College students’ motivation and study results after COVID-19 stay-at-home orders

Abstract

Due to the COVID-19 pandemic, many institutions of higher education had to close their campuses and shift to online education. Here, we investigate how stay-at-home orders impacted students. We investigated results obtained by 15,125 bachelor students at a large Dutch research university during a semester in which the campus was closed and all education had shifted online. Moreover, we surveyed 166 students of the bachelor of psychology program of the same university. Results showed that students rated online education as less satisfactory than campus-based education, and rated their own motivation as having gone down. This was reflected in a lower time investment: lectures and small-group meetings were attended less frequently, and student estimates of hours studied went down. Lower motivation predicted this drop in effort. Moreover, a drop in motivation was related to fewer credits being obtained during stay-at-home orders. However, on average students reported obtaining slightly more credits than before, which was indeed found in an analysis of administered credits. In a qualitative analysis of student comments, it was found that students missed social interactions, but reported being much more efficient during online education. It is concluded that whereas student satisfaction and motivation dropped during the shift to online education, increased efficiency meant results were not lower than they would normally have been.

Full text

College students’ motivation and study results after COVID-19 stay-at-
home orders
Meeter, M.*, Bele, T., Den Hartogh, C.F., Bakker, T., De Vries, R.E. & Plak, S.
Vrije Universiteit Amsterdam
* m.meeter@vu.nl
Abstract
Due to the COVID-19 pandemic, many institutions of higher education had to close their
campuses and shift to online education. Here, we investigate how stay-at-home orders
impacted students. We investigated results obtained by 15,125 bachelor students at a large
Dutch research university during a semester in which the campus was closed and all
education had shifted online. Moreover, we surveyed 166 students of the bachelor of
psychology program of the same university. Results showed that students rated online
education as less satisfactory than campus-based education, and rated their own motivation as
having gone down. This was reflected in a lower time investment: lectures and small-group
meetings were attended less frequently, and student estimates of hours studied went down.
Lower motivation predicted this drop in effort. Moreover, a drop in motivation was related to
fewer credits being obtained during stay-at-home orders. However, on average students
reported obtaining slightly more credits than before, which was indeed found in an analysis
of administered credits. In a qualitative analysis of student comments, it was found that
students missed social interactions, but reported being much more efficient during online
education. It is concluded that whereas student satisfaction and motivation dropped during
the shift to online education, increased efficiency meant results were not lower than they
would normally have been.

Introduction
Following the outbreak of the COVID-19 pandemic, many governments worldwide
introduced a lockdown to contain it. This entailed closing most non-essential businesses and
venues, and ordering people to stay at home. In most countries this included educational
institutions (e.g., Hirsch 2020; Crawford et al., 2020). Universities in the Netherlands and
elsewhere were abruptly forced to close their doors. Students and faculty personnel were
ordered to stay at home, which meant moving the lectures and other academic activities
from physical classrooms to an online environment using videoconferencing. Students had
to adapt in a short amount of time to a drastically changed situation. They could not attend
physical lectures or study and interact on campus.
Such drastic changes have a major impact on people. It is already clear that sudden
stay-at-home orders due to a pandemic like COVID-19 has consequences on people’s mental
health. For example, Tull et al. (2020) found that being under a stay-at-home order was
associated with greater levels of health anxiety, financial worry and loneliness. Similarly,
Gonzales-Sanguino et al. (2020) saw an increase of symptoms in depressive, anxiety and
post-traumatic stress disorders. These stay-at-home orders also puts a strain on student,
peer, and faculty interaction. It is not unlikely that this has consequences for students
beyond their mental wellbeing. For example, Goodenow (1993) found that a sense of
classroom belonging and teacher support is linked to motivation in early adolescence. This
makes it plausible that the consequences of COVID-19 and stay-at-home orders extend to
student’s motivation and their results as well. To date, there are very few studies of the
effects of sudden stay-at-home orders on university students’ motivation and results.
The aim of this study then, is to explore the relation between the sudden stay-at-
home order on university students’ motivation, effort, and results.
Theoretical framework
Motivation is an essential element in academic performance. High levels of motivation are
associated with good academic performance (Kusurkar, Ten Cate, Vos, Westers & Croiset,
2013). Highly motivated students tend to expend more effort in learning, leading to better
results (e.g., Gottfried, Marcoulidis, Gottfried & Oliver, 2013). Several factors in turn
underlie high motivation. In an influential model of student dropout, Tinto (1975) suggested
that academic integration, an attachment of the student to intellectual life of the college or
university, is a crucial factor in student retention. That is, the more students feel their
intellectual needs are met at college, and the more they identify with it, the more likely
students will persist (Tinto, 1975, see Tinto 1998).
A second factor in Tinto’s model is social integration, the social relations students have with
peers and faculty members. Without social integration, students find it more challenging to
persist in their studies. This is in line with self-determination theory, which posits that the
need for relatedness is a key precursor of motivation (Deci & Ryan, 2000). Indeed, students

