Forschung werden diskutiert.
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
Schlüsselwörter COVID-19 · Intrinsische Motivation · Prokrastination ·
Selbstreguliertes Lernen · Wahrgenommene Kompetenz
In March 2020, the novel coronavirus (COVID-19) was classified as a pandemic
by the World Health Organization (WHO 2020). In addition to other social distanc-
ing measures (e.g., curfews and business shutdowns), many countries temporarily
closed schools and switched to distance learning to contain the spread of the virus
(UNICEF 2020). The resulting situation posed great challenges for all actors in the
educational context. Teachers had to develop new concepts for “emergency teaching
at a distance” (Bozkurt et al. 2020) to ensure that lessons could continue without
disruption. Parents partially took over on the role of teachers in addition to their
work and household obligations (Huber et al. 2020; Viner et al. 2020). Students also
found themselves in a novel situation. While in face-to-face teaching, fixed struc-
tures regulated daily school life and learning time, students now had to organize
and self-regulate their learning autonomously with little time for preparation from
one day to the next. Self-regulated learning (SRL; planning, monitoring and adapt-
ing one’s thoughts, feelings and actions in a cyclical process to attain a personal
goal; Zimmerman 2000) and intrinsic motivation are considered important factors
for learning success in face-to-face settings (Dent and Koenka 2016; Fortier et al.
1995; Zimmerman 1990), and gain additional relevance when students face a situ-
ation such as distance learning with less external structure and guidance (Dabbagh
and Kitsantas 2004). Furthermore, intrinsic motivation, goal setting, and SRL are
influenced by perceived competence, that is, by the learner’s feeling of being able
to handle given tasks (e.g., Cho et al. 2011; Ferla et al. 2010; Miller et al. 1993).
A lack of both intrinsic motivation and of self-regulation has been associated with
higher passive procrastination (Steel 2007), which has in turn been associated with
various detrimental behaviors and outcomes (Howell and Watson 2007; Steel 2007)
in an academic context.
The aim of the current study was to investigate students differing in their perceived
competence when experiencing learning under COVID-19 conditions. We compare
students who perceive themselves as highly competent to those who perceive them-
selves as having low competence in this situation; specifically, we investigate how
they differ in terms of variables that are considered decisive for successful learning
in many studies: SRL, intrinsic learning motivation, and passive procrastination as
well as their experiences of challenges and successes during learning.
1 Theoretical background
1.1 Distance learning during the COVID-19 crisis
Simonson and Berg (2016) define distance learning as a “form of education in
which the main elements include physical separation of teachers and students dur-
ing instruction and the use of various technologies to facilitate student-teacher and
student-student communication”. Previous studies on distance learning conclude that
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E. R. Pelikan et al.
it can be just as effective as face-to-face teaching (Cavanaugh et al. 2004; Lee and
Figueroa 2012; Means et al. 2013). However, COVID-19 is a new and challenging
situation with respect to distance learning, as mandatory school shutdowns due to
a pandemic or natural catastrophe have never before been necessary at such a large
scale (Huber and Helm 2020).
During the COVID-19 lockdown, in addition to switching to distance learning,
most schools used digital platforms to support instruction during school closures
(Huber and Helm 2020). Although the digitization of teaching and learning has
frequently been called for in connection with the notion of lifelong learning and
adaptation to the modern labor market (European Commission 2018), it had not
yet been implemented across the board in Austria before COVID-19, especially in
primary and secondary education (Schrenk 2020; Wahlmüller-Schiller 2017). It is
therefore particularly important to investigate how students were able to cope with
this new situation and how students who perceived themselves as competent differed
from those who did not.
1.2 Perceived competence, self-regulated learning, motivation and passive
procrastination in distance learning
Distance learning in comparison to regular face-to-face lessons is characterized by
greater flexibility in scheduling, the opportunity to individualize learning processes,
the potential to enhance SRL skills and the easy distribution of information (Means
et al. 2013; Mupinga 2005; Paechter and Maier 2010;Rice2006). However, this
can present both advantages and disadvantages, especially for younger students
(Cavanaugh et al. 2004; Mupinga 2005), as the greater flexibility available in distance
learning places high demands on the learner’s ability to regulate their learning and
motivation (Adam et al. 2017; Dabbagh and Kitsantas 2004; Fryer and Bovee 2016;
Fryer et al. 2014) and thus poses an increased risk of passive procrastination (Rakes
and Dunn 2010).
SRL has long been recognized as an important contributor to learning success
in traditional as well as online learning settings (Dent and Koenka 2016; Donker
et al. 2014; Zimmerman 1990). Although various theoretical models have been
proposed (see Panadero 2017 for an overview), common to all is that self-regulated
learners are able to actively control their learning process by setting achievable
goals, managing their time and tasks, monitoring their progress, regulating their
motivation and seeking help when necessary (Zeidner et al. 2000). Distance learning
is typically less structured and therefore relies on learners to autonomously regulate
and organize their learning processes. Metacognitive strategies (e.g., monitoring
and evaluating progress towards goal achievement, adjusting learning strategies if
necessary, mobilizing personal and environmental resources) as well as other SRL
strategies such as setting goals and managing one’s time are considered to be even
more important in distance learning than in traditional learning settings (Dabbagh
and Kitsantas 2004). Furthermore, several studies have indicated that the use of
SRL strategies changes with age (Zimmerman and Martinez-Pons 1990; Cavanaugh
et al. 2004), suggesting that younger students need more support in regulating their
learning.
