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Research in Education
2022, Vol. 0(0) 1–23
School teams’ regulation © The Author(s) 2022
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school-external expectations sagepub.com/journals-permissionsDOI: 10.1177/00345237221090540
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for school improvement
AndreaWullschleger, Ariane Rickenbacher, Beat Rechsteiner,
Urs Grob and Katharina Maag Merki
University of Zurich, Zurich, Switzerland
Abstract
School-external expectations regarding implementation of reforms and innovations often
do not lead to successful school improvement processes in schools. To better understand
these processes in schools, this paper aims to investigate school improvement processes
on a deep level by focusing on cognitive, metacognitive, and motivational regulation
strategies used by school teams and by exploring what school-external and school-
internal factors are related to this strategy use. Principals, teachers, and specialist teachers
(N = 1328) at 59 primary schools responded to an online questionnaire indicating their
school’s use of regulation strategies on school improvement. Results from descriptive,
variance, and hierarchical multiple regression analyses revealed that school teams use all
forms of school-based regulation strategies but that schools differ significantly in their
strategy use. These differences were mainly explained more by school-internal deeper
structures (e.g., task cohesion) and less by school-internal surface structures (e.g., school
size) and not at all by school-external factors (e.g., governance systems).
Keywords
school improvement processes, school-based regulation strategies, primary school,
school-external expectations, school teams
Corresponding author:
AndreaWullschleger, Institute of Education, University of Zurich, Freiestrasse 36, 8032 Zurich, Switzerland.
Email: awullschleger@ife.uzh.ch
2 Research in Education 0(0)
Introduction
Schools have to continuously adapt to external expectations so that teachers can teach and
students can learn in the best possible ways. However, research shows that it is difficult for
schools to implement external expectations, with the result that reforms often do not lead
to successful change (Cohen et al., 2018; Terhart, 2013). There are many explanations for
why schools often struggle with this process, and research offers suggestions on how
school improvement processes might be fostered (e.g., McFadden and Williams, 2020;
Staman et al., 2017). However, to understand why school-external expectations and
related school improvement efforts often do not lead to sustained development, deeper
investigation is required to identify processes in which school improvement to deal with
school-external expectations take an unfavorable course (Maag Merki et al., 2021a). To
achieve this deep-level investigation, we rely on a theoretical framework of school
improvement that is able to analyze regulation strategies within school improvement.
Referring to theories on self-regulation (Winne and Hadwin, 2008; Panadero, 2017), we
see regulation strategies in the context of school improvement as the school team’s
cognitive, metacognitive, and motivational strategies for identifying, analyzing, and
adapting the school improvement tasks, the standards set, the individual and collective
dispositions of the teams, and the school improvement processes implemented (Maag
Merki et al., 2021a). Up to now, regulation strategies within school improvement pro-
cesses have not been analyzed. Therefore, this paper aims to examine school teams’
regulation strategies in school improvement, determine whether schools differ in their
strategy use, and explore what factors are related to strategy use.
This study took place in Switzerland, where school-external expectations are mul-
tifaceted, and for schools to deal with them, continuous activities are required. A major
school-external expectation in recent years was the implementation of a new national (D-
EDK, 2016). This reform commits the cantons and their schools to school improvement
and therefore to educational change. Furthermore, the cantonal educational governance
systems include several monitoring instruments, such as school inspections and
achievement testing during and at the end of the school year.
Regulation strategies for school improvement
How do schools learn and improve?
School improvement is seen as the responsibility of the individual school. It develops
from within by different actors negotiating and adapting conditions and interests at
different levels (Creemers and Kyriakides, 2010; Mitchell and Sackney, 2011). The focus
is on a school’s capacity to deal with change in a way that achieves high-quality teaching
and enables students to learn in the best possible way (Hopkins, 2005). This perspective
on single schools brings school improvement close to the theoretical concept of orga-
nizational learning (Schechter and Atarchi, 2014). In many organizational learning
theories, the starting point of learning is a challenging situation. While acting, organi-
zations notice gaps between expected and actual results, or exploit promising
Wullschleger et al. 3
opportunities (Argyris and Schön, 1996; March and Olsen, 1975). Based on the theories,
organizations learn productively when individuals, subgroups, or entire teams start to
inquire into these contradicting situations in a way that strengthens the organizations’
capacity for managing change. In this process of learning, the use of data is crucial, as it
may reveal gaps between expected and real outcomes that are worth examining (Poortman
and Schildkamp, 2016) and may lead to data-driven decisions that are expected to
improve teaching and learning (Mandinach and Jimerson, 2016).
To investigate data-based school improvement processes related to school-external
expectations, in this study we analyze a school team’s regulation strategies. To this end we
use theories of self-regulated learning and apply them to collective learning in schools
(Maag Merki et al., 2021a).
How do school teams regulate their learning?
Theories of self-regulated learning (SRL) offer a broad umbrella of aspects that influence
learning (Panadero, 2017). SRL describes students as “metacognitively, motivationally,
and behaviorally active participants in their own learning process” (Zimmerman, 2001:
p. 5). The learning process starts with setting goals and then making choices on how to
achieve those goals. In this way students regulate conditions of tasks, operations that lead
to products, and standards by which products are assessed (Winne and Hadwin, 2008).