who interact more with their peers and faculty members generally report higher satisfaction
and motivation than students who have less social interaction (Wentzel 2017, Trollian et al.,
2016).
Stay-at-home orders can be expected to strongly affect feelings of social integration, since
such orders lead to a strong reduction in social interactions with fellow students and faculty
members. This leads us to hypothesize that students with higher levels of social integration
will report a decrease in their academic motivation after stay-at-home orders. This may be
especially the case for students who score high on the character trait extraversion, who
tend to look forward to social interaction (e.g., Duffy et al, 2018). Social interaction may
have partly persisted online, though. Studies on Massive Open Online Courses (MOOCs)
have demonstrated that frequent online social interaction in the course is related to
learning engagement and MOOC completion (Fang et al, 2019; Sunar et al., 2016). We
therefore hypothesized that continuing social interaction during online educational activities
may buffer against a drop in motivation and effort.
There is less reason to believe that students’ experience of academic integration would
change due to a stay-at-home situation. Unlike social interaction, intellectual exchange may
still occur, albeit via different communication methods, and therefore identification may
remain unchanged. High levels of academic integration, on the other hand, should continue
to support academic persistence, even during a stay-at-home situation.
The decrease in social interaction was not the only change to occur with the stay-at-home
orders. The closure of university campuses also meant that education moved to a model of
distance education where students have to work more on their own, and plan and pace
themselves to a larger extent (Bol & Garner, 2011). How much of a burden this is may
depend on the resources available to students. Shapiro et al. (2017) examined experiences
of students in MOOCs, and identified lack of resources, such as poor internet connectivity,
as barriers to a positive MOOC experience. Similarly, whether students’ resources and
facilities are suited for online education may play an important role in students’ motivation
and effort after the stay-at-home orders.
Moreover, working at home may also place new demands on the skills of students. Students
with better self-regulation skills generally outperform their peers who possess less self-
regulation skills (for review see Duckworth, 2019). Such skills may be even more crucial in
distance education (Bol & Garner, 2011) and thus in the stay-at-home situation. In
particular, academic procrastination, which impairs academic performance in normal
college settings (e.g., Balkis, 2013; Kim & Seo, 2015), may be more strongly related to
performance during closure of universities, in which normal routines of lectures and
seminars stop being a trigger for study activities, thus leading to more opportunities for
procrastination. Furthermore, conscientiousness, which is a personality trait often found to
predict academic performance (e.g., Furnham, Nuygards, & Chamorro-Premuzic, 2013) may
be more crucial in situations that require working alone rather than studying on campus.
In summary, we believe that several factors may lead to a drop in academic motivation of
university students after stay-at-home orders.

 Most notably, the drop in social interaction may lead students relying on social
integration for their motivation to experience lower motivation after stay-at-home
orders. The same may hold for students scoring high on extraversion.
 We expect this lower motivation to translate into fewer hours spent studying and
lower academic results.
 Moreover, conscientiousness and procrastination may affect effort more strongly
after stay-at-home orders than before. Since low conscientiousness and high
tendency to procrastinate may make studying at home a more frustrating
experience, we also expect them to lead to a drop in motivation.
 Experiences during the lockdown, notably the social interaction that remains online
and the resources available to a student for online learning, may also affect
motivation.
The current study
Here, we investigated these hypotheses in a sample of graduate bachelor of psychology
students of a large Dutch research university. Students were asked to rate their motivation,
effort, and academic results during the stay-at-home orders, and compare them to the pre-
COVID-19 period. We then investigated whether these ratings were related to measures of
social and academic integration, procrastination, and personality traits. We analyzed
comments provided by students to find factors that they linked to motivation or
demotivation during online learning. In addition, we checked using administrative data
whether results of all bachelor students at the same university had suffered from lower
progress through their program during the crisis.
Methods
Participants
Students from three bachelor years of psychology were contacted. All 933 first-year, 244 of
503 second-year and 149 of 384 third-year bachelor of psychology students were contacted
(for second- and t hird-year students, only those were contacted that had given prior
consent to follow-up studies using their first-year personality data). A total of 166 students
participated (75% female, mean age 22). Of these, 88 were first-year students, 52 second-
year and 16 third-year participated in the study. Participation of first-year students was
incentivized with course credit. This incentive was not available for later years – for these
we organized a raffle for online store credit. All gave their informed consent before
participating. The survey was evaluated by a local ethics committee, while the analysis of
administrative data conformed to the Code of Conduct set by the university board for such
analyses. The study was performed in accordance with the Helsinki Declaration.

Materials
The following standardized questionnaires were used in the study and all were administered
via internet.
 The HEXACO-PI-R is a personality inventory consisting of 208 questions, measuring
the traits that form the HEXACO acronym, i.e., Honesty-humility, Emotionality,
eXtraversion, Agreeableness, Conscientiousness, and Openness to experience, each
with 32 items (plus 16 additional items that measure two interstitial traits, i.e.,
Altruism and Proactivity; De Vries et al., 2016; Lee & Ashton, 2006).
 The PASS (Solomon & Rothblum, 1984) is a 12-question procrastination
questionnaire.
 The Track and Field Social and Academic Integration Survey (TFSAIS; Lyons, 2007) is a
30-question inventory of experienced social and academic integration.
The Motivated strategies for learning questionnaire (MSLQ, Pintrich & de Groot, 1990) was
administered during the follow-up survey, but will not be reported since only a minority
participated in this follow-up survey.
A further self-developed questionnaire of COVID-19 related experiences was used to
measure
 academic results so far, and expectations for results during online education
 retrospective motivation and effort during standard university education
 motivation and effort expended during the experienced online education
 experiences within online education regarding online interaction and camera use
 facilities available to the student for online education
To be able to summarize these items in scales capturing online interaction, online camera
use, and home facilities for online education, we performed a principal component analysis
(PCA) on each set of items. Results are reported in Appendix 2.
The questionnaire also contained two open-ended questions: "What are for you motivating
and demotivating elements in online education?" and "What changes could psychology
make to make the online program work better for you?"
Procedure
All participants had filled out the HEXACO personality inventory for a first-year course. After
completing the HEXACO, students were asked whether they could be contacted for further
research using HEXACO scores. For first-year students, HEXACO administration occurred in
the same weeks with the current study, while for second- and third-year students it had
occurred in their first year. Students had to give their consent twice for HEXACO data to be
used in this study: once during the informed consent procedure of the current study and
once at the end of HEXACO administration. For second- and third-year students, only those
that had already consented at the end of HEXACO administration were contacted.

