Educational Gains of In-Person vs. Distance Learning in Primary and Secondary Schools: A Natural Experiment During the COVID-19 Pandemic School Closures in Switzerland

Abstract

Using data from a computer-based formative feedback system, we compare learning gains in the eight weeks of school closures related to the COVID-19 pandemic in Switzerland with learning gains in the eight weeks before these school closures. The school performance in mathematics and language of N = 28,685 pupils is modelled in second-order piecewise latent growth models with strict measurement invariance for the two periods under investigation. While secondary school pupils remain largely unaffected by the school closures in terms of learning gains, for primary school pupils learning slows down, and at the same time interindividual variance in learning gains increases. Distance learning arrangements seem an effective means to substitute for in-person learning, at least in an emergency situation, but not all pupils benefit to the same degree.

Full text

COVID-19 SCHOOL CLOSURES - 1
Educational Gains of In-Person vs. Distance Learning in Primary and Secondary
Schools: A Natural Experiment During the COVID-19 Pandemic School Closures
in Switzerland
Martin J. Tomasik
University of Zurich, Switzerland, and University of Witten-Herdecke, Germany
Laura A. Helbling and Urs Moser
University of Zurich, Switzerland
Corresponding Author
Dr Martin J. Tomasik
Institut für Bildungsevaluation, Wilfriedstrasse 15, 8032 Zurich, Switzerland
Tel.: +41 (78) 904 01 36
Email: martin.tomasik@ibe.uzh.ch
Statement of Authors’ Contribution
MT developed the concept of the study, drafted the manuscript and was responsible
for the statistical hypothesis testing; LH prepared the data and supported MT in the
data analyses; UM developed the computer-based formative feedback system,
provided the data, and revised the manuscript.

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Abstract
Using data from a computer-based formative feedback system, we compare learning
gains in the eight weeks of school closures related to the COVID-19 pandemic in
Switzerland with learning gains in the eight weeks before these school closures. The
school performance in mathematics and language of N = 28,685 pupils is modelled in
second-order piecewise latent growth models with strict measurement invariance for
the two periods under investigation. While secondary school pupils remain largely
unaffected by the school closures in terms of learning gains, for primary school
pupils learning slows down, and at the same time interindividual variance in learning
gains increases. Distance learning arrangements seem an effective means to
substitute for in-person learning, at least in an emergency situation, but not all pupils
benefit to the same degree.
Keywords: COVID-19; distance learning; learning progress; school achievement;
school closures

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Educational Gains of In-Person vs. Distance Learning in Primary and Secondary
Schools: A Natural Experiment During the COVID-19 Pandemic School Closures
in Switzerland
In an attempt to contain the spread of the COVID-19 virus, most governments around
the world have temporarily closed schools and other educational institutions,
affecting more than 1.2 billion pupils and students or almost three-quarters of the
learner population (United Nations Sustainable Development Group, 2020). Learners
are probably the single largest group to experience the pandemic’s indirect effects.
Some researchers and organisations (e.g., Burgess & Sievertsen, 2020; Education
Endowment Foundation [EEF], 2020; Kuhfeld et al., 2020) have projected that school
closures during the pandemic could have detrimental effects on learning gains and
social disparities in learning. To the best of our knowledge, there is no empirical
evidence yet on the school closure effect’s actual direction and size. There is mainly
more or less substantiated speculation, as educational researchers, like many other
stakeholders, were unprepared for and overwhelmed by the situation. Few expected
the pace and scope of the pandemic’s development in Spring 2020.
In this paper, we analyse a coincidentally ongoing data collection to provide
timely empirical evidence on the impact of distance learning in schools. If there was
an effect of the school closures on learning and if this effect was sufficiently large to
be reliably measured during the relatively short period of time, this would not only be
relevant on its own or helpful to identifying pupils at-risk that would require special
attention in the case of another school closure potentially to follow. We also know
that educational achievement can have cascading effects into other developmental
domains such as employment or health and affect other developmental outcomes

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such income or civic engagement – even years later. Of course, we cannot provide
any evidence on these long-term effects with the data at hand. However, we wanted
to mention them in order to indicate the potentially broad impact of the findings for
the individual and the society as a whole. Because education is correlated with
virtually every psychological trait and because it moderates many psychological
processes, this paper is meant as a more general contribution to the broader
discussion on the psychological effects of the pandemic.
Pandemic Policy on Education in Switzerland
In Switzerland, all educational institutions closed on 16 March 2020 and
reopened again on 11 May 2020. During these eight weeks, schools virtually
overnight switched to distance learning. It is safe to assume that most pupils, parents
and teachers were unprepared for the situation. The school closures were
announced at very short notice on the Friday afternoon before taking effect the
following Monday. School authorities at first did not provide any guidance for parents
and teachers on how to deal with the situation’s challenges, resulting in high
uncertainty on all sides. In the first days, for instance, it was not clear whether pupils
were allowed to pick up their workbooks that they had left in school. By the end of
March, however, school authorities had put together and distributed information on
best practice in distance instruction, and many EdTech companies had made their
distance learning applications available to schools. Moreover, decisions had been
made on how to handle attendance, grading and progression in the second half of
the school year. While the school closures were mandated by federal directive, the
definition of specific regulations and their implementation in school practice were
decided and supervised by the 26 individual cantons. This makes it difficult to