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
Another factor driving academic success is intrinsic motivation. While extrinsic
motivation, which involves external rewards or punishments, may be detrimental to
academic achievement and well-being (see, for example, Ryan and Deci 2000), in-
trinsic motivation has been proven to be an important predictor for learning success
in the form of e.g., reading achievement (Froiland and Oros 2014) and higher grades
(Fortier et al. 1995; Ning and Downing 2010). According to self-determination the-
ory (SDT; Deci and Ryan 2000,2008; Ryan and Deci 2000), intrinsic motivation
arises if an activity satisfies the need for perceived competence and autonomy (sup-
ported by social relatedness). Perceived competence refers to an individual “feeling
competent with tasks and activities” (Chen and Jang 2010, p. 742), while auton-
omy refers to a feeling of agency (Deci and Ryan 1985; Reeve et al. 2003). In the
educational context, students who feel that they have choice and responsibility in
their learning (autonomy) and feel competent in mastering their tasks (competence)
should experience increased intrinsic motivation (Deci and Ryan 2000). However,
while autonomous learning situations such as distance learning can increase motiva-
tion, they do so only if actors perceive themselves as competent and able to handle
the associated challenges and achieve their goals (Deci and Ryan 2000). Therefore,
perceived competence acts as an important predictor of intrinsic motivation (Cho
et al. 2011;Guayetal.2001; Stephan et al. 2011; Zisimopoulos and Galanaki 2009).
Results of previous studies have shown that perceived competence also influences
the use of SRL strategies such as goal setting, monitoring and the use of metacogni-
tive strategies (Miller et al. 1993; Pichardo et al. 2014; Zimmerman and Martinez-
Pons 1986). Studies have also found that intrinsic motivation may vary with age
(Gillet et al. 2012; Gottfried et al. 2001).
Whereas the use of SRL strategies and high motivation have been shown to
predict academic achievement, passive procrastination is usually associated with
various kinds of detrimental behaviors and outcomes. Passive procrastination in
the academic context means delaying school-related tasks even when faced with
negative consequences (Steel 2007; Steel and Klingsieck 2016). Notably, passive
procrastination has been differentiated from active procrastination, which describes
an intentional and strategic delay of tasks and is seen as an act of self-regulation
and not associated with negative consequences (Steel 2007). Passive procrastination
has been associated with lower goal commitment and less use of organizational as
well as metacognitive learning strategies (Howell and Watson 2007; Steel 2007). It
has also been shown to correlate with lower academic outcomes (e.g., lower GPA;
Steel 2007). Ryan and Deci (2000) suggest that passive procrastination is simply the
opposite of motivation. Wolters (2003), however, found that the use of metacognitive
SRL strategies also plays a role in the tendency to passively procrastinate. This
finding was echoed by Steel (2007) in his meta-analysis, which found that low self-
control, low self-discipline and organization and low achievement motivation are
all strong predictors of passive procrastination. In contrast to SRL and motivation,
the role of perceived competence for passive procrastination is less often examined.
Perceived competence was found to be a negative predictor of students’ passive
procrastination on academic tasks (Brando-Garrido et al. 2020). Another study found
that the link between fear of failure and passive procrastination was moderated by
perceived competence, indicating students with higher perceived competence could
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E. R. Pelikan et al.
handle challenges more positively than students with low perceived competence
(Haghbin et al. 2012).
Despite several studies documenting the importance of perceived competence for
SRL and motivation and the increased risk of passive procrastination in distance
learning (e.g., Ferla et al. 2010), to our knowledge, the interplay between these
constructs has not been investigated so far. The current study aims to fill this gap
by focusing on the role of perceived competence in SRL and motivation during
the COVID-19 pandemic, which provides a unique opportunity to investigate the
interplay among these constructs in distance learning.
1.3 The present study
Distance learning requires high self-regulation and intrinsic motivation and carries
the risk of passive procrastination. Therefore, SRL skills became imperative when
switching to distance learning during the COVID-19 pandemic. Previous research
indicates that perceived competence is related to various aspects of self-regulated
learning, such as goal setting, time management and the use of metacognitive strate-
gies. Furthermore, autonomous learning situations such as distance learning may
improve intrinsic motivation, but only in connection with high perceived compe-
tence.
Therefore, students who perceive themselves as highly competent should differ in
their use of self-regulated learning strategies, their intrinsic motivation and passive
procrastination from students who perceive themselves as less competent. In our
study, we separated students based on their self-reported perceived competence,
drawing from a larger sample of around 19,000 students. This allowed us to build
true extreme groups of still considerable sample size.
Furthermore, there is a lack of deeper knowledge about the different mecha-
nisms used by students who perceive themselves as highly competent in contrast to
students who perceive themselves as lacking competence in new and challenging
learning situations, such as distance learning during COVID-19. In our study, we
complimented our quantitative scales with qualitative data and asked students about
their challenges, successes and need for support in an open-ended format.
Our first research question addresses the differences between students who per-
ceive themselves as highly competent and students who perceive themselves as lack-
ing competence in important aspects of self-regulated learning (time management,
goals and plans, metacognition), intrinsic learning motivation and procrastination.
In line with previous studies, we expect students who perceive themselves as highly
competent to report higher scores on all SRL aspects (Zimmerman and Martinez-
Pons 1986) and on intrinsic motivation (Fortier et al. 1995; Froiland and Oros 2014;
Ning and Downing 2010; Rakes and Dunn 2010;Wangetal.2013,2019)aswell
as lower passive procrastination scores (Brando-Garrido et al. 2020; Haghbin et al.
2012) than students who perceive themselves as lacking competence.
In our second research question, we were interested in the mechanisms applied by
students who perceived themselves as high in vs. lacking competence in mastering
the distance learning situation. We asked students about their experiences in terms
of challenges as well as positive aspects and need for support. Applying thematic
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
analysis (Braun and Clarke 2006), we again focused on differences between students
who perceived themselves as highly vs. lacking competence.
2 Methods
2.1 Participants, procedure and context of data collection
This study was part of a larger study on learning during COVID-19 (Schober
et al. 2020). The full sample comprised 19,337 secondary school students (37.9%
males, 61.6% females, 0.5% diverse) with a mean age of 14.56 years (SDage = 2.49,
Mdn = 14.00, Range = 10–21). Data were collected with online questionnaires from
April 7th to April 24th, 2020. To recruit the sample, we distributed the link to
the online questionnaire by contacting manifold stakeholders such as school boards,
educational networks, and school principals. Additionally, the Austrian Federal Min-
istry for Education, Science, and Research recommended participation in the study
and published the link on its website. We also received support from several media
outlets. Participants were informed about the study’s goals, inclusion criteria for par-
ticipation, i.e., attending secondary school in Austria, and the complete anonymity
of their data. All students participated voluntarily and only those who gave active
consent were included in the dataset. In Austria, schools stopped providing onsite
learning on March 16th. Also, as of March 16th, the government announced that
homes could only be left for work, making necessary purchases, assisting other
people or outdoor activities, alone or in the company of people living in the same
household. During the full data collection period, schools were obliged to ensure
continued education by providing distance learning. Teachers and schools were au-
tonomous in the organization and design of remote teaching. Although there was
no on-site teaching, schools remained open, as they provided childcare in necessary
cases (Federal Ministry of Education 2020b). However, this offer was only taken up
by roughly 2% of the student population (Federal Ministry of Education 2020a).