Students were sent an information email and asked to click on a link if they wanted to
participate. The survey was open from April 29 to May 15. They were then led to an
informed consent form that they could read at their ease. At the end of the form they were
asked for their consent with participation, and separately again for use of their HEXACO
scores. If they then consented to participation, they were further led to a Qualtrics
questionnaire that contained, in order, the COVID-19 related experiences questionnaire, the
TFSAIS and PASS. At the end of the questionnaire, students were asked whether they would
like to participate in a follow-up measurement a month later, and were asked to leave their
university ID code so that HEXACO scores could be linked to their other data, and so that
credits could be handed out (giving student ID was not mandatory).
In the follow-up survey, the COVID-19 related experiences questionnaire and the MSLQ
were presented to the participants. The follow-up survey was accessible from June 22 to
July 6 (which coincided with the end of the semester). Just 46 participants also responded to
the follow-up questionnaire. We therefore only report on the data from this follow-up
questionnaire in our checks on the consistency of the responses.
Statistical analyses of questionnaire data
Three dependent variables were of main interest:
 The difference between self-reported motivation during online education and
retrospective motivation during preceding education.
 The difference between self-reported effort during online education and
retrospective motivation during preceding standard education
 Self-reported credits obtained during online education
For all three, we fit a linear model with semester (pre- and post-Corona) as repeated factor
and as predictors HEXACO trait scores, PASS score, TFSAIS subscale scores (where we took
together interactions with students and faculty as social integration, and faculty concern
and academic development as academic integration), and experiences in online education.
Here, taking into account our scale analyses (reported in Appendix 2), we used online
interaction, having cameras on during online lectures (camera use), and two separate items
(‘quiet place to study’ and ‘good internet’) as predictors. Moreover, we used motivation and
motivation drop (post minus pre) as predictors for effort and results. We tested the
following hypotheses:
 For motivation, we expected main effects of conscientiousness, academic and social
integration. We expected an interaction with semester for social integration,
extraversion, online interaction, home facilities and PASS procrastination scores.
These interactions would reflect a drop in motivation in the online situation because
of a drop of social interactions (social integration, extraversion, online interaction,
camera use), and a stronger reliance on self-regulated learning the online situation
(home facilities and procrastination).
 For effort, we expected main effects of conscientiousness, of pre-COVID-19
motivation, of social and academic integration, and of procrastination (with negative

sign). We expected an interaction with semester for motivation drop, social
integration, extraversion, experiences of online interaction, home facilities and
procrastination. These interactions would reflect a drop in effort in the online
situation because of a drop of social interactions (social integration, extraversion,
experiences of online interaction), and a stronger reliance on self-regulated learning
in the online situation (home facilities and procrastination).
 For self-reported credits obtained, we expected main effects of effort and of
motivation, and an interaction with semester for motivation drop and the effort
drop. We thus expected the switch to online learning to affect results through
effects on motivation and effort.
Figure 1 presents a summary of all hypotheses.
Figure 1: Visualization of the hypotheses tested. Left: how included variables are
hypothesized to affect motivation, effort and obtained credits. Right: how the same variables
are hypothesized to affect changes in motivation, effort and credits during stay-at-home
orders. Abbreviations denote instruments used to measure particular variables. Green
arrows denote positive relations, red ones negative relations. Full-color arrows reflect
relations that were supported by the data, outlined ones relations that did not receive
support from the results.
Power analysis
For simplicity, a power analysis was performed using an ANOVA model with a within-
between factor interaction, an effect size of 0.15, a power of 80%, a .2 correlation between
measurements, and an alpha of 0.05. This yielded that at least 142 participants were
required for the study.

Qualitative analysis of open-ended questions
Most students volunteered one or more comments to the two open questions, resulting in
435 codable comments from 137 students. To analyze these, two research assistants coded
the first thirty via open coding, and then coalescing around a common set of codes. This set
of 14 codes was assessed by one author (MM), who checked the codes and how they were
applied to the set of 30 comments. After agreement, the research assistants each
independently coded 276 comments, of which 148 overlapped. The overlapping comments
were used to compute interrater agreement. Cohen’s kappa was computed to be 0.95,
which signifies very high agreement.
Analysis of administrative data
In parallel, we performed an analysis of registered student results for all bachelor’s
programs of Vrije Universiteit Amsterdam. Data on the progress of 15,125 bachelor students
in the spring semester of 2020 was compared to progress of 36,832 students in the spring
semesters of 2017, 2018 and 2019. Spring semester at Vrije Universiteit runs from February
to June, with exams scheduled at the end of March, May and June. All courses in the spring
semester of 2020 were thus affected by the stay-at-home orders, which started on March
12 in the Netherlands. We performed a regression analysis with second-semester course
grades (i.e., scores on individual course exams) as dependent measure, and as predictors:
 first-semester GPA,
 student controls; known student-level predictors of student progress (prior
qualifications, age, gender, and early vs late registration for the program),
 Whether stay-at-home orders were in place (coded 1 for 2020, 0 for the other years).
We refer to this predictor as COVID-19 semester.
We tested four models. Model 1 included just first-semester GPA and COVID-19 semester as
predictors. Model 2 added an interaction term between these two. Model 3 included the
student-controls but no interactions, and model 4 included both student controls and
interactions with COVID-19 semester. A total of 190421 exam scores was entered in the
analysis.
Results
Online education: Exploring student attendance and satisfaction
Figure 1 shows the means to questions related to meetings attended, satisfaction with
meetings, motivation, hours worked in addition to attending meetings, and credits that they
expected to obtain. During the COVID-19 crisis, students attended fewer small-group
meetings, t(160) = 7.51, p<.001, were less satisfied with lectures, t(150) = 6.51, p<.001, and
with small-group meetings, t(150) = 3.31, p = .001, felt less motivated, t(150) = 8.25, p<.001,