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summarise them in brief. In most cantons, grading was suspended sooner or later,
and special regulations came into effect regarding attendance and progression.
Pupils often received no grades until the end of the school year, even after schools
reopened. Many pupils therefore received only a pro forma school report at the end
of the 2019/20 school year.
Potential Impact of School Closures on Educational Gains
Although the situation leading to the school closures in the second term of the
2019/20 school year and its scope are unprecedented in recent history, studies on
school non-attendance for reasons other than a pandemic may be informative for
estimating the effects of school closures and the rapid transition to distance learning
due to COVID-19. In reviewing these studies, we orientate ourselves by the few
highly topical works that have been published in a remarkably short time: the
projection study by Kuhfeld et al. (2020), the short article by Burgess and Sievertsen
(2020) and the meta-analysis by the EEF (2020). In all three publications, based on
previous research, the authors expect that the school closures will have an impact on
learning gains, although they disagree on its order of magnitude. Notably, none of
these papers analyse data collected during the actual pandemic. Evidence is cited
from studies on seasonal learning and school closures during natural disasters,
comparative studies on instructional time and school absenteeism studies.
Seasonal Learning Studies
There is a long tradition of studies scrutinising the institutional effects on
learning from the learning loss during regular school vacations. Early works on
summer learning loss, summarised and meta-analysed, for instance, by Cooper et al.
(1996) suggest not only that achievement declines to an extent equivalent to one

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month of school learning but also that there are social disparities in this effect that
contribute to a growing heterogeneity in achievement. More recent studies, however,
question not only the summer learning loss but also the social disparity effect (e.g.,
von Hippel & Hamrock, 2019) and argue with the methodological issues related to
scaling, the use of different test forms, the choice between a regressor variable
model or a change score model and the way of modelling measurement error,
asserting that all these issues can jeopardise the valid interpretation of findings from
studies on learning loss. In any case, a large variance of effect sizes is reported, with
estimates ranging from no loss at all up to d = .010 per vacation day. Extrapolating
these figures to the eight weeks or 40 days of school closures during the COVID-19
pandemic would result in learning losses of .000 ≤ d ≤ .400.
Comparative Studies on Instructional Time
As Burgess and Sievertsen (2020) argue, studies investigating differences in
instructional time on educational outcomes might be informative for estimating the
impact of school closures related to COVID-19. The authors cite as exemplars two
such studies that try to establish causality by investigating dosage-response patterns.
In the study by Carlson et al. (2015), Swedish males took a battery of cognitive tests
in preparation for military service that randomly varied in date and hence in the time
for preparation in school. In this study, just ten days of extra schooling raised the
scores of the recruits on the crystallised intelligence test by d = .010. Lavy (2015)
took another approach by explaining international achievement gaps found in the
Programme for International Student Assessments by differences in schools’
instruction time in the various countries. He found that one more hour per week in
the main subjects increased test scores by about d = .060. Of course, this correlative

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study can only statistically control for all the other differences that exist between the
educational systems in the various countries. Taking these two effect sizes and
extrapolating them to the 40 days of school closures in Switzerland would result in a
learning loss of only d = .040.
Studies on School Absenteeism
Another strand of research relevant in the present context deals with school
absenteeism and compares the learning progress of those who attend school with
the learning progress of those who miss some lessons, hence also applying a
dosage-response paradigm. There are numerous reasons why pupils do not attend
school, including their own illness or lack of access to reliable transportation. Minority
status and low family income are also important correlates for school absenteeism.
Research regularly reports a linear association between the number of days missed
in school and end-of-year test scores, although the range of effect sizes reported
tends to be large. While Aucejo and Romano (2016), Gershenson et al. (2017) and
Liu et al. (2019) consistently report effect sizes of .006 ≤ d ≤ .008 for each school day
missed, Goodman (2014) finds that one single day of absence reduces the pupils’
mathematics scores by as much as d = .050. Extrapolating this to the 40 days of
school closures during the COVID-19 pandemic would amount to a learning loss of
.240 ≤ d ≤ 2.000.
School Closures During Natural Disasters
Research on school closures due to natural disasters is less frequent, as these
events rarely occur. Kuhfeld and colleagues (2020) identify three recent studies that
observe the impact of severe weather events on learning. Hansen (2011) found that
each day of school closures due to snow in Colorado reduced achievement by .013 ≤