2.2 Sample selection
For this study, a subsample of students was identified from the larger survey study.
Using an adapted version of the Work-related Basic Need Satisfaction Scale (W-
BNS; Van den Broeck et al. 2010) with three items, which were modified to suit the
school context (sample item: “These days I am able to successfully complete most
of my schoolwork”; CR= 0.85 for the whole sample), all students with scale means
of = 1 (low perceived competence; n= 235) and= 5 (high perceived competence,
n= 2417) were selected to build extreme groups (low and high perceived compe-
tence, respectively). Therefore, the sample for this study consisted of 2652 Aus-
trian secondary school students (37.6% males, 61.9% females, 0.6% diverse) with
a mean age of 14.15 years (SDage = 2.53, Mdn = 14.00, Range = 10–21). The students
stemmed from secondary schools all over Austria: academic-track secondary schools
(“Gymnasium”; 31.2%), comprehensive middle schools (“Neue Mittelschule”, “Mit-
telschule”, “Hauptschule”, “Polytechnische Schule”, or “Fachmittelschule”; 39.4%),
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E. R. Pelikan et al.
academic-track vocational schools (“Berufsbildende Höhere Schule”; 24.6%), lower-
track vocational schools and dual vocational education (“Berufsbildende Mittlere
Schule” or “Berufsschule und Lehre”; 4.2%), and other school types, including
inclusive education (0.6%).
2.3 Measures
Due to the novelty of the COVID-19 situation, it was necessary to adapt existing
scales or newly developitems to fit the current circumstances. To ensure the construct
validity of the measures, we conducted confirmatory factor analyses (CFA) and
analyzed composite reliability (CR; Raykov 2009). All items were rated on a 5-
point scale ranging from 1 (strongly agree) to 5 (strongly disagree). Participants
were instructed to answer the items with respect to the current situation (learning
from home due to the coronavirus). We conducted analyses with recoded items so
that higher values reflected higher agreement with the statements. We used three
scales (Goals and plans, Time, Metacognition) to measure aspects of SRL.
Goals and plans in terms of setting goals and planning one’s learning process was
assessed with three items, slightly adapted from the short version of the Learning
Strategies of University Students questionnaire (LIST-K; Klingsieck 2018;sample
item: “When I am currently studying, I make a plan of what I need to do”; CR= 0.80).
Tim e was assessed with three items also adapted from the LIST-K (Klingsieck
2018; sample item: “When I am currently studying, I reserve specific times for
studying each day”; CR = 0.72).
Metacognition was measured with three items. The first item was newly developed
for the questionnaire (“When I am currently studying, I try to motivate myself (e.g.,
through rewards for each completed task)”), while the other two were adapted from
the LIST-K (Klingsieck 2018; “When I am currently studying, I try out different
ways when something is not working”; “When I am currently studying, I seek out
feedback when I need it”; CR =0.62). The three items cover three important aspects
of metacognition: motivation regulation, monitoring, and resource management.
Passive procrastination was measured with three items slightly adapted from the
Procrastination Questionnaire for Students (PFS; Glöckner-Rist et al. 2014;sample
item: “I put off tasks until the last minute”; CR= 0.86).
Intrinsic learning motivation was assessed with three items adapted from the
Scales for the Measurement of Motivational Regulation for Learning in University
Students (SMR-LS; Thomas et al. 2018; sample item: “Currently, I really enjoy
studying and working for school”; CR= 0.93).
In addition to the quantitative questions, the survey contained six open-ended
questions, three of which were used in this study. In Question 1, students were
asked about challenges regarding distance learning during the COVID-19 crisis
(“What do you currently find especially hard when studying?”). Question 2 asked
whether students had success in distance learning (“What parts of studying are
currently going particularly well?”) and Question 3 addressed students’ need for
support (“With what could you currently use some help?”).
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
2.4 Data analysis
2.4.1 Quantitative analysis.
Quantitative data were analyzed using SPSS version 25.0 (IBM 2017)andMplus
version 8.4 (Muthén and Muthén 2017). To deal with the very small number of
missing values (ranging from 0.0% to 0.6% on the item level), the full information
maximum likelihood approach implemented in Mplus was employed. All statistical
significance testing for the quantitative analyses were performed at the 0.05 level.
However, due to the large sample, rather than relying on statistical significance,
we particularly focused on the identified effect sizes when interpreting the obtained
results. To interpret the effect sizes of regression parameters, we followed Cohen
(1988), according to whom standardized values of 0.10, 0.30, and 0.50 reflect small,
moderate, and large effect sizes, respectively.