were less active in small-group meetings, t(150) = 5.50, p<.001 and spent fewer hours
studying, t(148) = 4.00, p<.001. There was a trend towards fewer lectures attended, t(160) =
1.88, p = .062. However, they expected to obtain more course credits during the COVID-19
crisis than the semester before, t(148) = 3.00, p = .003.
Figure 2 Average rating (on a scale from 1 to 7) for lectures and small-group meetings
attended, satisfaction with lectures and small-group meetings, self-rated academic
motivation, active participation in small-group meetings, hours worked in addition to
attending meetings, and expectation for attained credits, all assessed for the time period
during the COVID-19 crisis and retrospectively the period before. Error bars give the standard
error for the difference between the mean before and during the crisis (i.e., the within-
participant difference score).
Measurement checks
To ascertain that responses from students were consistent, we compared expected credits
in the second semester reported in the first questionnaire with those reported in the follow-
up questionnaire, when students had already obtained most credits. There was no
significant difference (mean 0.065 higher, t(45) = 0.573, p = .569). The two credit estimates
were correlated, although imperfectly so (r = 0.311, p = .035). To check the reliability and
stability of the retrospective measurements of motivation and effort, we computed
correlations between these items measured at the first questionnaire and the follow-up.
The correlation between the two ratings was high for both motivation (motivation before
the COVID-19 crisis r=0.61, during: r=0.59, both p < .001), and effort (before the crisis
r=0.73, during r=.81, both p < .001). The MSLQ administered only during the follow-up

questionnaire included an intrinsic motivation scale. We examined the correlation between
that scale and the follow-up questionnaire ratings of motivation before and during the
COVID-19 crisis. Both correlations were positive (Before crisis: r = 0.311, p = .038; during: r =
0.206, p = .174).
Models including HEXACO Data
Several hypotheses concerned personality traits, measured with the HEXACO. Due to
participants’ missing HEXACO data or non-consent to use this data this led to the loss of 41
participants who would otherwise have been included in the data set. We fit models
including HEXACO variables to the remaining 101 participants. Since most hypotheses
regarding personality traits were not supported, we here only report the parameters
involving HEXACO variables and below report results for the other variables from models
that did not include personality variables.
With regard to motivation, we did not find effects of Extraversion (b = -.004, se =
.115, p>.10) nor of Conscientiousness (b = .135, se = .090, p>.10). Extraversion did not, as
hypothesized, interact with semester (b = -.024, se = .146, p>.10). With regard to effort, we
did find that high conscientiousness was related to more effort (b = .203, se = .081, p =
.013), but there was no main effect of Extraversion (b = -.030, se = .088, p>.10), nor did it
interact with semester (b = .140, se = .090, p>.10).
Motivation
Out of the 166 participating students, 24 did not fill out one or multiple items of the
questionnaire relevant for testing our hypotheses regarding motivation. Hence, 142
participants were included in the analysis. To test all our hypotheses about main effects and
interaction effects on motivation, we fitted a linear model in R using the generalized least
squares (gls) function from the nlme package (version 3.1-149; Pinheiro et al., 2020) using
maximum likelihood estimation. This allowed us to treat the two motivation measurements
(retrospective before the COVID-19 crisis and during the COVID-19 crisis) as repeated
measures by accounting for this within-person correlation structure. All predictors were
centered.
The results of the motivation analysis are presented in Table 1. In line with our
expectations, we found a reduction in motivation as reflected by the main effect of
semester. Additionally, the expected interactions between semester and the home
environment variables suited computer and internet connection for online education, and a
quiet place to study at home were found. These results imply that students with a computer
and internet connection suited for online education and a quiet place to study had a less
steep motivation drop than students with worse facilities.
Contrary to our expectations, we did not find main effects for academic integration
or social integration. Furthermore, we did not find interaction effects between semester

(i.e., the drop in motivation) and social integration, online collaboration, online interaction,
frequency of online interaction, camera use, or procrastination.
Unexpectedly, we did find a main effect of online interaction on motivation.
Students who indicated there was a lot of interaction during their online meetings tended to
report higher motivation before the crisis hit, as compared to those who indicated not a lot
of interaction took place in their online meetings.
Table 1. Motivation Results: Coefficient Estimates and Standard Errors Given in Parentheses
(N = 142).
Coefficient (se)
Intercept 3.934*** (0.087)
Semester -0.892*** (0.110)
Social integration -0.042 (0.092)
Academic integration -0.142 (0.077)
Online collaboration -0.076 (0.089)
Online interaction 0.232** (0.087)
Frequency of online interaction -0.023 (0.089)
Online camera use -0.022 (0.094)
Computer and internet suited for online edu -0.036 (0.093)
Quiet place to study at home -0.029 (0.096)
Procrastination 0.150 (0.090)
Semester*Social integration 0.054 (0.111)
Semester*Online collaboration -0.011 (0.114)
Semester*Online interaction -0.091 (0.112)
Semester*Frequency of online interaction -0.144 (0.114)
Semester*Camera use 0.187 (0.120)
Semester*Computer and internet suited for online edu 0.292* (0.118)
Semester*Quiet place to study at home 0.242* (0.121)
Semester*Procrastination 0.069 (0.115)
Note. */**/*** denote significance at a 5/1/0.1 percent confidence level (two-sided).