During this learning process, different regulation strategies are used. Here, we focus on
three: cognitive, metacognitive, and motivational. Cognitive regulation strategies are
strategies that students use to learn, remember, and understand domain-specific
knowledge and skills. The strategies are used for working on an academic task
(Boekaerts, 1996). They include, for example, rehearsal, elaboration, structuring, and
summarization (Weinstein and Mayer, 1986). Metacognitive regulation strategies are
relevant when learners identify a discrepancy between the strategy used and the demands
of the task and realize that a change in strategies makes sense (Boekaerts, 1996). Ex-
amples of a metacognitive strategy are creating a distinct mental representation of the task
demands and redefining it when needed, or creating an action plan and expanding or
modifying it when necessary. Motivational regulation strategies are applied during
learning processes to create good states of mind and prevent undesired situations
(Boekaerts, 1996). Examples are reducing external distractions or using thought stopping
when negative feelings arise (Weinstein and Mayer, 1986).
Social constructivist learning theories expanded the SRL theories’ focus on the in-
dividual learner by directing more attention to the social embeddedness of the learning
process and considering co-regulation and shared regulation (Hadwin et al., 2011). These
concepts describe regulated learning in interactions such as exchanging expertise or
collectively regulating processes towards a shared goal.
SRL is fruitful when thinking about collective regulation processes among school staff
for school improvement. Maag Merki et al. (2021b) define regulation processes in school
teams aiming to improve a school “as the (self-) reflective individual, interpersonal, and
organizational identification, analysis, and adaptation of tasks, dispositions, operations,
and standards and goals by applying cognitive, metacognitive, motivational-emotional,
4 Research in Education 0(0)
and resource-related strategies.” Cognitive regulation might refer to data-based decision
making when schools are required to elaborate and structure results of different feedback.
An example of metacognitive regulation is regular reflection upon the further devel-
opment of teaching and considering whether a change is needed. Motivational regulation
refers, for example, to strategies for carrying on even if work is sometimes demotivating,
such as when pupils do not show expected outcomes.
Research on regulation strategies in school teams
For teachers, SRL is of particular importance, as “teachers have no boss supervising their
daily work in the classroom or motivating them to stay focused on goals” (Randi, 2004:
p. 1826). There is initial research linking SRL, professional learning, and professional
development (e.g., Persico et al., 2015). Special in this area is that ideally, teachers should
not only apply self-regulation to their own learning but also implement it in the classroom.
Studies suggest that teachers’ ability to self-regulate their own learning is related to how
they can promote self-regulation in students (Randi, 2004; Perry et al., 2006). Ac-
cordingly, there is research interest in SRL during teacher education with a view to
teachers’ later continuous professional development. Studies show that pre-service
teachers’ use of SRL varies greatly and is positively associated with their academic
achievement (Hwang and Vrongistinos, 2002). Programs for teachers’ professional
development using SRL are more effective than others regarding different professional
growth measures (Kramarski and Michalsky, 2009) in terms of cognitive, metacognitive,
and motivational self-regulation (Michalsky, 2012). Regarding in-service professional
development, Butler et al. (2004), for instance, combined collaborative development
theories with theories of SRL. Their 2-years collaborative researcher–teacher partnership
aimed at supporting the learning of students with learning challenges. They applied a
qualitative case study design to examine instructional innovation and found that teachers
reflected upon and self-regulated their learning regarding shifts in instructional practice.
This brief literature review reveals that self-regulation among teachers is already being
studied but almost exclusively as an individual’s competency and rarely as embedded in
the social structure of a school team. In addition, the studies examined regulation
strategies in relation to student learning and not to school improvement in general. Finally,
previous studies did not consider school-internal as well as school-external influences on
teachers’ SRL strategies. We argue that examining school-based regulation strategies for
school improvement can shed new light on why external expectations and related im-
provement processes might not lead to sustainable change. Examining different regulation
strategies in school teams can reveal which strategies are used to what extent, whether
there are schools that are more capable than others in strategy use, and what factors
influence strategy use.
Research questions and hypotheses
We investigated the following research questions
Wullschleger et al. 5
1. As reported by teachers, to what extent do school teams use various school-based
regulation strategies for school improvement?
2. To what extent are there systematic differences between schools in school-based
regulation strategies?
3. If there are differences, to what extent can they be explained by school-external
and school-internal factors?
As there have been no studies investigating school-based regulation strategies for
school improvement up to now, this study is exploratory in nature. However, if we take
school improvement studies into account that analyzed differences between teachers and
schools in their professional activities (Camburn and Won Han, 2017; Holzberger and
Schiepe-Tiska, 2021), we can hypothesize that there are systematic differences between
teachers (hypothesis 1, H1) and schools (hypothesis 2, H2) also with regard to the
implementation of school-based regulation strategies for school improvement. However,
as previous studies found, the differences between schools might be smaller than those
between teachers. Furthermore, we expect only moderate school effects (Camburn and
Won Han, 2017), particularly as our analyses are done within a rather narrow sample
focusing on only one type of schools. To investigate school-external and school-internal
factors that might explain the differences in strategy use (hypothesis 3, H3), we developed
a hypothetical framework, which we also derived mainly from findings in school im-
provement research.
Framework of school-external and school-internal factors
Factors are grouped into school-external and school-internal factors; for school-internal
factors we distinguished surface and deep structures.