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d ≤ .039. Goodman (2014), analysing the effects of school closures due to snow in
Massachusetts, found a comparable effect only for poor schools. The third study by
Sacerdote (2012), which is not directly comparable in terms of the effect size,
investigated learning loss in the aftermath of hurricane Katrina and found an overall
effect of d = .100 due to displacement. Extrapolating the effect sizes of the snow
studies to the 40-day school closures during the COVID-19 pandemic would yield
effect sizes of .510 ≤ d ≤ 1.560, which is much larger than the overall effect reported
for hurricane Katrina.
Generalisability of Existing Evidence to School Closures During COVID-19
The generalisability of all four types of studies to the situation during the
COVID-19 pandemic is somewhat limited for several reasons. Evidence from
seasonal learning studies has the advantage of relying on numerous studies and
sometimes very large samples. However, school vacations are predictable events
that do not resemble sudden school closures. Parents and their children can prepare
and often spend at least some vacation time together, especially when the children
are still young. Comparative studies on instructional time are useful to estimate the
effect of schooling but hardly control for cultural, curricular and other differences
between single countries, so a number of alternative explanations are possible.
Evidence from studies on school absenteeism probably offers the most direct way of
studying institutional influence on learning, and at the same time large control groups
of pupils attending school are available. However, a high degree of self-selection and
low equivalence between attending and absent pupils on virtually all behavioural,
cognitive and socio-demographic variables threaten the valid interpretation of the
results as effects of schooling. Evidence from studies on school closures during

COVID-19 SCHOOL CLOSURES - 9
natural disasters is probably best suited to generalise to the COVID-19 pandemic
situation because such events usually occur suddenly, allow for little if any
preparation (in contrast to seasonal learning studies), are locally confined, which
limits the influence of cultural factors (in contrast to studies on instructional times),
affect every single pupil and hence reduce self-selection bias (in contrast to studies
on absenteeism). However, fortunately, such disasters are rare as is empirical
evidence from large samples. Furthermore, the educational situation is different, as
no systematic distance learning has so far been implemented as a means to
compensate for in-person learning. All these reasons, together with the
methodological uncertainties and the broad range of extrapolated effect size
estimates (.000 ≤ d ≤ 2.000), make it extraordinarily difficult to provide reliable point
estimates for the actual effect of the COVID-19 school closures.
Another substantial factor that significantly limits the comparability of existing
studies—perhaps with the notable exception of some of those related to natural
disasters—is that the COVID-19 pandemic brought about a high degree of
psychological uncertainty for parents and their children, confronted many families
with social, emotional and economic strain, and compromised mental health in the
general population (e.g., Serafini et al., 2020). Many working parents had to educate
and care for their children besides meeting their jobs’ old and new demands. Existing
social ties, for instance those to grandparents and other supportive networks, were
disrupted. The risks of income or job loss during one of the major economic
downturns in recent history together with tangible health threats were ubiquitous in
many families. It is well known that such strains immediately translate from the distal
to the proximal developmental contexts of individuals (Tomasik & Silbereisen, 2016)

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and can challenge the functioning of families, particularly those who are most
vulnerable (Elder & Caspi, 1988; Silbereisen & Tomasik, 2011). All these
considerations suggest that the effect of the school closures due to the COVID-19
pandemic could be larger than in previous studies and produce more heterogeneity
in the learning trajectories.
At the same time, learning continued (see Chamberlain et al., in press) and
the institutional impact of schooling was not totally diminished during the COVID-19
school closures. On the contrary, teachers, in collaboration with the school
authorities, quickly implemented digital forms of distance learning. It is possible that
some teachers were incapable, unmotivated or both to develop pedagogically
effective instruction in the digital space (see also Iivari et al., in press). Research from
the United States suggests that a concerning number of teachers lost contact with
their pupils and did not interact with their pupils on a daily basis (Lieberman, 2020).
This is consistent with models of instruction in which the coordination of pupils is the
central challenge that teachers face. However, instruction continued, albeit under
very different and sometimes quite demanding conditions (see also Basilaia &
Kvavadze, 2020).
Present Study
The unique situation of school closures related to the COVID-19 pandemic
enables testing the effects of in-person vs. distance learning in an unselected sample
to estimate the potential loss in learning progress that could have occurred during
this time. Two competing hypotheses can be scrutinised. On the one hand, one
could assume that the lack of institutional schooling results in slower progress or
even a decline in competence. This effect should be particularly pronounced for at-