Effort
We used the same estimation procedure as in the motivation analysis, with again 142
participants being included. The results of the effort analysis are presented in Table 2. In line
with our expectations, we found that the COVID-19 crisis reduced effort as reflected by the
main effect of semester. Additionally, we found that a stronger motivation was related to
more effort (main effect of motivation). As hypothesized, we found an interaction between
semester and motivation decrease, indicating that students whose motivation decreased
during the crisis also reduced their effort, while students with less of a drop in motivation
also reduced their effort less.
Against our expectations, we found no main effects for academic and social
integration. Moreover, we did not find interaction effects between semester and social
integration, online collaboration, online interaction, frequency of online interaction, camera
use, computer and internet suited for online education, quiet place to study, or
procrastination.
Table 2. Effort Results: Coefficient Estimates and Standard Errors Given in Parentheses (N =
142).
Coefficient (se)
Intercept 2.443*** (0.065)
Semester -0.340*** (0.066)
Motivation before 0.394*** (0.071)
Motivation decrease 0.070 (0.083)
Social integration 0.018 (0.070)
Academic integration -0.069 (0.066)
Online collaboration -0.004 (0.068)
Online interaction -0.023 (0.068)
Frequency of online interaction 0.114 (0.068)
Online camera use -0.131 (0.072)
Computer and internet suited for online edu -0.054 (0.072)
Quiet place to study at home 0.007 (0.073)
Procrastination 0.008 (0.069)
Semester*Motivation decrease -0.632*** (0.074)
Semester*Social integration -0.002 (0.066)

Semester*Online collaboration 0.067 (0.068)
Semester*Online interaction -0.038 (0.067)
Semester*Frequency of online interaction -0.032 (0.068)
Semester*Camera use 0.116 (0.071)
Semester*Computer and internet suited for online edu -0.134 (0.071)
Semester*Quiet place to study at home -0.027 (0.073)
Semester*Procrastination 0.116 (0.068)
Note. */**/*** denote significance at a 5/1/0.1 percent confidence level (two-sided).
Self-Reported Obtained Credits
Seventeen students did not fill out one or multiple items of the questionnaire relevant for
testing our hypotheses regarding obtained credits; 149 participants were thus included in
this analysis. We employed a similar estimation process as for motivation and effort.
The results regarding our hypotheses are displayed in Table 3. Contrary to our
hypotheses, we found a positive main effect of semester, indicating an increase in self-
reported course credits: Students expected to obtain more credits in the COVID-19 crisis
semester than they reported to have obtained in the first semester. In line with our
expectation, we found an interaction between drop in motivation and semester, indicating
that a higher drop in motivation was associated with less increase in credits.
Contrary to our expectations, we found no main effects for motivation or effort on
credits, and no interaction between the reduction in effort and the increase in self-reported
credits. Unexpectedly, we did find main effects for the motivation decrease, demonstrating
that a strong decrease in motivation is associated with higher self-reported credits in both
semesters and a high effort decrease is associated with lower self-reported credits in both
semesters. Since we have no evidence supporting one causal direction over the other, these
effects can also be interpreted as that motivation decreased more for students who obtain
many credits, while these same students reduced their effort less strongly during the
COVID-19 crisis than other students.
Table 3. Self-Reported Credits Results: Coefficient Estimates and Standard Errors Given in
Parentheses (N = 149).
Coefficient (se)
Intercept 2.593*** (0.068)
Semester 0.232** (0.076)

Motivation before Covid 0.046 (0.072)
Effort before Covid 0.106 (0.073)
Motivation decrease 0.193* (0.094)
Effort decrease 0.204* (0.092)
Semester*Motivation decrease -0.286** (0.098)
Semester*Effort decrease 0.071 (0.097)
Note. */**/*** denote significance at a 5/1/0.1 percent confidence level (two-sided).
Open-ended questions
Table 4 shows the nine categories used to code answers to the question of motivating and
demotivating elements in online education. For each of the codes, the number of comments
belonging to the category defined by the codes is given, and two sample answers. Answers
giving motivating elements could be clearly distinguished from demotivating elements
(denoted with ‘+’ or ‘-‘ in the table). Figure 3 shows the proportion of comments that either
described motivating or demotivating elements, and fell within each of the nine categories.
Table 4 categories in which comments to the question “What are for you motivating and
demotivating elements in online education?” were coded, with number of comments in the
category and for each two sample comments. Whether these were motivating (+) or
demotivating (-) was also coded.
Category N Motiv?
Example comments
Changed
organization
of education
41 + Tutors and lecturers do their best to make it work, most students
actively participate. (95)
- It's like you don't get education, it's just self-study (40)
Digital
discomforts
32 - Sometimes it doesn't work quite well (wifi, or not technical
professors) (50)
- Higher threshold to ask questions in lectures/tutorials is higher and
online proctoring for exams is stressful and invasive (122)
Distractions 29 + Better concentration (without friends around) (63)
- It is hard to concentrate when you are sitting infront of your laptop
the whole day. (77)
Personal
motives
37 + Motivating because I can finally live in my home town, with my
friends, family and boyfriend. That makes me happier and therefore,
more motivated. (118)
- I also find it more difficult to move enough during the day, as I don't
cycle to uni or walk from one lecture hall to the other. (110)
Planning 46 + No more travel time, so more time to study (57)