School-external factors. Different countries have highly diverse models of governance
systems, for example, ranging from rigorous monitoring to more decentralized systems
(Kogan, 1996). Different governance systems are related to the practices of school of-
ficials, principals, and teachers (e.g., Altrichter and Kemethofer, 2015; Coburn et al.,
2016; Slomp et al., 2020). Switzerland is a federally organized country, and the cantons
(states) differ in their educational policies. We assumed that stronger cantonal ac-
countability pressure has an impact on regulation strategies in schools, in that they will be
used in a more targeted manner regarding desired educational goals (H3a).
In addition, the socioeconomic status of the school’s catchment area seems to be
relevant as a school-external factor (H3b). Socioeconomic composition and status are
negatively related to instructional quality and students’ academic outcomes (Holzberger
and Schiepe-Tiska, 2021; Palardy, 2020).
School-internal factors. In line with Mitchell and Sackney (2011), as school-internal factors
we distinguished surface and deep structures. Both support improvement processes in
schools. As surface structureswe considered school size and teacher characteristics, such
as gender and seniority of practice.
6 Research in Education 0(0)
Regarding school size effects, studies found that the direct impact of school size on
cognitive learning outcomes and non-cognitive outcomes on students and teachers is
rather weak (Luyten et al., 2014). Although most studies found a negative relationship
between school size and outcomes in teacher professional development, some studies
reported positive or curvilinear effects. It has been argued that the inconsistent empirical
evidence is due to factors interfering with the impact of school size (Luyten et al., 2014).
As we included aspects like leadership, we assume no direct effects of school size on
regulation strategies (H3c). Further, as the relationships between teacher characteristics
and professional learning activities are inconsistent (Kwakman, 2003; Kyndt and Baert,
2013), we have no hypotheses concerning gender and seniority effects (H3d).
Deep structures of collective learning were conceptualized as organizational culture,
supportive school leadership, and teachers’ interest in their professional development.
Organizational culture is composed of task cohesion (Van Den Bossche et al., 2006),
handling of errors, and knowledge sharing (Staples and Webster, 2008)—three aspects of
a cognitive and affective working culture that have a direct impact on the well-being and
working performance of teachers. We suppose that these three aspects are highly in-
terrelated and form a fabric of essential features of the culture in a school that positively
influences the use of regulation strategies (H3e). In particular, we assume a positive
contextual effect in that a higher-level of organizational culture among teachers in the
same school has a positive effect on the individual teacher’s perspective on strategy use in
the school (H3f).
Supportive school leadership has proven to be an important aspect of teacher de-
velopment and school improvement (e.g., Amels et al., 2020). There is evidence of a
broad repertoire of what school leaders do to positively influence work condition to foster
high-quality teaching, such as setting directions, supporting desired practices, and de-
veloping people (Leithwood et al., 2020). Therefore, we suppose that supportive school
leaders also foster regulation strategies of their school teams (H3g). We assume a positive
contextual effect such that if teachers at the same school perceive the principal as highly
supportive, this has a positive effect on the individual teacher’s perspective on regulation
strategies (H3h).
Teachers’ interest in their professional development as a more individual element of
deep structures is related to more collaborative work and a higher motivation to col-
laborate (Pancsofar and Petroff, 2013). Also, SRL research provides evidence that interest
is linked to SRL and the use of learning strategies (e.g., Cleary and Kitsantas, 2017),
especially for metacognitive and motivational regulation (Wang et al., 2021). Therefore,
we assume that a teacher’s educational interest is associated with metacognitive and
motivational regulation (H3i).
Method
Participants and procedure
This study examined regulation processes for school improvement at 59 primary schools
(students aged 6–12) in 14 cantons in the German-speaking part of Switzerland. The
Wullschleger et al. 7
Table 1. Instruments to assess regulation strategies for school improvement.
Regulation strategies N Nitem M (SD.) α ICC1
Cognitive regulation 1304 5 4.51 (0.67) 0.86 0.046
Metacognitive regulation 1296 6 4.70 (0.66) 0.88 0.048
Motivational regulation 1292 6 4.61 (0.64) 0.87 0.023
schools were in urban, peri-urban, or rural regions, varied in size (34–593 pupils), and
differed in the socioeconomic status of the communities. Participants were 1328 school
staff (87% women) working as principals, teachers, and specialist teachers. Teachers’ age
ranged from 21 to 67 (M = 42.81, SD = 11.51). Teachers completed an online ques-
tionnaire (response rate 82%; 1328 of 1627 teachers) on regulation strategies for school
improvement.
Measures
Regulation strategies for school improvement. Three scales were used to assess regulation
strategies for school improvement at the schools (Table 1); participants rated statements
on a 6-point Likert scale ranging from strongly agree to strongly disagree. Cognitive
regulation was assessed with a self-developed and pre-piloted instrument that showed
good reliability. Participants were asked how their school deals with different types of
information from feedback. They had to indicate the extent of their agreement with
statements such as “Our school reduces extensive information to the essentials.” The
metacognitive regulation instrument was developed by the authors with reference to
Sleegers et al. (2013) and showed good reliability as well. Participants had to assess the
extent to which their school reflects upon the further development of their work, for
example, with the item “At our school, we often reflect on what works in our own work
and what needs to be changed.” Finally, motivational regulation was assessed using a
scale based on Schwinger et al. (2007) that showed good reliability. Participants had to
indicate what their school does to get ahead, even if the work may sometimes be de-
motivating (example item: “At our school, we use appropriate strategies to motivate
ourselves to continue working”). For detailed information on operationalization, see
Appendix A.