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risk pupils, such as those from disadvantaged social backgrounds, those with
learning difficulties or very young learners (see Cooper et al., 1996; Lee, 2020). On
the other hand, one could argue that the institutional influence was not totally
diminished and that learning took place in another form and maybe even at the same
pace. Some studies even suggest that distance learning, if implemented well, can
have advantages compared to traditional classes (e.g., Allen et al., 2006), although
other studies report a negative impact of distance education (e.g., Ahn & McEachin,
2017). In the given emergency situation, one would not assume that distance
learning would outperform in-person learning. However, advantages and
disadvantages could be more or less balanced. The current pandemic situation offers
a unique natural experiment to test these competing hypotheses by comparing
learning progress before and during the school closures.
Even if the potentially negative effects of the school closures and the
potentially positive effects of distance learning balance out or if the time intervals for
observing any positive or negative effects are too small, one would nevertheless
expect the variance of educational gains to significantly increase during school
closures compared to regular in-person learning. As the institutional influence of
school decreases, the effect of the family environment tends to increase, introducing
another source of variance in learning. Furthermore, while some teachers and pupils
might have found themselves completely unprepared for online distance learning,
others might have already been using digital tools in their classrooms for some time.
Teachers, on the one hand, not only differed in their experience but also in their
motivation for using distance learning arrangements. Pupils, on the other hand,
differed in their personal characteristics, such as age or self-regulatory learning skills,

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and found themselves in different contexts that made distance learning more or less
effective. While some pupils were very well equipped at home and supported by their
parents, others lacked basic material such as a working personal computer or their
own desk or had to deal with dysfunctional family arrangements. Social background
seems to play a pivotal role for support provided by schools, as surveys conducted
with parents (Andrew at al., 2020) or teachers (Cullinane & Montacute, 2020)
suggest. All these considerations are consistent with surveys from the UK that found,
for instance, a huge variance in the proportion of students getting involved in school
work at home as a function of the level of deprivation (Lucas et al., 2020). Taken
together, one might assume that the variance in learning gains during the eight
weeks of the school closures would be larger than the variance in learning gains
during the eight weeks of regular in-person school attendance (see also van Lancker
& Parolin, 2020).
We also hypothesised that the impact of the school closures would be more
profound for younger compared to older pupils, as their capabilities for self-regulated
distance learning are less developed, they require more cognitive scaffolding during
learning and are probably more severely affected by the socioemotional strains that
the COVID-19 pandemic brought about for families, as previous research on the
effects of macrostructural disruption (e.g., Elder & Caspi, 1988) suggests. Also,
limited physical activity due to restrictions requiring physical distancing, limited
community interactions, as well as sports facilities, playground, and park use (Moore
et al., 2020) might be particularly detrimental for the younger children.
Methods
Participants and Procedure

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All active users of the MINDSTEPS system who completed at least one
teacher-generated assessment between 19 January 2020 and 11 May 2020 were
included in the statistical analyses. MINDSTEPS is a computer-based formative
feedback system developed at the Institute for Educational Evaluation in Zurich and
deployed to all pupils in the cantons of Aargau, Basel-Stadt, Basel-Landschaft and
Solothurn and to many pupils from other German-speaking cantons of Switzerland. It
serves pupils from grade 3 to grade 9 and covers the subjects of mathematics,
German (the instructional language), English and French (the two foreign languages
taught at schools), according to the official school curriculum. The system allows
teachers to set up both linear and adaptive assessments as well as pupils to practice
ad lib in these subjects. For the present analyses, we focused on teacher-generated
assessments only in mathematics and German (in the reading comprehension and
grammar domains) because instruction in these two subjects is most aligned
between the single cantons. There were almost 15,000 items in mathematics and
almost 10,000 items in German that could have been used to set up the single
assessments. Although the system was originally developed to provide formative
feedback to teachers and pupils, it can also be used to obtain ability estimates of
pupils over time. Details of the system’s theoretical rationale can be found in Tomasik
et al. (2018). König et al. (2020) provide a hands-on demonstration of the system’s
capabilities. A total of N = 28,685 pupils (N = 13,134 in primary school and N =
15,551 in secondary school) were considered in the following analyses, although this
sample size cannot be compared to a traditional study design, as the data are
relatively sparse both between domains and over time. The numbers of pupils and
assessments are summarised in Table 1. On average, pupils were M = 9.20 (SD =

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1.69) years old in grade 3, M = 10.20 (SD = 1.46) years old in grade 4, M = 11.21
(SD = 0.82) years old in grade 5, M = 12.20 (SD = 1.55) years old in grade 6, M =
13.17 (SD = 1.93) years old in grade 7, M = 14.49 (SD = .90) years old in grade 8,
and M = 15.39 (SD = 1.63) years old in grade 9. There were 50.3% boys and 30.8%
non-native speakers in primary school as well as 49.1% boys and 32.7% non-native
speakers in secondary school.
We divided the eight weeks before the school closures and the eight weeks
during the school closures into eight intervals of 14 days. For each interval and each
domain, we obtained the WLE ability estimates based on all items completed during
that time. For WLE estimation, we used grade-by-grade one-parameter logistic
models based on probabilistic measurement theory that were then vertically linked
using the Stocking-Lord equation method. All the item parameters needed for this
procedure were previously obtained from a much larger sample collected between
September 2017 and February 2020 and fixed during WLE estimation. Berger et al.
(2019) provide a justification of this procedure together with a validation of the item
parameters obtained on the curriculum contents for mathematics.
Statistical Modelling Approach
School achievement was operationalised with eight latent factors, each
representing one of the eight intervals, four before and four during the school
closures. Within each interval, we used the respective three ability estimates in the
three domains as manifest indicators of latent school achievement. Using these eight
latent factors, we set up a multigroup second-order piecewise latent growth model
(see Isiordia & Ferrer, 2018) with a joint intercept for the two periods under
investigation but with separate estimates for primary school (grades 3 to 6) and