- Loss of structure and planning, i cant separate work time and free
time (93)
Social
interaction
43 - I miss the presence of other students in tutorials (online workgroups
make students more anonymous and sometimes things don't go
smoothly) (37)
+ Less formal, less social anxiety (100)
Stimulating
digital
education
26 + No more mandatory workgroups. I still attend the same amount of
workgroups but I don't grudge the fact that "I have to". And I can
listen and relisten to lectures and classes anytime I want (58)
+ Small assignments/projects which are sometimes in groups; (mostly)
regular schedule; zoom lectures with interaction (chat function,
menti, etc.) (69)
Freedom 23 + I can decide for myself when I want to work. (116)
- Tutorials aren’t mandatory and I don’t have to wake up and go to
lecture but only watch it when it suits me (and I somehow conclude
that is never) (42)
Other 9 + No public transport costs (63)
- Its boring (151)
Figure 3. Number of comments given to the question “What are for you motivating and
demotivating elements in online education?”, which fell within one of the nine categories
listed in Table 4. A student’s answer sometimes contained comments in multiple categories.
Comments giving demotivating element are labeled with “-“and colored with a red shade,

while comments giving motivating elements are labeled with “+” and colored with a green
shade.
In line with the quantitative survey results, more comments related to demotivating than to
motivating factors. Social interaction was the category that elicited most negative
comments. Most described a lack of social interaction due to the shutdown, such as “No
interaction/ not really seeing someone in real life”. A few comments either described that
seeing others was still motivating (e.g., “Motivation is to see classmates”), and one
comment (shown in Table 4) suggested that no social interaction was an improvement.
Many comments also fell into categories that described negative attitudes towards online
education – these were coded under changed organization of education if they referred to
the new forms of education or under digital discomfort when they described either
technical problems or a dislike of following an online lecture or seminar. Some comments in
these categories referred to specific, local issues, such as the scheduling of exams
(“Underlying stress, more exams in one week than used to”) or personal tools (“My bad
internet connection”). Most, however, were more generic and applied to online education
in general (see sample items in Table 4).
The two next-largest categories of demotivating elements referred to having to motivate
oneself. The category “distractions” contained comments about the ease of getting
distracted at home (see sample items in Table x), while the comments under freedom
mostly referred to too much freedom: having less structure, the ease with which lectures
can be skipped, but also seeing fellow students not participate (“Less people are present so
it is easier to not attend”).
The largest category of motivating elements related to planning. These mostly consisted of
time being saved by not having to commute, and being able to plan everything themselves
(“everything in own time”, “I watch all the lecutures [sic] when i want to / when i have
time”). Stimulating digital education consisted of elements of online education that were
appreciated (“Having good, fast and central access to all the study materials/lectures,
quality of recordings”; “Motivating elements is the breakout rooms”). Under personal
motivation, comments were coded that referred to personal reasons to be motivated or
not. Some of these were generic (“I want to learn as much as possible, looks good on my
CV”), others specific to the online situation (“What is also nice is that I sit in my own vibe; I
don't always like the vibe in the classroom, which tends to drain my energy”). Most
described motivating elements but some also demotivating elements (“I think study is the
only thing you can do now but I Miss studying in the university library”).
Comments elicited by the second question, about the changes the program could make,
were coded into five categories (see Table 5 for the number of comments in each category,
and sample comments). Most comment were either in the better organization or better
pedagogics category. In the first fell comments related to deadlines (either to strict or too
few, as in the example comment in Table 5), the number and pacing of meetings and exams,
and the provision of information to students. In the better pedagogics category comments

focused on more interaction in small groups e.g. “a bit more tutor-student interaction”, or
“support students to actively participate and encourage each other during the zoom
meetings.”), and about better use of assignments (“More group assignments and
discussions”). Quite a few students expressed that no change was needed, and that they
were satisfied (see sample comments in Table 5).
Comments in the online lectures category were for a large part calls for lectures to switch
from asynchronous, pre-recorded format to synchronous (“The lectures I've had since the
crisis were all recorded. I would prefer that the lectures were 'live', and that there was more
contact between students and professors.”). However, some students commented that they
liked the asynchronous format. Some comments were about the quality of the online
lectures (“Lecturers should follow a different approach of making videos for their students.
More short and concise videos in a higher frequency instead of translating a lecture to a
video”) r asked for increased speed (“My tutor/teacher speaks in a v e r y s l o w voice. If
there was an option to play the recorded lectures at 1.5x or 2x speed I would love that”).
Comments coded as other were calls for more social interaction or personal assessments of
online education (“The online program is fine but it just doesn't work for me. If uni is still
online for next semester, i'll most likely drop out”).
Table 5: categories in which comments to the question “What changes could psychology
make to make the online program work better for you?” were coded, with number of
comments in the category and for each two sample comments.
Category N Example comments
Better
organisation
42 More deadlines, because with ADD I need some structure and now there
just isn't really any. (48)
Less homework, spread out the exams. (89)
Better
pedagogics
48 for small group meetings, i would give other assignments. no
presentations via zoom. And tutors have to be well prepared so they can
use the time we have for the meeting. Sometimes the meetings take
more time than planned. (103)
Have more material on canvas with good descriptions on how to do
certain assignments (149)
No changes
needed
29 There isn’t really something Psychology can do since my problems are not
study related. (45)
I dont know, im pretty satisfied with the way things are (166)
Online
lectures
22 Maybe to have the normal lectures again on set times with participation
in a group instead of watching just videos (49)
For the recorded lectures: use a video format with a button to adjust the
speed since a lot of lecturers talk very slowly and I would save a lot of
time with increased speed. (126)
Other 8 Introduce ways to get to know your fellow students and provide options
to keep up social contacts between students (52)
Take into account that there is added stress and anxiety; (142)