First, confirmatory factor analyses using robust maximum likelihood estimation
(MLR) were conducted to analyze the scales’ construct validity. Goodness-of-fit was
evaluated using the χ2Test of Model Fit, Tucker-Lewis index (TLI), Comparative Fit
Index (CFI), root mean square error of approximation (RMSEA) and Standardized
Root Mean Residual (SRMR). In addition, a 90% confidence interval around the
point allowed us to estimate the precision of the RMSEA estimate. We considered
typical cutoff scores reflecting excellent and adequate fit to the data, respectively:
(a) CFI and TLI> 0.95 and 0.90; (b) RMSEA <0.06 and 0.08; (c) SRMR < 0.08
(Hu and Bentler 1999). Additionally, we relied on the comparative model fit in-
dices AIC and BIC for model comparison, with lower values indicating a better
trade-off between fit and complexity. We tested three CFA models for the five in-
vestigated constructs. In our first model, the items loaded onto the five different
constructs as expected (Goals and plans, Time, Meta-cognition, Intrinsic learning
motivation, Passive procrastination). All standardized factor loadings were mod-
erate to strong (ranging from 0.49 to 0.94) and the model demonstrated excellent
model fit indices, χ2(80) = 566.12, p<0.001, CFI =0.97, TLI =0.96, RMSEA= 0.048
[0.044, 0.052], SRMR = 0.039, AIC = 112,082.92, BIC= 112,406.41. We addition-
ally tested the hypothesized model against a one-factor model, χ2(90) = 5880.70,
p<0.001, CFI =0.59, TLI = 0.53, RMSEA= 0.156 [0.153, 0.159], SRMR= 0.117,
AIC = 118,985.53, BIC = 119,250.20, and a model with a SRL (Goals and plans,
Time, Meta-cognition) and a motivation factor (Intrinsic learning motivation, Passive
procrastination), χ2(84) = 950.43, p<0.001, CFI = 0.94, TLI = 0.92, RMSEA = 0.062
[0.059, 0.066], SRMR = 0.047, AIC = 112,571.57, BIC = 112,871.53. Our hypothe-
sized model showed the best model fit.
Second, we set up a SEM using the MLR estimator to test main effects (Goal and
plans, Time, Metacognition, Passive procrastination, Intrinsic learning motivation)
between the two groups. We conducted CFAs with covariates for all five outcome
variables. We introduced group (0 = low perceived competence; 1 = high perceived
competence) as a predictor for the outcome variables and additionally we controlled
for age. We report standardized coefficients (b*) and standard errors (SE).
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E. R. Pelikan et al.
2.4.2 Qualitative analysis.
Qualitative data analysis of the open-ended questions was conducted by applying
thematic analysis (Braun and Clarke 2006), using the software MAXQDA 2020
(VERBI Software 2019) to support the coding process. The categories were gener-
ated based on a mixed deductive and inductive approach. First, 300 answers to each
question were screened separately by the first author to familiarize herself with the
data. Initial codes were generated based on current research on distance learning
as presented in the literature review and applied to the data (deductive approach).
Subsequently, further main themes and subthemes based on the data were identified
and added to the category system (inductive approach). After this procedure, the
categories were defined and arranged in a systematic order. This step also included
the formulation of category descriptions with inclusion and exclusion criteria as
well as examples (see Tables IV–VI in the supplementary material for the final cat-
egorization system for each question). After this systematic coding of 300 answers,
the coding system for each question was reviewed, which involved redefining and
rearranging the themes and subthemes. Afterwards, a random sample of 25% of
the data for each question were coded separately by the first author and a second
trained researcher. Interrater reliability checks were conducted and found to be satis-
factory, with Cohen’s Kappa ranging from κ=0.89toκ= 0.95. Disagreements were
discussed, and category descriptions as well as the order of categories, were further
refined. Finally, the first author completed the coding for the remaining data.
To test for differences in the number of coded segments in the high, respectively
low perceived competence group,chi-squared tests for the number of coded segments
were computed for each of the main categories and subcategories. As conducting
a large number of significance tests bears the risk of alpha error accumulation,
significance testing was performed at the 0.01 level.
3Results
Table 1provides bivariate latent correlations among all variables as well as descrip-
tive statistics and composite reliabilities.
3.1 Main effects of intrinsic learning motivation, passive procrastination and
self-Regulated learning.
In order to investigate whether there are differences between students who perceived
themselves as high in vs. lacking competence in terms of intrinsic learning motiva-
tion, passive procrastination and self-regulated learning, CFAs with covariates were
conducted. The effect of age on the outcome variables of passive procrastination
and time was controlled for (both variables showed small age effects with r> |0.1|).
Overall fit indices indicated an excellent model fit, χ2(103)= 803.376, p< 0.001,
CFI= 0.95, TLI= 0.93, RMSEA= 0.051 [0.047, 0.054], SRMR= 0.041. Estimation
revealed that group (low vs. high perceived competence) positively predicted stu-
dents’ goals and plans, b* = 0.47, SE = 0.06, p< 0.001, time, b* = 0.37, SE = 0.05,
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
Tab l e 1 Bivariate Latent Correlations, Descriptive Statistics and Composite Reliabilities
12345
1. Goals and Plans –
2. Time 0.73 –
3. Meta-Cognition 0.67 0.63 –
4. Procrastination –0.48 –0.40 –0.40 –
5. Intrinsic learning motivation 0.53 0.47 0.62 –0.47 –
NumberofItems 33333
M4.42 3.37 3.87 1.88 3.71
SD 0.89 1.16 0.99 1.12 1.23
Skewness –1.99 –0.41 –0.84 1.44 –0.91
Kurtosis 3.80 –0.77 0.10 1.16 –0.16
Range 4.00 4.00 4.00 4.00 4.00
Composite Reliability 0.80 0.72 0.62 0.86 0.93
Note. N = 2652 students .All scales used a 5-point response format. CR Composite Reliability. All correla-
tion coefficients are statistically significant at p< 0.001
p<0.001, metacognition, b* = 0.51, SE = 0.07, p< 0.001, and intrinsic learning moti-
vation, b*= 0.59, SE = 0.06, p<0.001, with students with high perceived competence
expressing higher levels of the outcome variables. Moreover, passive procrastination
was negatively predicted by group, b*= –0.46, SE = 0.05, p< 0.001, indicating that
students with high perceived competence exhibited less passive procrastination than
students with low perceived competence.
3.2 Challenges, successes and need for support in distance learning
In the thematic analysis, five main themes emerged, reoccurring in all three open-
ended questions, but differing slightly in their individual subcategories. These main
themes were Contact with others (family/parents or guardians, peers and teachers),
Learning outcomes, Learning process, Contextual conditions and Well-being.The
category systems for all three questions were developed based on these main themes.
In the following section, narrative summaries of the main categories are provided
separately for each question. The absolute and relative numbers of coded segments
for the total sample as well as for each group and the according Chi-square tests can
be found in the supplementary material. Since only n= 235 answers were available
from the low perceived competence group (compared to n= 2417 answers in the high
perceived competence group), the number of coded segments was set in relation to
the number of segments coded in each group rather than to the overall number of
coded segments in the full sample.