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secondary school (grades 7 to 9) pupils. The two central growth components we
focused on in our analyses were the in-person learning slope and the distance
learning slope.
Before conducting the substantive analyses on these two, we first tested the
measurement invariance properties of the latent factor model incorporating the eight
single intervals. Reverting to a procedure suggested, for instance, by Ferrer et al.
(2008), we started with a model representing configural invariance and subsequently
tested models representing weak, strong and strict invariance across the eight
intervals. Unlike Ferrer and colleagues, however, we parameterised weak invariance
in terms of tau-equivalence by fixing all factor loadings to one. Following Chen (2007;
as cited in Putnick & Bornstein, 2016), the criteria we used for assessing invariance
were ΔCFI < .01, ΔRMSEA < .015 and ΔSRMR < .030 (for weak invariance only) or
ΔSRMR < .015 (for strong and strict invariance). We used this model to statistically
compare the two slopes and their associations with other variables by setting up
equality constraints one by one.
All computations were conducted using the lavaan package for R, considering
the nesting of pupils in school classes, employing the robust maximum likelihood
estimator for parameter estimation and using full information maximum likelihood
(FIML) for dealing with missing data. The decision for the latter, given the sparse data
we faced, was made against the findings reported by Xiao and Bulut (in press), who
found that FIML outperforms all other methods compared under most conditions.
Results
Baseline Model and Measurement Invariance
The baseline model with a common intercept across all eight intervals and

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with two different slopes for either in-person learning or distance learning fit the data
well: χ2(508) = 910.74, χ2/df = 1.79, CFI = .972, RMSEA = .010, 90% CIRMSEA = .009–
.011, SRMR = .070 (all indices reported here and hereafter are robust estimates). We
had to set the variance of some first-order factors to zero to prevent the estimation of
an inadmissible solution but otherwise did not encounter any further computational
problems despite the sparse data provided. Although the coverage of the variance-
covariance matrix was very low, with most of the cells representing less than five
percent of all cases, simulation studies suggest the effectiveness of structural
equation modelling techniques under similar conditions (e.g., Willse et al., 2008).
Starting with this configural invariance model, we first established weak
invariance in a tau-equivalent model by fixing all factor loadings to one. In addition,
we needed to fix the variance of the distance learning slope factor to zero in the
primary school group because this component was very close to zero (ψatt < .001,
SEψ = .002, p = .90), which would have resulted in inadmissible solutions in the
following analyses. This weak invariance model did not fit the data significantly worse
than the configural invariance model: ΔCFI = .004, ΔRMSEA = .000, ΔSRMR = .012.
To arrive at a strong invariance model, we first fixed the intercepts within the
two groups and then also between them. With the first set of constraints, one can test
whether invariance is given for each individual group and with the second whether
the measurement property constrained is the same in both groups. Fixing the
intercepts within groups did not significantly deteriorate the model fit compared to
the weak invariance model: ΔCFI = .007, ΔRMSEA = .010, ΔSRMR = .002.
Subsequently, fixing the intercepts between the two groups also did not harm the
model fit: ΔCFI = .001, ΔRMSEA = .000, ΔSRMR = .001.

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Finally, we fixed the error variances to be equal for the respective indicators,
first within and then also between the two groups. In addition, we had to fix two error
variances in the secondary school group to zero because otherwise they would have
been estimated as negative. The within-groups strict invariance model did not fit the
data significantly worse: ΔCFI = .002, ΔRMSEA = .000, ΔSRMR = .003. Compared to
this model, the following between-group strict invariance model deteriorated in terms
of ΔCFI = .011 but not in terms of ΔRMSEA = .001 and ΔSRMR = .004. We retained
this model for further hypothesis testing, as model fit deterioration was only marginal
and, and overall the model fit was satisfactory: χ2(622) = 1317.94 χ2/df = 2.12, CFI =
.947, RMSEA = .012, 90% CIRMSEA = .011–.013, SRMR = .092.
Means and Variances of the Learning Slopes
In this strict invariance model, the mean of the learning slope for primary
school pupils was estimated as νinp = .042 (SE ν = .007, p < .001) for in-person
learning and νdis = .018 (SE ν = .004, p < .01) for distance learning. The learning
progress of primary school pupils during in-person learning was more than twice as
high as during school closures, and this difference was highly significant: Δχ2(1) =
8.86 (p < .001). For secondary school pupils, the in-person learning slope was νinp =
.012 (SE ν = .005, p < .05), and the distance learning slope was νdis = .008 (SE ν =
.004, p = .05). These slopes did not differ significantly from each other: Δχ2(1) = 1.01
(p = .31).
For primary school pupils, the variance of the in-person learning slope was
deliberately set to ψinp = .00, and the variance of the distance learning slope was
estimated as ψdis = .009 (SEψ = .001, p < .001), so that the two significantly differed.
For secondary school pupils, the variance of the in-person learning slope was ψinp =