Administrative data
To ascertain that students’ claims of obtaining normal or better-than-normal results and
more credits during the COVID-19 semester, we analyzed registered exam results stored
within the administrative data from the university. Table 6 summarizes the results from the
analyses. Most importantly, in all four models grades were higher in the semester in which
stay-at-home orders were in place than in comparable earlier years. Importantly, this effect
interacted with first-semester GPA, showing that especially students with lower first-
semester GPA (who tend to also receive lower grades in the second semester) did better
during the semester with COVID-19 than they would otherwise have. These results also held
when student controls were included in the model (models 3 and 4).
These results confirm the survey answers from the students. Follow-up analyses, not shown
here, confirmed that higher grades also resulted in more pass grades (i.e., more credits
obtained by the students), the results held for exams administered in March and in May,
though not those administered in June where results had apparently returned to normal
(p>0.05 for both COVID-19 semester and its interaction with 1st semester GPA).
Table 6. Results from the regression analyses performed on administrative data, with exam
grades as dependent variable and the listed variables as predictors. Reported parameters
are standardized regression coefficients (beta), with level of significance (***= p<.001).
Model 3 and 4 included student controls, model 2 and 4 interactions between COVID-19
semester (N=51,957).
Model 1 Model 2 Model 3 Model 4
COVID-19 semester 0.06*** 0.24*** 0.06*** 0.25***
1st -semester GPA 0.42*** 0.44*** 0.42*** 0.41***
Interaction COVID-19 sem. /1
st
sem.
GPA -0.19*** -0.18***
student-level controls
V v
BIC 631035 630940 629854 629940
# fitted parameters 4 5 32 61
Discussion
In line with our expectations, we found that students reported being less motivated than
before the COVID-19 pandemic. This drop in motivation was related to a drop in effort;
students reported spending less time on their studies than before, and attending fewer
lectures and small-group meetings. Nevertheless, self-reported obtained credits increased in
the COVID-19 semester compared to the semester before. This surprising increase was

indeed found in administration data. A smaller drop in motivation was related to a higher
increase in credits, but effort was unrelated to obtained credits.
In both their closed-question answers and in comments, students expressed an appreciation
for online lectures and small-group meetings that was lower than it was for offline ones. In
their written comments they described online education as lacking the social aspect, causing
discomfort because of technical failures, or just “not real education”.
A lack of social interaction was the largest category in students’ comments on factors that
negatively affected their motivation. Contrary to our expectations, however, we did not find
a relation between motivation on the one hand, and either social integration, extraversion
or social interactions during online education on the other hand. This lack of associations
between motivation and measures reflecting social interaction seems to contrast with
student’s comments that flagged lack of social interaction as a demotivating factor. One
explanation that may reconcile these findings is that lack of social interaction is
demotivating for all student equally, and not in stronger fashion for either students
reporting high levels of social integration beforehand or students high in extraversion.
Supporting this idea, while persons low on extraversion do not look forward to social
interactions as much as others, they enjoy them just as much when they do happen (Duffy
et al, 2018).
Another category of demotivating factors listed by students was digital discomforts- the
technical imperfects that mar online education. Indeed, the drop in motivation after stay-at-
home orders was associated with not having the resources suited for online education, such
as a quiet place to study and proper internet connectivity. Although the study design does
not allow for any causal claims, the results suggest students might benefit when provided
these resources. Students also often commented on technical formats, suggesting for
example that pre-recorded lectures could better be presented synchronously (i.e.,
broadcasted at some scheduled time, and only watchable then). However, it is fully possible
that if that had been the dominant pattern, similar numbers of comments would have
suggested switching to asynchronous presentation of lectures.
Students reported obtaining more credits (i.e., passing more exams) during the COVID-19
semester than in the first semester. This was supported by our analysis of administered
grades, which showed that grades were higher in the second semester of 2019-20, instead
of lower as hypothesized. At least one other study, although not yet peer-reviewed, has
found a positive effect of COVID-19 on results of university students in Spain (Gonzalez et al,
2020). Students’ comments suggested that online education was more efficient than typical
university education, with no need for transportation and fewer attendance rules in place.
Gonzalez et al. (2020) had access to digital traces in online learning systems, and reported
that students seemed to study more regularly than before.
These results contrast with those obtained in typical distance learning universities, which
tend to suffer from relatively high dropout rates, typically linked to a lack of social
integration (e.g., Gregori et al., 2018). Similarly, MOOCs tend to suffer from massive
dropout, which can be ameliorated through frequent online social interaction in the course

(Fang et al., 2019; Sunar et al., 2016). One possibility is that the social integration already
obtained before universities were forced to close their campuses (i.e., in Fall semester and
previous years) was sufficient to sustain successful learning in universities. The
improvement in results seen in the first months of the lockdown seemed to be wearing off
in June/July, when results returned to what is seen in other years. It is therefore an open
question whether this will be sustained in the future, when social relations fray through
continuing isolation.
Limitations
Several features limit the generalizability of the current results. First of all, the survey was
only performed after stay-at-home orders were in place. All ratings of standard education
were therefore retrospective, which can introduce well-known biases. Results from our
follow-up questionnaire showed that the ratings were stable, but this does not show that
they were unbiased. Moreover, motivation and effort ratings were self-report data, and in
that regard also open to, for example, social desirability biases.
Moreover, no data was available on typical differences between fall and spring semester. It
is possible that some drop in motivation occurs each year, and is not related to COVID-19 or
stay-at-home orders. However, our analysis of student comments would argue against this,
as students articulated factors clearly related to stay-at-home orders and online education
as motivating and demotivating factors.
Also, the sample was not very large. It is possible that our null findings reflect false negatives
more than truly absent effects. However, our power analysis suggests that the sample was
large enough to detect medium sized-to-large effects, suggesting that mostly small effects
would have been missed.
Finally, our survey data only included psychology students from one Dutch research
university (although our analysis of student results included all bachelor programs). Our
results would have to be replicated in other samples to obtain some generality.
Conclusion
University students appreciated online education less than they did traditional college
education, felt less motivated and reported spending fewer hours on their studies.
Decreases in motivation could be linked to less optimal facilities for online education, while
a lack of social interaction was a factor in dissatisfaction with online education.
Nevertheless, results did not suffer, they were even somewhat better than they would
otherwise have been. It remains to be seen whether results remain at a higher level with
continuing isolation.