3.2.1 Challenges in distance learning
Overall, 23.54% of segments didn’t report any particular challenges in distance
learning, with more such segments coded in the high perceived competence group
compared to the low perceived competence group. On the other hand, 71.98% of
coded segments concerned challenges students faced. Thereof, 0.74% were coded
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E. R. Pelikan et al.
Fig. 1 Overview over the differ-
ences in experienced challenges
during distance learning between
students who perceive them-
selves as high vs. low competent
with respect to the main cate-
gories in the qualitative analysis
as Everything feels challenging right now, with more such answers coded in the
low perceived competence group opposed to the high perceived competence group.
Challenges concerning the Learning process were coded for 23.26% of answers
(more often by the lacking competence group), whereas in 16.09% of the segments
difficulties in achieving the desired Learning outcomes were mentioned. Contextual
conditions and a lack of Contact with others were mentioned with approximately the
same frequency (15.36% and 15.16%, respectively). In 1.38% of the coded segments,
students reported that their physical and mental Well-being was challenged in the
current situation. A further 4.48% of segments couldn’t be coded in any content-
bearing category and were therefore assigned to the Residual category.TableIin
the supplementary material provides a quantitative summary of coded segments
(absolute and relative frequencies for main and subcategories) for all students as
well as separately with high vs. low perceived competence. An overview over the
differences between high vs. low perceived competence students regarding the main
categories is provided in Fig. 1.
Contact with others Within this category, lack of contact with or support from
teachers was mentioned most frequently by students with both high and low per-
ceived competence. Students reported that they missed face-to-face communication
with teachers in real life as well as online, stating thatthey often didn’t understand the
instructions and assignments and that they needed more explanations—particularly
when learning new content. Moreover, students stated that they had difficulty con-
tacting teachers and were getting no or delayed answers to their questions. In addi-
tion, both groups mentioned a lack of feedback from teachers. Consequently, they
didn’t know whether their performance was sufficient. Some students also indicated
that they would like to have more contact with their peers, either in order to support
each other in school-related efforts or simply because they missed their friends.
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
Whereas students lacking perceived competence often referred to feeling left alone
with the material (“That you can’t really ask anybody”) and not getting help in
general, only students in the high perceived competence group reported that their
parents and siblings couldn’t support them, either because they were busy or because
they lacked the skills to do so.
Learning outcomes Students from both groups indicated difficulties in achieving
learning outcomes, especially in terms of learning and understanding new topics.
In this context, challenges in specific subjects were often mentioned. Above all,
mathematics seemed to be particularly challenging. Some students stated that tasks
in non-core subjects distracted them from completing their work for more important
subjects like mathematics and that they wished that “music lessons, arts, cooking,
handicraft lessons etc. would currently simply be omitted or reduced”.
Learning process More segments related to the learning process were coded in the
low perceived competence group compared to the high perceived competence group.
Difficulties in organizing the learning process were reported most often. Whereas
a lack of daily structure was only of concern to a few students, a greater percentage
of segments addressed difficulties in keeping track of all tasks to be done, managing
tasks and time and adhering to deadlines. Difficulties concentrating and avoiding
distractions as well as a lack of motivation and (self-)discipline were mentioned
more often in the low perceived competence group. While learning independently
and without support was difficult for both groups, it was even more challenging for
students who perceived themselves as lacking competence.
Contextual conditions In terms of contextual conditions, challenging school-re-
lated requirements were mentioned most often by students, especially in the low
perceived competence group. Students reported that they had too many assignments
to do in too little time, with even more time pressure than during regular schooling
(“Many teachers assign many more tasks than one would normally manage in school
during this period”). Some students mentioned that their home learning environment
was suboptimal, as they had to share a room with siblings or were otherwise dis-
tracted while studying. More segments related to the digital learning environment
itself were coded in the high perceived competence group. Difficulties arose mainly
with respect to the learning platforms, either because there were too many different
platforms that had to be checked, running the risk of overlooking important informa-
tion or assignments, or because the platforms didn’t work. In addition, some students
reported technical issues due to malfunctioning soft- or hardware or because they
lacked the necessary technical equipment for digital learning (e.g., they had to share
a computer with another family member or had no access to a printer). Students
also mentioned that they didn’t have sufficient knowledge to accomplish all neces-
sary tasks (e.g., receiving and handing in digital assignments) and that they weren’t
accustomed to working in front of a screen for prolonged periods of time and were
easily distracted and exhausted, e.g., “Constantly looking at a screen (I have constant
headaches from it despite taking breaks).”
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E. R. Pelikan et al.
Well-being Challenges in maintaining one’s physical and mental well-being were
coded more frequently in the group of students who perceived themselves as less
competent, who mentioned dealing with general anxiety and uncertainty resulting
from the situation and struggling to maintain a healthy learning-life balance. Students
also indicated that being in close quarters with their family led to increased stress
and arguments. The lack of opportunities to move around was mentioned by a few
students.
3.2.2 Successes in distance learning
Overall, in 91.96% of coded segments, students reported that there was something
positive to gain in distance learning, with 13.90% stating that Everything is going
well right now. Notably, there was a large difference in coded segments between
those students who perceived themselves as low (0.50%) compared to high compe-
tent (14.76%). While 52.97% of coded segments in the lacking perceived compe-
tence group were assigned the code Nothing is going well, only 0.32% of the coded
segments in the highly perceived competent group were coded in this way (out of
a total of coded segments 3.48%). In more detail, success in terms of the Learning
process was the most frequently mentioned subcategory (36.29%), again with low
perceived competence students mentioning this category less often (9.41%) than
high perceived competence students (38.01%). The second most frequently coded
category concerned Success in Achieving learning outcomes (34.80%). Segments in
this category were coded less often in the low perceived competence group (20.79%)
than in the high perceived competence group (35.67%). Good Contextual conditions
were addressed in 4.64% of coded segments, with students who perceived them-
selves as lacking in perceived competence mentioning this form of success less often
(0.50%) than students who perceived themselves as highly competent (4.91%). The
remaining two categories, receiving support from and staying in Contact with others
(1.25%) and successfully maintaining one’s Well-being (1.07%) were mentioned al-
most exclusively by students who perceived themselves as highly competent, with
only 2 and 1 coded segment(s), respectively, in these categories found in the low
perceived competence group. For this question, 4.55% of the segments couldn’t be
coded in any content-bearing category and were assigned to the Residual category.