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.002 (SEψ = .001, p = .27), and the variance of the distance learning slope was ψdis =
.002 (SEψ = .001, p < .05). These two did not differ significantly: Δχ2(1) = 0.99 (p =
.32). Clearly, the heterogeneity in learning progress only increased in primary school
pupils and not in secondary school pupils.
Covariances of the Learning Slopes
Because for primary school pupils the in-person learning slope was set to
zero, no covariance between this and other growth components was computed. In
this group, we found a significant negative correlation between the intercept and the
distance learning slope (r = -.27, p < .001). Primary school pupils generally achieving
higher made slower learning progress during the school closures and vice versa.
We did not find this correlation to be significant for secondary school pupils,
for whom the intercept and the distance learning slope were uncorrelated (r = .00, p
= .98). By contrast, we found a strong positive correlation between the intercept and
the in-person learning slope (r = .57, p < .001). The two slopes were not significantly
correlated with each other (r = .23, p = .75).
Discussion
Among the findings reported here, some are particularly noteworthy. First, the
overall quality of our data, as indicated by the many insignificant measurement
invariance tests performed and the appropriate modelling approach chosen as
indicated by the excellent model fits, seems highly suitable for testing differences in
learning progress between in-person and distance learning. The unique design of our
study with unbiased observations before the school closures and continued
observations during this event allowed us to draw strong causal conclusions about
the two different types of learning. However, cautious interpretation is necessary

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because we were not comparing best in-person learning with best practice distance
learning but rather usual in-person learning with ad hoc distance learning rapidly
implemented in an emergency situation of high societal and individual uncertainty.
Nevertheless, the natural experiment setup of this study in combination with large
and unselected samples allows us to draw unique conclusions from the data.
We observed that the heterogeneity in learning progress significantly
increased for primary school pupils during the school closures. In the eight weeks
before the school closures, learning in primary schools took place rather uniformly
and with hardly observable differences between single pupils, but during the school
closures interindividual differences skyrocketed. These findings are compatible with
those of parents’ (Andrew et al., 2020) and teachers’ survey (Cullinane & Montacute,
2020) that were conducted in the UK and found that pupils from the most affluent
households were being offered active assistance (e.g., online tutoring) from their
schools during the lockdown more frequently than pupils from the least affluent
households. Although we have no data on the socio-economic status of pupils
participating in this study, the very same social disparity might explain the growing
heterogeneity in learning outcomes. At the same time, learning slowed down for this
particular group. We found that primary school pupils learned more than twice as fast
attending school in person compared to the distance setup. In contrast, secondary
school pupils were not significantly affected in their learning pace by the school
closures.
From a developmentalist perspective, the increased variance in and the
decreased pace of learning progress in primary school pupils can probably be
explained by cognitive, motivational and socio-emotional factors. The younger the

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pupils, the more they need to rely on cognitive scaffolding during instruction. In
addition, their executive functioning and hence their capabilities for self-regulated
learning might not yet be fully developed. Finally, younger pupils might be
particularly vulnerable to the stress and strains related to the pandemic. Similar age-
differential effects were reported for a completely different situation in the seminal
work of Glen Elder (Elder & Caspi, 1988), who found that while older children gained
in terms of autonomy and competence development during the great recession of
the 1930s, younger children suffered more from the economic hardship of their
families and more often became victims of marital discord or even family violence.
A final noteworthy effect is that the pace of learning during school closures
could not be predicted by the pace of learning during in-person learning. The
correlations between the intercept and the distance learning slope and between the
two slopes were either not significant or negative. This could mean either that two
completely different processes were measured before and during the school
closures (which would be a validity issue) or that the same process was measured
but the situations of in-person and distance learning were so different that learning
progress was driven by different factors before and during school closures (which
would be a substantial finding). Given the strict invariance measurement properties,
the former explanation seems unlikely.
Potential Long-Term Repercussions
Academic achievement can have cascading effects into other domains of life.
While Masten and colleagues (2005) have demonstrated such “developmental
cascades” on internalizing and externalizing problem behaviour, most existing
studies usually focus on academic or achievement-related outcomes such as