As the mean scores of the scales are relatively high and interrelated (see Appendix B)
and as the ICCs are relatively small, we calculated confirmatory factor analyses for a
three-factor model and compared it to a one-factor model using the lavaan statistical
package (Rosseel 2012, Version 06–3). Because not all items had a normal distribution, a
maximum likelihood robust estimator was used. In addition, two error correlations were
allowed, one in cognitive regulation and one in motivational regulation, as these items
asked the same questions but differed in wording. The three-factor model showed good fit
values (χ2 = 305.607, df = 136, p = 0.000, χ2/df= 2.25, CFI = 0.976; RMSEA = 0.043;
SRMR = 0.029) whereas the fit of the one-factor model was not sufficient (χ2 = 1968.677,
df = 119, p = 0.000, χ2/df= 16.54, CFI = 0.765; RMSEA = 0.132; SRMR = 0.085) (Hu and
8 Research in Education 0(0)
Bentler, 1999). The three-factor model fit the data significantly better. The chi square
value, AIC, and BIC of the three-factor model were smaller than those of the one-factor
model, and the difference in chi square values is significant.
School-external factors. The governance systems of the German-speaking cantons of
Switzerland were analyzed by assessing eight categories in publicly available documents.
The categories were rated using a scale ranging from 0 (not existing, for example, no staff
appraisal), 1 (existing, no or low accountability pressure, for example, formative staff
appraisal without consequences), to 2 (existing, high accountability pressure, for ex-
ample, wage-effective staff appraisal). As these questions could be answered unam-
biguously, the analysis was carried out by one trained person. Accordingly, the cantons’
governance systems could range from 0 (no accountability pressure) to 16 (high ac-
countability pressure). The governance score of the cantons was allocated to the schools.
On average, the schools had a governance score of 9 (N = 59, SD = 1.73, Min = 6,
Max = 13).
Socioeconomic status was assessed by average taxable income per capita in Swiss
francs in the municipality, as assessed by the Swiss Federal Statistical Office (N = 59,M =
33,489, SD = 10,390, Min =16,183, Max = 64,735).
School-internal factors. School size referred to the total number of pupils per school. The
average school size was 227 pupils (N = 59, SD = 153, Min = 34, Max = 593).
Teacher characteristics were described as gender (f = 86.7%) and seniority of practice
in years (M = 17, SD = 11, Min = 0, Max = 45).
Teachers’ personal interest in improving their work was assessed with a self-
developed and pre-piloted instrument that showed good reliability (α = 0.80).
Teachers indicated the extent to which they agreed with statements such as “I really want
to know what I can do better in the classroom” on a 6-point Likert scale ranging from
strongly agree to strongly disagree (N = 1316, M = 4.78, SD = 0.69).
In the instrument leadership for developing people, based on Sleegers et al. (2014) and
Robinson et al. (2008), teachers reflected upon the work of their school principal by
providing information on statements such as “Our principal encourages us to try new
things that are important to us.” The statements were assessed on a 6-point Likert scale
(N = 1239, M = 4.73, SD = 0.83) and showed good reliability (α = 0.87, ICC1 (ICC2) =
0.128 (0.755)).
The school’s organizational culturewas assessed using three subscales with four items
each: (1) handling of errors (e.g., “People in our school admit it when they have made a
mistake” (self-developed and pre-piloted); α = 0.88, ICC1 (ICC2) = 0.103 (0.717)), (2)
knowledge sharing (e.g., “People in our school are willing to share knowledge and ideas
with others” (Staples and Webster, 2008); α = 0.80, ICC1 (ICC2) = 0.128 (0.766)), and (3)
task cohesion (e.g., “Our school is united in trying to reach its goals for performance”
(Brawley et al., 1987); α = 0.73, ICC1 (ICC2) = 0.176 (0.826)). All items were rated on a
6-point Likert scale ranging from strongly disagree to strongly agree. Results of a
confirmatory factoring analysis based on this three-dimensional structure revealed an
acceptable model fit (χ2 (51) = 371.25, p < 0.001, CFI = 95, TLI = 0.93; RMSEA = 0.069,
Wullschleger et al. 9
SRMR = 0.04). For further information on the factorial structure of the psychometric
scales, see Appendix A.
Analytical approach
First, to obtain an impression of how teachers assess the use of regulation strategies for
school improvement at their school, descriptive analyses were carried out. Second, hi-
erarchical multiple regression analyses were performed. The analyses were conducted
using the lme4 package in R (Bates et al., 2015; version 1.1–23). Three models were
specified for cognitive, metacognitive, and motivational regulation strategies as de-
pendent variables. Teacher’s gender, seniority, as well as the assessment of leadership,
organizational culture, and interest were entered into the model as predictors on the within
school level (level 1). Governance system, socioeconomic status, school size, and again
leadership and organizational culture were inserted as predictors on the between school
level (level 2).