A quantitative summary of all coded segments is provided in Table II in the sup-
plementary material. Fig. 2gives an overview over the differences between students
who perceive themselves as highly vs. lacking competence with respect to the main
categories.
Contact with others Within this subcategory, staying in Contact with teachers and
receiving teacher support were coded most frequently and only in the high per-
ceived competence group. Students stated that they were able to contact teachers
and ask questions, enjoyed their (online) lessons and received timely (and often pos-
itive) feedback on assignments. Some students also addressed that they successfully
learned with peers (e.g., group work).
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
Fig. 2 Overview over the dif-
ference in experiences success
during distance learning between
students who perceive them-
selves as high vs. low competent
with respect to the main cate-
gories in the qualitative analysis
Learning outcomes Students from both groups mentioned that learning in specific
subjects or tasks was going well. Students who perceived themselves as highly
competent reported more frequently that they were successfully completing tasks
and assignments, noting that they were working more thoroughly and thus making
fewer mistakes or else being faster and more productive. Students from the highly
perceived competence also indicated that they were able to understand new material
and got better grades in distance learning; in contrast, only two segments in the low
perceived competence group fell within these subcategories.
Learning process Concerning their learning process, students who perceived them-
selves as lacking competence described fewer successes in learning independently
than students from the high perceived competence group. Although both groups
mentioned that they were successful at setting priorities and learning at their own
pace, students in the high perceived competence group indicated that they were more
self-reliant in their learning overall and even enjoyed learning independently (“I ac-
tually like doing things on my own”). Students in the low perceived competence
group indicated less often that they were successful in being organized, especially
with respect to managing their time and planning their tasks as well as adhering to
deadlines.
Contextual conditions With regard to the learning context, only one segment was
coded in the low perceived competence group, which addressed successfully working
online (“that I can do many online exercises or that some things are well explained
on YouTube”). Students from the high perceived competence group, in contrast,
indicated that they enjoyed the quiet learning environment at home and that they
felt successful in digital learning. They mentioned getting better at working on the
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E. R. Pelikan et al.
computer (“Handling of the computer has improved”) and taking advantage of the
benefits of technologies (“Writing texts on the computer, because it is much easier
to change things”).
Well-being Only one segment in the low perceived competence group was assigned
to this category (“The balance between breaks and learning”). A few students from
the high perceived competence group stated that they were successfully maintaining
their well-being, mainly by taking breaks while learning and maintaining a healthy
life-learning balance overall.
3.2.3 Need for support
Overall, in 55.56% of coded segments, students indicated that they needed support
in distance learning, of which 1.69% stated that Support is needed in everything.
The number of coded segments in relation to group size differed between students
who perceived themselves as high vs. lacking competence. In total, 40.94% of stu-
dents indicated that they needed no further support, either because they already
felt sufficiently supported or because they did not need any support at all. Notably,
only 2.20% of segments in the low perceived competence group were coded thusly,
compared to 46.61% in the high perceived competence group. The same gap can
be observed in almost all the main categories, specifically for need for support with
the Learning process (4.10%) as well as in the need for psychological Well-being
(0.20%) and Contact with others (6.31%). Only with respect achieving Learning
outcomes (40.02%) and in dealing with Contextual conditions did both groups ex-
pressed the same desire for further support (3.42%). Finally, 3.29% of the segments
couldn’t be coded in any content-bearing category and were assigned to the Residual
category. A quantitative summary of all segments can be found in Table III in the
supplementary material; a narrative summary is provided below. In Fig. 3the differ-
ences with respect to the main categories between students who perceive themselves
as high vs. low competent is provided.
Contact with others Very few segments referred to the desire for (more) contact
with or support from family, parents or guardians and peers; a few segments men-
tioned the need for contact with or support from teachers. Several students noted
that they would like to have regular face-to-face meetings (e.g., “my teachers whom
I can address directly in class”), in order to get clear instructions. Students from the
low perceived competence group particularly missed having a teacher who could
explain things to them (e.g., “Perhaps that the topics can be explained in more detail
than the book says”).
Learning outcomes In most of the segments within this category, students ex-
pressed a desire for support in learning specific subjects, especially mathematics but
also German and English, often with respect to learning new material (e.g., “It is
difficult to understand the new material”).
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
Fig. 3 Overview over the dif-
ferences in the need for support
during distance learning between
students who perceive them-
selves as high vs. low competent
with respect to the main cate-
gories in the qualitative analysis
Learning process Regarding their learning process, students particularly expressed
a need for support in staying organized, specifically in terms of keeping track of
their tasks and in managing their tasks and time. They also needed support in
maintaining motivation and self-discipline, stating, for example, that they would
like to have a “motivational trainer” to help them get started and finish their tasks.
This was mentioned more frequently in the low perceived competence group, which
indicates a greater need for support among students with low perceived competence.
Contextual conditions Both groups mentioned contextual conditions as an area
where they would need support, especially in regard to the digital learning envi-
ronment, where some students expressed that they would like to have help “when
dealing with the computer” in general, with “[...] finding your way around the plat-
forms” and with technical issues like their Internet connection (“Better and more
stable Internet”). Students also indicated that they needed help with too demanding
school requirements, hoping for “fewer tasks” and “more time” to accomplish them.
Well-being Need for help with psychological well-being was expressed only by
students in the low perceived competence group. A total of five segments were coded,
all referring to the desire to have less stress in general, needing help in dealing with
aggression and generally feeling lost and hopeless (“Through all the events, the goal
disappears in front of your eyes and you sometimes ask yourself what the point is
of going further, because at the moment everything seems hopeless”).
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E. R. Pelikan et al.