COVID-19 SCHOOL CLOSURES - 21
academic self-concept (Guay, Marsh, & Boivin, 2003), the choice of a college major
(Trautwein & Lüdtke, 2007), the choice of an occupation (Heckhausen & Tomasik,
2002), or earnings later in life (Zax & Rees, 2002). Studies based on economic
models are particularly interesting, as they provide a tangible description of the
expected effect size. Taking such economic models as a foundation, Azevedo and
colleagues (2020), for instance, estimated a life-time permanent loss in yearly
earnings ranging from USD 355 to 1,408 as a function of the duration of the school
closures.
Although studies predicting more general, non-achievement related aspects of
development by school achievement are not very common, there are good reasons
to assume a positive association if one considers some basic concepts of human
motivation. From a macro perspective, competence can be considered a
fundamental need “to experience satisfaction in exercising and extending one’s
capabilities” (Levesque et al., 2004, p. 68) and its successful fulfilment is associated
with intrinsic motivation, effective self-regulation, positive social development, and
well-being (Ryan & Deci, 2000). Following this reasoning, Tomasik and colleagues
(2019) have demonstrated that steeper educational gains across compulsory
schooling predicted successful development as indicated by notions of competence,
confidence in oneself, strong character, caring for others, and positive connections
with others (“Five Cs Model”; see Lerner et al., 2015). Notably, these constructs in
turn are predictive for contribution to society (Lerner et al., 2014).
This finding allows us to speculate about long-term repercussions for society
as a whole. Besides the direct impact of the pandemic in terms of death toll or
economic losses, one could almost certainly expect indirect consequences that are

COVID-19 SCHOOL CLOSURES - 22
mediated by slower educational gains and lower academic attainment. These
consequences comprise, but are not limited to, lasting effects on economic growth
and tax revenues, lower job satisfaction, more prevalent health issues, higher crime
rates, and lower cohesion in society. Whether or not the eight weeks of school
closures are sufficient to produce measurable effects has to remain an open question
here.
Theoretical Implications
Silbereisen and Tomasik (2011) argue that circumstances and events that – in
Bronfenbrennian terms – are located in the macro context of individual development
become psychologically effective only insofar as they are able to translate into the
most proximal micro contexts such as the family or the school. Within these micro
contexts, habits and routines are disturbed and require some form of adaptation. This
is exactly the conceptual blueprint needed to understand why the outbreak of the
COVID-19 pandemic could have had a measurable effect on pupils’ learning.
Against this backdrop, at least three theoretical insights can be gained from
the present analyses. First, the increased heterogeneity in the individual learning
trajectories points to the existence of unobserved factors might have moderated the
impact of the macro level transformation on individual adaptation and development.
These moderating factors might either be conceptualized as “institutional filters” (see
Schoon & Bynner, 2019), which prevent that events at the macro level become
manifest on the more subordinate levels. They might also be conceptualized at the
level of individual or social resources that strengthen the resilience of children and
their families (Tomasik & Silbereisen, 2009). Finally, more or less effective coping
strategies might be responsible for the large variance in developmental outcomes

COVID-19 SCHOOL CLOSURES - 23
(Pinquart & Silbereisen, 2008). Which of these factors and in which combination
were particularly relevant here needs to investigated in future research.
Second, our research demonstrated that macro level events can impact
specific outcomes (here: educational gains) also outside the specific developmental
context that is most proximal to the specific outcome (here: educational institutions
such as schools). We were able to present a case in point for a transmission
mechanism for which already Bronfenbrenner (1979) has coined the term “meso
context”. Although we have no direct evidence, it is plausible to assume that features
in the family context (such as the education of the parents or their occupational
uncertainty during the pandemic) were at least in part responsible for the educational
gains of children when the influence of schools was partly muted. Understanding and
predicting such meso effects is an intriguing endeavour for future psychological and
sociological work.
Third, our data provides convincing evidence that schools effectively attenuate
social disparities in learning, at least in primary school pupils. During in-person
schooling, we were able to observe a rather uniform learning progress, which is a
finding that also seems to generalize across much longer periods of time (e.g.,
Helbling et al., 2019). This is not only good news for educational policy but also might
help understanding how the participation in social contexts more generally shapes
individual developmental trajectories.
Limitations of the Study
Our study has some limitations, of which the questionable generalisability to
other countries and educational settings is probably the least severe. Of course, we
cannot say anything about the effects in other countries that were different in the