Leadership and organizational culture were inserted on both levels to determine a
contextual effect. Level 1 predictors were centered at the grand mean. In this way a
contextual effect can be taken from the level 2 regression coefficients (Raudenbush and
Bryk, 2002). We interpreted: (1) within effects as the effect of individual level variables on
the individual outcome for the predictors gender and seniority, (2) between effects re-
ferring to the influence of a mean characteristic of a school on the mean outcome for the
predictors governance system, socioeconomic status, and school size, and (3) contextual
effects as the effect that refers to how the actions of others at the same school influence
individual behavior (outcomes) for the variables leadership and organizational culture.
Random intercept models were calculated. The estimation method of maximum
likelihood was used.
CriterianVariableij ¼ γ00 þ γ10genderij þ γ20seniorityij þ γ30leadershipij
þ γ40organizational_cultualþ γ50interestij þ γ01governancej
þ γ02socioec_statusj þ γ03school_sizej þ γ04organizational_cultualj
þ γ05leadership þ μ0j þ eij
For the multilevel analysis we checked linearity, homoscedasticity, and residual as-
sumptions. The data satisfied linearity assumptions. One model—cognitive regulation—
slightly violated the homoscedasticity assumption as assessed using the Levene test (p =
0.009). An upward residual analysis was applied to check level 1 residuals separately
(Loy and Hofmann, 2014). The level 1 residuals of each model appeared normal except
for the very low values. However, this discrepancy was quite small and hardly constituted
a violation of the normality assumption. Normal quantile plots of the level 2 residuals for
the intercept revealed no evidence of a deviation from normality. For additional infor-
mation on the analyses, see Appendix B for standard correlations as well as within and
between level correlations.
10 Research in Education 0(0)
Table 2. Frequency data of regulation strategies for school improvement.
Strongly Slightly Slightly Strongly
Regulation disagree, Disagree, disagree, agree, Agree, agree,
strategies N M SD % % % % % %
Cognitive regulation 1304 4.51 0.67 0.5 0.7 5.5 36.7 52.8 3.8
Metacognitive regulation 1296 4.70 0.66 0.2 0.6 2.2 34.0 52.2 10.7
Motivational regulation 1292 4.61 0.64 0.3 0.5 2.5 39.2 50.2 7.4
Results
Frequency data
Table 2 summarizes the frequency data on the use of regulation strategies for school
improvement at the schools. The mean values and standard deviations indicated that as
assessed by the participants, there was a quite high occurrence of all three forms of
regulation strategies at their schools. The majority of the participants slightly agreed
or agreed with the statements on the regulation strategies being used in their
school team.
Regulation strategies related to school-external and school-internal factors
Hierarchical linear models were specified for the three variables of regulation. Table 3
presents the results for cognitive regulation, Table 4 for metacognitive regulation, and
Table 5 for motivational regulation. The null models revealed that only 5.3%, 5.5%, and
2.4% of the observed differences in cognitive, metacognitive, and motivational regulation
were due to between school variability.
Comparing the baseline models (only within school predictors) with the final model
(within and between school predictors) in all cases, the results reveal that the between
school predictors did not add much variance explanation to the models. This is also
evident in the final models when comparing within and between school variances.
Following Nakagawa and Schielzeth (2013), we report marginal and conditional R2.
Marginal R2 estimates the variance explained by the fixed effects and conditional R2 by
the fixed and random effects. The effect sizes for the overall models were f2 = 0.47 for
cognitive regulation, f2 = 0.46 for metacognitive regulation, and f2 = 0.54 for motivational
regulation, indicating large effects (Cohen, 1992).
Within effects: Regarding gender, at the within school level (level 1) women reported
significantly stronger agreement with statements indicating that all three forms of
regulation strategies were used more often by their school. In contrast, teacher’s se-
niority was not related to any form of regulation strategies. Teachers’ personal interest in
improving their work was significantly and positively associated with only motivational
regulation, indicating that teachers with a strong interest in developing their work
Wullschleger et al. 11
Table 3. Multilevel regression on cognitive regulation (nobs = 1178, nschool = 58).
Null model (1) Baseline model (2) Final model (3)
β SE p β SE p β SE p
Intercept 4.52 0.03 0.000 4.65 0.14 0.000 5.04 0.42 0.000
Level 1
gender (m = 1, f = 0) 0.15 0.05 0.003 0.20 0.05 0.003
seniority 0.00 0.00 ns 0.00 0.00 ns
leadershipa 0.23 0.02 0.000 0.25 0.03 0.000
organizational culturea 0.38 0.03 0.000 0.36 0.03 0.000
interest 0.00 0.03 ns 0.00 0.03 ns
Level 2
governance 0.00 0.01 ns
socioecon. status 0.00 0.00 ns
school size 0.00 0.00 ns
leadership 0.12 0.06 ns
organizational culture 0.05 0.08 ns
Random part Var SD Var SD Var SD
σ2u0 0.025 0.157 0.002 0.044 0.000 0.000
σ2e 0.440 0.663 0.318 0.564 0.318 0.564
Explained variance, % (marginal) 0 30.8 31.3
Explained variance, % (conditional) 5.3 31.2 31.3
Note.
aCentered at the grand mean of the sample.
12 Research in Education 0(0)
Table 4. Multilevel regression on metacognitive regulation (nobs = 1178, nschool = 58).