Acknowledgements: the authors would like to thank Ambarien Suliman and Zeinab Oulel for
their help in coding student comments.
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Appendix 1: questionnaire Corona education
We start with questions on the current online study program. How often do you have
online tutorials, seminars or working group meetings per week? [we'll call these small-
group meetings from now on]
Do you usually have your camera on during online tutorials or working group meetings?
Do other students usually have their camera on during online small-group meetings?
How many students typically participate in online small-group meetings that you attend?
Before corona, what percentage of lectures did you approximately attend?
Now in the crisis, what percentage of online lectures do you follow?
Before corona, what percentage of small-group meetings did you attend?
Now in the crisis, what percentage of online small-group meetings do you attend?
Before corona, there was a lot of interaction in small-group meetings
Now in the crisis, there is a lot of interaction during online small-group meetings
Before corona, I often actively participated in small-group meetings
Now in the crisis, I often actively participate in small-group meetings
Now in the crisis, I feel less motivated to participate when others have turned off their
camera
Before corona, I was satisfied with most lectures I received
Now in the crisis, I am satisfied with most lectures I follow
Before corona, I was satisfied with most small-group meetings I attended
Now in the crisis, I am satisfied with most small-group meetings I attend.
Before corona, I was highly motivated for my studies
Now in the crisis, I am highly motivated for my studies
Before corona, my tutor / small-group leader expected a lot of work from me
Now in the crisis, my tutor / small-group leader expects a lot of work from me
Now in the crisis, there are many assignments on which we have to collaborate during
small-group meetings
During small-group meetings, we are often separated into small breakout groups to
collaborate or discuss
How many hours per week do you typically spend on your studies? (including time
attending lectures and small-group meetings - Before corona
How many hours per week do you typically spend on your studies? (including time
attending lectures and small-group meetings - Now in corona
How many people live at the place where you now reside during the work week?
Do you currently reside in the Netherlands?
Are your computer and internet connection well-suited for online education?
Do you have a quiet place to study and work at your home?
Will you pass the courses you are currently following?
How many course credits did you earn in the first semester (so Sept-Jan - regular is 30)
How many course credits do you intend to earn in the second semester (so Feb-June -
regular is 30)
What are for you motivating and demotivating elements in online education?
What changes could psychology make to make the online program work better for you?

Appendix 2: Scale Analyses
Online Interaction
Four statements were intended to measure online interaction: “How often do you have
online tutorials, seminars or working group meetings per week?”, “Now in the crisis, there is
a lot of interaction during online small-group meetings”, “Now in the crisis, there are many
assignments on which we have to collaborate during small-group meetings” and “During
small-group meetings, we are often separated into small breakout groups to collaborate or
discuss”.
The PCA results of the online interaction scale are presented in Table S1. Only the
first component has an eigenvalue above 1, implying that according to the Kaiser criterion
only that component should be retained. However, the first component explains less than
half of the total variance in the variables, which we consider too low. Based on the
eigenvalues and a cumulative explained variance of higher than 70%, we retained the first
three components.
Table S.1. Eigenvalues, Explained Variance, and Cumulative Explained Variance for the Four
Principal Components of Online Interaction Scale.
Eigenvalue % Variance explained Cumulative % explained
Component 1 1.791 44.769 44.769
Component 2 0.897 22.422 67.191
Component 3 0.855 21.386 88.577
Component 4 0.457 11.423 100
The loadings of the four items on the three components are presented in Table S2.
The bottom two items load most strongly on the first component. Given the overlap in the
content of these two items, we interpret the first component to reflect ‘online
collaboration’. The second item loads most strongly on the second component, which thus
reflects ‘online interaction’ during meetings. Lastly, the first item loads most strongly on the
third component, which therefore reflects the ‘frequency of online interaction’.
Table S.2 Loadings of the Four Online Interaction Items on the Components.
Component 1 Component 2 Component 3
How often online meetings -0.385 0.423 -0.818
Lot of interaction -0.346 -0.890 -0.298
Many collaboration
assignments
-0.599 0.102 0.389
Often separated into small
groups to collaborate
-0.611 0.136 0.302

Online Camera Use
Two items aimed to measure online camera use: “Do you usually have your camera on
during online tutorials or working group meetings?” and “Do other students usually have
their camera on during online small-group meetings?”.
The results of a PCA on these two items revealed that the first component with an
eigenvalue of 1.432 explained 71.59% of the total variance. We consider this an acceptable
large percentage, and therefore combined these two items into one ‘online camera use’
score.
Home Facilities Suited for Online Education
Two items aimed to measure whether students’ home facilities were suited for online
education: “Are your computer and internet connection well-suited for online education?”
and “Do you have a quiet place to study and work at your home?”.
The results of a PCA on these two items demonstrated a first component with an
eigenvalue of 1.303, and a second component with an eigenvalue of 0.696. Furthermore,
the first component explained 65.18% of the total variance. Based on this explained
variance, we decided to not reduce these two items into one score, because we would lose
a considerable part of the information captured by the two original items. Hence, the
original items were included in our analyses.