COVID-19 SCHOOL CLOSURES - 24
organization of their school system, in their cultural values, in their economic
standing, or in the social and economic impact caused by the pandemic. Therefore,
we refrain from speculating about any potential cross-cultural differences that might
or might not be found in comparative studies, although there is some elaborated
reasoning about the potentially differential impact of the pandemic in low- and
middle-income countries (Zar et al., 2020). Furthermore, data from international
large-scale assessments suggest striking similarities in the factors and mechanisms
associated with school achievement across countries with a quite diverse cultural
and economic background (e.g., Lee, 2014). We do not want to argue for a
universalist interpretation of our findings, but also do not see plausible reasons to
assume that they would be completely different somewhere else (but see Guan et al.,
2020, for a discussion of differential impact of the COVID-19 pandemic on career
development from a cultural psychology perspective).
More problematic is the establishment of causal effects of distance learning,
given the plethora of other factors that simultaneously comprised the situation during
the school closures. The three probably most important to mention are the high level
of strain for the families that could have undermined the teachers’ educational efforts
to provide good instruction, the teachers’ lack of expertise and time for preparation
and the lack of grading during and after the school closures that might have
undermined extrinsic motivation in pupils. These and other factors threaten the
internal validity of interpreting any differences as differences between in-person and
distance learning. At the same time, the external validity of our study could not have
been superior, and the results are informative for understanding both the short- and
long-term causal effects of this specific historical event.

COVID-19 SCHOOL CLOSURES - 25
Another limitation of the present study is that it does not allow empirically
answering the question whether the effect of reduced learning gains will translate
into disadvantageous developmental trajectories within other domains of life in the
short and in the long run. Not only does it not comprise developmental outcomes in
these domains nor does it cover sufficiently long time spans to make substantial
statements about such repercussions. We also cannot draw an any conclusions
about potentially aggravating or compensating factors in the other development
context of these youth.
A final limitation of our study is that it did not cover any transition phase in the
lives of the pupils affected by the school closures. None of our sample was entering
school, transitioning between different school types, or graduating from school,
although it is known that the timing of such events can make a huge difference for
their effect (e.g., Schoon et al., 2002). Research conducted during German
unification in the 1990s, for instance, suggests that those pupils who just graduated
from school during the system change were much worse off as compared to those
who have successfully entered the labour market or who had some “protected time”
while still at school. We are not able to investigate these interesting effects in the
present study because such transitions are not covered with the data available and
because the duration of the school closures was way too short to produce
meaningfully large comparison groups.
Conclusion
Empirical studies that could establish causal relations between the macro and
the micro are extremely rare. For both practical and ethical research reasons, one
has to rely on naturally occurring experimental designs and hence be in the right

COVID-19 SCHOOL CLOSURES - 26
place at the right time to be able to study the links between societal events and
individual adaptation and development. The COVID-19 pandemic provides an
opportunity for such a natural experiment. With MINDSTEPS we were in the right
place at the right time to provide solid evidence-based findings that can inform both
developmental science and educational policy. The main message that these findings
convey is that while older pupils are seemingly able to compensate for school
closures in terms of a sustained learning progress, the effects are dramatically
different for the younger ones. The learning gains of the younger children do not only
slow down, with potential long-term repercussions for future development, but also
become more heterogeneous. While some primary school pupils even seem to profit
from school closures, others’ school performance markedly deteriorates within a very
short period of time. These children are at risk for loosing track in the academic
domain and we may not leave them behind (see also Masonbrink & Hurley, 2020).

COVID-19 SCHOOL CLOSURES - 27
Ethical Compliance Section
The authors have no funding to disclose. All procedures performed in
studies involving human participants were in accordance with the ethical standards
of the institutional research committee and with the 1964 Helsinki Declaration and its
later amendments or comparable ethical standards. The authors declare that they
have no conflict of interest. In line with the American Psychological Association's
Ethical Principles and Code of Conduct, as well as with the Swiss Psychological
Society's Ethical Guidelines, written informed consent from pupils and their parents
was not required because this study was based on the assessment of normal
educational practices and curricula in educational settings. The Institute for
Educational Evaluation as a contractor of the cantonal educational authorities signed
and committed to obeying the laws of the four cantons involved to ensure strict data
confidentiality. In line with the laws of the four cantons approval from an ethics
committee was not required for this study.

COVID-19 SCHOOL CLOSURES - 28
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Table 1. Number of Assessments Completed and Number of Pupils Having
Completed these Assessments.
Assessments
Before
School Closures
During
School Closures
Pupils
Only Before
Before and
During
Only During
Mathematics
13,816
(3,898/9,918)
50,760
(25,044/26,716)
3,225
(983/2,242)
2,928
(1,197/1,731)
13,536
(7,337/6,199)
Reading
5,811
(1,893/3,918)
22,600
(12,401/10,199)
2,350
(620/1,730)
1,340
(577/763)
9,364
(5,268/4,096)
Grammar
10,773
(3,451/7,322)
39,405
(18,931/20,747)
3,036
(1,011/2,025)
2,984
(1,162/1,822)
12,997
(7,149/5,848)
Note: Total number of assessments and total number of pupils in the upper rows;
respective numbers, divided into primary and secondary schools, in the bottom rows
in brackets.