Null model (1) Baseline model (2) Final model (3)
β SE p β SE p β SE p
Intercept 4.71 0.03 0.000 4.70 0.13 0.000 4.68 0.40 0.000
Level 1
gender (m = 1, f = 0) 0.14 0.05 0.005 0.15 0.05 0.002
seniority 0.00 0.00 ns 0.00 0.00 ns
leadershipa 0.27 0.02 0.000 0.26 0.02 0.000
organizational culturea 0.38 0.03 0.000 0.38 0.03 0.000
interest 0.04 0.02 ns 0.04 0.02 ns
Level 2
governance 0.00 0.01 ns
socioecon. status 0.00 0.00 ns
school size 0.00 0.00 ns
leadership 0.03 0.06 ns
organizational culture 0.04 0.07 ns
Random part Var SD Var SD Var SD
σ2u0 0.025 0.156 0.000 0.013 0.000 0.000
σ2e 0.417 0.646 0.277 0.526 0.277 0.526
Explained variance, % (marginal) 0 37.0 37.1
Explained variance, % (conditional) 5.5 37.1 37.1
Note.
aCentered at the grand mean of the sample.
Wullschleger et al. 13
Table 5. Multilevel regression on motivational regulation (nobs = 1178, nschool = 58).
Null model (1) Baseline model (2) Final model (3)
β SE p β SE p β SE p
Intercept 4.63 0.02 0.000 4.43 0.13 0.000 6.28 0.44 0.000
Level 1
gender (m = 1, f = 0) 0.12 0.05 0.014 0.11 0.05 0.015
seniority 0.00 0.00 ns 0.00 0.00 ns
leadershipa 0.24 0.02 0.000 0.25 0.02 0.000
organizational culturea 0.39 0.03 0.000 0.42 0.03 0.000
interest 0.06 0.02 0.006 0.06 0.02 0.012
Level 2
governance 0.01 0.01 ns
socioecon. status 0.00 0.00 ns
school size 0.00 0.00 ns
leadership 0.11 0.06 ns
organizational culture 0.25 0.08 0.002
Random part Var SD Var SD Var SD
σ2u0 0.010 0.099 0.012 0.111 0.005 0.069
σ2e 0.406 0.637 0.260 0.510 0.260 0.510
Explained variance, % (marginal) 0 36.3 36.3
Explained variance, % (conditional) 2.4 39.2 37.4
Note.
aCentered at the grand mean of the sample.
reported significantly stronger agreement that their school used motivational regulation
strategies.
Between effects: At the between school level (level 2), school-external factors
(governance system, socioeconomic status) and school size were not significantly as-
sociated with the use of regulation strategies.
Contextual effects: For supportive leadership there was no significant contextual effect
for all three regulation strategies. This means that how individuals within the same school
perceived the support of their school leader had no effect on individual agreement with the
use of regulation strategies. For organizational culture we found a significant negative
contextual effect for motivational regulation (0.25). This means that the more positively
individuals within the same school rated organizational culture, the lower their individual
agreement with the use of motivational regulation strategies was. The school context thus
negatively influenced individual agreement with the use of motivational regulation
strategies. For cognitive and metacognitive regulation, we found no significant contextual
effect.
14 Research in Education 0(0)
Discussion
This paper is an attempt to consider new aspects in school improvement processes to shed
light on their often unsuccessful implementation of external expectations. To do this, we
investigate cognitive, metacognitive, and motivational regulation processes in Swiss
primary schools, where school-external expectations are multifaceted. Dealing with these
expectations requires continuous activities on the part of the schools.
Regulation strategies of school teams
Regarding research question one on different school-based regulation strategies for school
improvement, the findings indicate that all forms of school-based regulation strategies are
used in school teams, as the majority of principals, teachers, and specialist teachers
slightly agree or agree with the statements on the regulation strategies used. However, the
use of strategies still can be improved, as very few participants indicate strong agreement
with the statements. Compared to other studies on teachers’ reflective practice (Camburn
and Won Han, 2017), there is quite low variation in the identification of regulation
strategies, meaning that teachers’ perspectives on the implementation of collective
regulation strategies are quite coherent. This could be an indication of the reliability of the
measures. On the other hand, further studies are needed to analyze if the self-report data
was impacted by social desirability (see Limitations).
The results provide evidence for shared regulation processes in the context of school
improvement, as has been already identified for student learning (Hadwin et al., 2011).
However, as our results are only able to show that school teams use the strategies that we
surveyed here, further research should investigate how, how often, and how well
strategies are used and include methods that are able to capture the performed activities
(Ohly et al., 2010), for instance, by using data on collaborative interactions in school
teams (Hadwin et al., 2011). Further, analyses should explore whether and how the
implemented collective regulation processes impact instruction and student learning
(Panadero, 2017).
Regarding research question two on school differences in school-based regulation
strategies, the findings reveal differences—but small ones. These moderate differences
were expected, as previous studies in related fields yielded similar results (e.g., Camburn
and Won Han, 2017). The small between school differences may be explained by the
narrow sample focusing on only one type of school, namely, primary schools. Future
research could extend these findings by researching different school types. The findings
reveal further that the differences are larger for cognitive and metacognitive than for
motivational regulation strategies. As we know from school improvement research,
teachers’ motivation is one of the challenges in school improvement (Thoonen et al.,
2011). Therefore, the fact that schools hardly differ in their use of motivational regulation
strategies could be explained by the widespread knowledge on the part of school leaders
that it is important to strengthen the capacity to regulate a lack of motivation.