4 Discussion
The present study aimed to gain insight into how students coped with the unique
and challenging situation of distance learning during COVID-19 pandemic by in-
vestigating SRL, intrinsic motivation and passive procrastination. The large number
of participants in our study allowed us to examine subsamples of students who per-
ceived themselves as particularly high or low in competence. By complementing the
quantitative data with open-ended questions, we were able to investigate differing
underlying mechanisms in these two groups.
Our first research question addressed the differences in regard to self-regulated
learning, intrinsic learning motivation and passive procrastination between students
who perceived themselves as high vs low in competence. A quantitative approach
was used to answer this question. As expected, our results showed that students
with high perceived competence are better able to manage their time and tasks and
plan their goals, use metacognitive strategies more often and have higher intrinsic
motivation than students with low perceived competence. Our results are in line with
previous studies finding perceived competence to be positively related to various as-
pects of SRL, such as planning and goal setting, time management and metacognitive
strategies (Miller et al. 1993; Lüftenegger et al. 2012; Zimmerman and Martinez-
Pons 1986). Moreover, as hypothesized, students who perceived themselves as less
competent exhibited higher passive procrastination. This result is in accordance with
findings of Haghbin et al. (2012) and Brando-Garrido et al. (2020), who found that
perceived competence was negatively associated with procrastination in university
students. However, we expand these previous findings by examining a younger age
group in a distance learning setting.
In our second research question, we focused on challenges, successes and areas
where support is needed among students in this new learning situation. Again, we
were interested in differences between students who perceived themselves as high vs.
low in competence with respect to the current distance learning situation and if and
how different mechanismscame into play in these two groups. A qualitative approach
was taken to answer this research question. The thematic analysis of the open-ended
questions largely complemented the results from our first research question, however
students also mentioned new aspects (e.g., regarding their well-being) that weren’t
included in the quantitative analysis (e.g., regarding their well-being) and offer
additional insights into students’ experiences during distance learning.
In general, the qualitative analysis revealed that even though all students faced
similar challenges in distance learning, students who perceived themselves as highly
competent were better able to cope with the situation. For instance, whereas more
students from the low perceived competence group stated that everything is challeng-
ing right now, students who perceived themselves as highly competent mentioned
that everything is going well more frequently. Whereas both groups indicated sim-
ilar challenges regarding, for example, understanding specific tasks, subjects and
new material, students in the high perceived competence group more often reported
being successful with learning independently and even enjoying their increased self-
reliance. They also actively utilized the unique characteristics of distance learn-
ing (e.g., doing tasks on the computer) and more frequently reported getting better
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
grades than in traditional school settings, partly because they could learn at their
own pace and in their own time. Both groups acknowledge challenges with re-
spect to organizing their learning and had a particularly hard time keeping track
of tasks, managing their time and adhering to deadlines. However, students from
the high perceived competence group indicated that they were more successful in
dealing with these challenges, whereas low perceived competence students required
more support. These findings further support the importance of SRL strategies for
learning success and strengthen the results of our quantitative analysis, particularly
regarding planning and time management but also with respect to metacognitive
strategies such as monitoring goal attainment. Additionally, students from the low
perceived competence group reported less motivation and self-discipline, stating
that they needed support in starting and following through with tasks. This high-
lights the interplay between motivational and self-regulatory mechanisms in passive
procrastination (Klingsieck 2013; Steel 2007), which was also reflected in the mod-
erate to high correlations between the SRL-related scales, motivation and passive
procrastination.
Both groups described their lack of contact with others as challenging; they
missed their peers and required more opportunities for synchronized online teach-
ing. This is in line with theoretical approaches like self-determination theory (Deci
and Ryan 2008), where social relatedness as one of three basic psychological needs
is considered to be essential for students’ intrinsic motivation, learning engage-
ment and overall well-being. Moreover, students also had a hard time understanding
instructions, complained about having to wait for answers, and emphasized the im-
portance of direct teacher support. However, even if challenging, students in the high
perceived competence group more often reported being successful in maintaining
contact with teachers and peers. This is in line with the results of a study by Zimmer-
man and Martinez-Pons (1986), in which high-achieving students tended to utilize
social resources and assistance more readily than low-achieving students. Actively
seeking support may be a strategy primarily applied by students with high perceived
competence, even though such a strategy may only be successful if teachers and
other adults are available to respond. Consistent with the literature on antecedents
of online learning success, our findings emphasize the importance of social support
and integration (particularly teacher-student relations) in distance learning settings
(Borup et al. 2014;Weiner2003). Furthermore, Lock et al. (2017) propose that pur-
poseful planning is necessary to create a distance learning environment that fosters
SRL. However, due to the rapid implementation of school closures, teachers and
students had to adapt to the new situation within a very short amount of time. Our
results indicate that students who perceive themselves as highly competent might
be better able to develop the necessary SRL skills on their own, whereas students
from the low perceived competence group needed more support in learning how to
regulate their learning.
With respect to contextual challenges, both groups felt that there were too many
assignments to complete in too little time and that the amount of work had in-
creased, compared to regular school, especially in non-core subjects. While some
students reported that learning at their own pace enabled them to work diligently
and effectively, the wish for additional time and/or fewer assignments was expressed
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E. R. Pelikan et al.
particularly often by the low perceived competence group. This again emphasized
the importance of well-established communication and feedback systems, not only
from teacher to student but vice versa (Borup et al. 2014;Weiner2003), as well as
coordination among teachers so that the students’ cumulative workload for students
can be assessed accurately.
Finally, students from the low perceived competence group also had more diffi-
culty maintaining their physical and psychological well-being and stated that they
felt anxious in this highly uncertain situation. Although not the primary subject of
our study and therefore not included in the quantitative analysis, this finding coin-
cides with theories on the relations between perceived competence, autonomy and
well-being (Niemiec and Ryan 2009).
In summary, our study substantially contributes to current research as it under-
lines the importance of perceived competence for successfully coping in the diffi-
cult, stressful situation created by emergency distance learning. It draws attention
to the role of perceived competence for positive learning behaviors like the use
of SRL strategies and the avoidance of passive procrastination. Finally, perceived
competence also positively affects intrinsic motivation, which has been found to
be particularly important in distance learning settings. Furthermore, the qualitative
analysis emphasizes the importance of social support and contact with teachers as
well as peers, not only for developing important SRL skills but also for student’s
well-being.