Regarding research question three on school-external and -internal factors to explain
differences in regulation strategies, the findings reveal that school-based regulation
Wullschleger et al. 15
strategies are not related to school-external factors. This is contrary to our expectations
(H3a, H3b) based on previous findings that different governance systems are related to
school improvement practices (e.g., Altrichter and Kemethofer, 2015) and that socio-
economic composition is related to school practices (Holzberger and Schiepe-Tiska,
2021). The finding of no governance effect may be due to the rather low variation between
the cantonal governance systems in Switzerland. Although the cantons differ in their
education policy, they vary in the low to medium range on accountability pressure. The
lack of an effect of socioeconomic status could be an artifact of our data. The Federal
Statistical Office provides data only at the municipality level. However, it is possible that
different school locations within a municipality have different socioeconomic catchment
areas, so that the values are not equally applicable to all schools.
As for school-internal factors, surface structures tend to be less predictive of the
perceived use of regulation strategies (H3c and H3d) than are deep structures concep-
tualized as organizational culture, supportive leadership, and teachers’ interest in their
professional development on the within level (H3e, H3g, and H3i). As expected (H3i),
teachers’ interest in their professional development is related to motivational and met-
acognitive regulation. The findings reveal that interest is relevant not only for students’
learning (Wang et al., 2021) but also for teachers’ learning. However, as our results reveal
almost no contextual effect for supportive leadership and organizational culture, which is
contrary to our expectations (H3f, H3h), the use of regulation strategies for school
improvement is basically influenced by individual perceptions. Accordingly, it is less
about whether a school teammember is a man or woman or has a lot of or little experience,
whether the school is large or small, or whether the individuals within the same school
assess the level of a school’s organizational culture on average as high or low, and more
about how individuals perceive the working culture and leadership support and how
interested they are in improving their work.
Why most contextual effects were nonsignificant and why there was even a negative
contextual effect on the use of motivational regulation strategies needs to be analyzed in
further studies. One possible explanation for these unexpected results may be that the
working culture in a school team may be different in different subteams, as subteams are
an important unit of analysis (e.g., Vangrieken et al., 2017). In this respect, further re-
search should include a third level with subteams in the analyses.
Implications
As an implication for school improvement theory, the results show that cognitive,
metacognitive, and motivational regulation strategies are used in primary schools to deal
with school-external expectations. Therefore, complementing existing school improve-
ment theories with SRL theories is profitable, especially because they focus on concrete
learning processes and thus provide a deeper insight into which strategies are used to what
extent, whether there are schools that are more capable than others in strategy use, and
what factors influence this.
16 Research in Education 0(0)
As an implication for educational policy and practice, the results are important, as they
reveal that a focus on regulation strategies in teachers’ professional development is
relevant. However, to understand influencing factors further research is needed.
Limitations
First, self-reporting should always be regarded with certain reservations and should be
complemented by alternative measures (Pekrun, 2020). It is possible, for example, that
the high mean values of the regulation variables may be related to social desirability.
Therefore, methods that are able to capture not only perceived activities but also
concrete performances would extend these results substantially (Ohly et al., 2010).
Second, future studies should focus also on capturing the quality and not only the
quantity of the implemented regulation strategies (Wirth and Leutner, 2008). Third, as
we use cross-sectional data, the interpretation of the results must be viewed with some
reservation. Fourth, the assumption of homoscedasticity for the multilevel analysis
regarding cognitive regulation was slightly violated. This may have multiple causes,
none of which is immediately apparent. As the extent is not severe, this is only a slight
limitation.
Conclusions
Up to now, no studies have investigated school-based regulation strategies for school
improvement. This paper marks only the beginning in the exploration of regulation
strategies in school teams. The results show that it is worthwhile to continue along this
path by investigating regulation strategies in a more differentiated way. Future research
should approach the topic with a variation of the methodology, preferably combining
quantitative and qualitative data.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/
or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or
publication of this article: This work was supported by the Swiss National Science Foundation
[grant number 100019_175872/1].
ORCID iD
Andrea Wullschleger  https://orcid.org/0000-0003-4390-3835
Wullschleger et al. 17
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Appendix A
All psychometric scales have a single-factor structure. The factor loadings can be taken
from the table below.
Table A1. Results of factor analyses of the psychometric scales of the study.
Item Loadings α
Cognitive regulation 0.86
To what extent do you agree with the following statements
1 Our school obtains an overview of the various information 0.73
2 Our school reduces extensive information to the essentials 0.78
3 Our school interrelates the most important points from various information 0.84
4 Our school establishes relationships between new information and its own pedagogical practice 0.82
5 Our school links new information with already known information 0.81
Metacognitive regulation 0.88
To what extent do you agree with the following statements
1 At our school, we often think about what works in our own work and what needs to be changed 0.80
2 At our school, after completing a task, we think about what we would do differently in the next similar task 0.76
(continued)
Wullschleger et al. 21
Table A1. (continued)
Item Loadings α
3 At our school, we consider from time to time during the work if we need further information or materials 0.78
4 At our school, while working, we check from time to time if we are still on the right track 0.84
5 At our school, we often reflect on how our own work supports student learning 0.77
6 At our school, we question the goals of our work from time to time 0.84
Motivational regulation 0.87
To what extent do you agree with the following statements
1 At our school, we use appropriate strategies to motivate 0.76
ourselves to continue working
2 At our school, we realize how important it is for students’ growth 0.78
to keep at it despite challenges
3 At our school, we use appropriate strategies to manage negative 0.82
feelings that arise at work so that work can continue well
4 At our school, we are motivated by the thought that students 0.73
will benefit from our involvement
5 At our school, we find ways to reduce unpleasant emotions 0.81
at work in a targeted way
6 At our school, we consciously use strategies to positively 0.80
influence our own emotions
Teachers’ personal interest in improving their work 0.80
1 I Really want to know what I can do better in the classroom 0.76
2 I Really want to know how good my own students really are 0.78
3 I Really want to know how effective my own teaching really is 0.87
4 I Really want to know if all students have met the learning objectives 0.75
Leadership for building relationships and developing people 0.87
1 Our school principal encourages us to try new things that 0.83
are important to us
2 Our school principal encourages us to think about how our 0.87
school can be improved
3 Our school principal encourages us to seek out and discuss 0.88
new ideas for moving the school forward
4 Our school principal creates enough opportunities for us to 0.82
work on developing our own skills
Organizational culture - handling of errors, (1) 0.88
1 At our school, we are patient when any of us make a mistake 0.81
2 At our school, we openly admit it when one of us has made a mistake 0.87
3 When mistakes happen at our school, we discuss them in a way that 0.90
really makes a difference
4 Mistakes at our school help us to do better afterward 0.86
Organizational culture - knowledge sharing (2) 0.80
1 At our school, we keep the best ideas to ourselves. (R) 0.74
2 At our school, everyone is willing to share knowledge 0.82
and ideas with others
3 At our school, people who have special expertise in an 0.83
area are willing to share it
4 At our school, we are good at using knowledge and ideas 0.78
from different people
Organizational culture - task cohesion (3) 0.73
1 At our school, we try to achieve goals together 0.83
2 At our school, everyone takes responsibility for the further 0.83
development of our school
3 At our school, there are rival ideas about the further development 0.47
of our school. (R)
4 At our school, we agree on the direction in which we want 0.81
to move forward
Note. N = 1239–1316. The extraction method was principal component analysis with an orthogonal (varimax)
rotation. Reverse-scored items are denoted with an (R).
22 Research in Education 0(0)
Appendix B
Table B1. Standard correlations between regulation strategies, school-external factors, and
school-internal factors.
n 1 2 3 4 5 6 7 8 9 10 11
1. Cognitive regulation 1187 —
2. Metacognitive regulation 1187 0.67*** —
3. Motivational regulation 1187 0.54*** 0.58*** —
4. Gender 1187 0.07* 0.07* 0.06* —
5. Seniority 1187 0.04 0.06* 0.03 0.01 —
6. Leadership 1187 0.45*** 0.51*** 0.48*** 0.03 0.01 —
7. Organizational culture 1187 0.49*** 0.53*** 0.52*** 0.03 0.06* 0.47*** —
8. Interest 1187 0.11*** 0.15*** 0.18*** 0.00 0.12*** 0.16*** 0.16*** —
9. Governance 1187 0.05 0.05 0.05 0.02 0.09** 0.15*** 0.01 0.03 —
10. Socioeconomic status 1187 0.05 0.03 0.01 0.04 0.02 0.05 0.02 0.01 0.33*** —
11. School size 1187 0.12*** 0.08** 0.09** 0.02 0.05 0.02 0.22*** 0.05 0.21*** 0.24*** —
Table B2. Within level correlations between school-external and school-internal factors.
n 1 2 3 4 5 6 7 8 9 10
1. Gender 1187 —
2. Seniority 1187 0.01 —
3. Leadership level 1 1187 0.01 0.02 —
4. Organizational culture level 1 1187 0.04 0.04 0.48 —
5. Interest 1187 0.01 0.11 0.18 0.18 —
6. Governance 58 0.00 0.00 0.00 0.00 0.00 —
7. Socioeconomic status 58 0.00 0.00 0.00 0.00 0.00 0.00 —
8. School size 58 0.00 0.00 0.00 0.00 0.00 0.00 0.00 —
9. Leadership level 2 58 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 —
10. Leadership level 2 58 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 —
Note. *** p < 0.001. **p < 0.01.* p < 0.05.
Wullschleger et al. 23
Table B3. Between level correlations between school-external and school-internal factors.
n 1 2 3 4 5 6 7 8 9 10
1. Gender 1187 —
2. Seniority 1187 0.02 —
3. Leadership 1187 0.64 0.16 —
level 1
4. Organizational 1187 0.09 0.49 0.40 —
culture level 1
5. Interest 1187 0.58 0.47 0.07 0.01 —
6. Governance 58 0.26 0.74 0.33 0.02 0.13 —
7. Socioeconomic 58 0.41 0.25 0.06 0.08 0.03 0.32 —
status
8. School size 58 0.22 0.47 0.03 0.52 0.32 0.20 0.17 —
9. Leadership 58 0.64 0.16 1.00 0.40 0.07 0.33 0.06 0.03 —
level 2
10. Organizational 58 0.09 *.49 0.40 1.00 0.01 0.02 0.08 0.52 0.40 —
culture level 2