4.1 Limitations and strengths of our study
Like all research, our study has several limitations. Due to the contact restrictions
during the COVID-19 pandemic, we had to rely on self-report measures via an on-
line questionnaire. We are fully aware that this approach comes with several caveats.
Firstly, self-reports are always contingent on participants answering candidly. How-
ever, since we assured full anonymity and our questions did not target particularly
sensitive topics, we are confident that students responded honestly. Secondly, data
collection via online questionnaires excludes some (high-risk) populations (e.g.,
those without internet access, those who are not sufficiently proficient in the German
language to understand our instructions and questions and those who have learning
disabilities). While this is a problem faced by many studies, it may be of greater
concern in this particular context (learning during COVID-19). We must assume
that our results are positively biased and that the differences between students who
perceive themselves as high in vs. lacking competence are probably graver in reality
than our study suggests. Thirdly, despite the wide reach of our questionnaire, we
didn’t collect data systematically but relied on various channels to promote partici-
pation in our study (see methods section). Therefore, we did not control the selection
of our sample regarding sociodemographic variables (e.g., our sample turned out to
be predominantly female). For these reasons, our study is not representative, and
our results cannot be generalized to the larger Austrian student population.
However, our study also has several valuable aspects. Firstly, the current situation
(almost all students in distance learning) allowed us to collect a very large sample,
which in turn enabled us to identify and work with data on actual extreme groups
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
while still retaining a considerable sample size. Moreover, supplementing the quan-
titative results with qualitative analysis provided deeper insights into the challenges
but also opportunities of distance learning in students’ own words and—most impor-
tantly—allowed us to collect concrete information about the areas in which students
express a need for support.
4.2 Implications for distance learning
Although the generalizability of our results is somewhat limited due to the cross-
sectional nature of the study and the sampling method, our findings underscore the
relevance of SRL in autonomous learning situations, such as distance learning. Fos-
tering SRL should therefore be made a priority in the physical classroom as well
as in online teaching. SRL can be supported in various ways, such as helping stu-
dents set goals and schedule their time or else supporting their monitoring by asking
prompting questions (e.g., Dignath and Büttner 2008; Dresel and Haugwitz 2008;
Stebner et al. 2020; Zimmerman and Martinez-Pons 1990). Helping students to set
and reach achievable goals, allows them to experience increased perceived compe-
tence, which in turn also boosts intrinsic motivation and learning success (Ryan and
Deci 2000). Introducing accountability partners may serve to foster social relations
between students, in addition to encouraging them to follow through on their plans.
In our study, students from both the high and low perceived competence groups ex-
pressed a desire for clear and comprehensive instructions for tasks and assignments.
Teachers should provide detailed instructions, offer explanations when necessary
and be available for questions if possible. In addition, the multitude of communica-
tion platforms used for e-learning assignments and the different delivery modalities
used by teachers overwhelmed and confused many students. Thus, better coordina-
tion among teachers regarding platforms but also deadlines and delivery intervals
may relieve some of the stress students reported. Additionally, providing timely and
respectful feedback can support students’ self-efficacy and motivation as well as
the teacher-student relationship (Wisniewski et al. 2020). Some students expressed
a need for social contact, which was particularly impaired during the COVID-19
lockdown. The need for social relatedness is identified as a basic psychological
need within social determination theory (Deci and Ryan 1993,2000,2008). There-
fore, enhancing social interaction by providing opportunities for synchronous (e.g.,
video conferences, virtual learning groups) as well as asynchronous (e.g., group
work and forum discussions) communication may promote distance learning suc-
cess (Broadbent and Poon 2015) and benefit students’ overall well-being (Ryan and
Deci 2000). However, online communication also has drawbacks and must be me-
diated by teachers to reach its positive potential (Rovai 2007). Finally, even though
digital learning has been complementing traditional learning for years now (Brand-
hofer et al. 2019; Huber et al. 2020), most teachers were not prepared for the abrupt
switch to distance learning that was necessary due to the COVID-19regulations (Hu-
ber et al. 2020; World education blog 2020). Online teaching is not merely about
transferring normal lessons into a digital environment; it requires unique skills that
both teachers and students need to develop (Christensen and Alexander 2020;Flores
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E. R. Pelikan et al.
2020;Quirozetal.2016). Immediate action should be taken to better prepare and
support teachers for distance and online education (Whalen 2020).
4.3 Conclusion and future direction
Although distance learning and in particular emergency distance learning in a crisis
such as COVID-19 present challenges for students as well as teachers and parents,
SRL skills and high intrinsic motivation may serve as protective factors and foster
not only learning success but also student’s well-being. Our findings are of partic-
ular interest in light of possible school closures in future crises but can also serve
to inform stakeholders seeking to develop effective concepts for successful future
blended or online learning.
Several areas of interest could be addressed in future research. Other important
actors (e.g., parents, teachers, school principals) should be approached to gain an
understanding of the distance learning situation from different perspectives. To gain
additional knowledge about the differentiating effects of subjects and the way lessons
and assignments are presented, further studies taking methods of delivery should be
conducted. Additionally, a longitudinal design would allow for insights into changes
in student’s perceived competence, self-regulated learning and motivation, providing
further information about the underlying mechanisms that influence distance learn-
ing success. Furthermore, our study focused on student’s subjectively perceived
competence as opposed to objective performance data. Future research could aim to
incorporate other measures of academic success (e.g., grades or achievement tests).
Finally, we assessed contact with others as a form of resource management in our
metacognition scale. However, the results of the qualitative analysis suggest that
contact with and support from teachers may play an essential role for SRL and in-
trinsic motivation and well-being, especially in distance learning. The role of social
integration and support should therefore be investigated in future studies on distance
learning.
Supplementary Information The online version of this article (https://doi.org/10.1007/s11618-021-
01002-x) contains supplementary material, which is available to authorized users.
Funding This work was funded by has been funded by the Vienna Science and Technology Fund
(WWTF), by MEGA Bildungsstiftung, and the City of Vienna through project COV20-025.
Funding Open access funding provided by University of Vienna.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.
0/.
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Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination...
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