Variable- and Person-centred meta-re-analyses of university students' learning strategies from a cross-cultural perspective

Abstract

Studies on learning strategies across cultures in higher education inform the internationalisation of teaching and learning. Previous comparisons relied on geographical generalisations (e.g., “Asian”, “Western”, “Latin-American”) or only variable-centred methods, which can overgeneralise the contexts they represent. Eight learning strategy datasets (ILS; Inventory of Learning patterns of Students) from seven countries (n = 4883) were obtained and (re-)analysed using variable-centred and person-centred (Latent Profile Analysis; LPA) methods. Employing Hofstede’s individualism-collectivism and power distance indices as predictors, lower individualism and higher power distance scores corresponded to students’ overall combined reporting of meaning-directed, reproduction-directed and application-directed learning strategies. Furthermore, sample LPAs found that less individualistic contexts presented profiles with similar shape (i.e., profiles differed by similar amounts across most learning strategies). More individualistic contexts presented profiles with different shapes (i.e., different profiles preferred different strategies). Multiple “Western” contexts presented profiles that describe the “Asian” and “Latin-American” learner stereotypes. These results question the existence of such stereotypes and point to the usefulness of cultural indicators for making cross-cultural learning strategy comparisons. Theoretical and practical implications are discussed.

Full text

Vol.:(0123456789)
Higher Education
https://doi.org/10.1007/s10734-023-01062-4
1 3
Variable‑ andPerson‑centred meta‑re‑analyses ofuniversity
students’ learning strategies fromacross‑cultural
perspective
AlexShum1· LukeK.Fryer1· JanD.Vermunt2· ClaraAjisuksmo3· FranciscoCano4·
VincentDonche5· DennisC.S.Law6· J.ReinaldoMartínez‑Fernández7·
PeterVanPetegem5· JiYu8
Accepted: 26 May 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023
Abstract
Studies on learning strategies across cultures in higher education inform the internation-
alisation of teaching and learning. Previous comparisons relied on geographical general-
isations (e.g., “Asian”, “Western”, “Latin-American”) or only variable-centred methods,
which can overgeneralise the contexts they represent. Eight learning strategy datasets (ILS;
Inventory of Learning patterns of Students) from seven countries (n = 4883) were obtained
and (re-)analysed using variable-centred and person-centred (Latent Profile Analysis; LPA)
methods. Employing Hofstede’s individualism-collectivism and power distance indices as
predictors, lower individualism and higher power distance scores corresponded to students’
overall combined reporting of meaning-directed, reproduction-directed and application-
directed learning strategies. Furthermore, sample LPAs found that less individualistic con-
texts presented profiles with similar shape (i.e., profiles differed by similar amounts across
most learning strategies). More individualistic contexts presented profiles with different
shapes (i.e., different profiles preferred different strategies). Multiple “Western” contexts
presented profiles that describe the “Asian” and “Latin-American” learner stereotypes.
These results question the existence of such stereotypes and point to the usefulness of cul-
tural indicators for making cross-cultural learning strategy comparisons. Theoretical and
practical implications are discussed.
Keywords Learning strategies· International· Cross-cultural Analysis· Person-centred
Analysis· Learning Patterns
Introduction
The prevailing internationalisation of higher education has strengthened demand for the cross-
cultural understanding of student learning strategies (Eaves, 2011; Marambe etal., 2012).
Previous literature has investigated the learning experiences of international students in uni-
versities and local students in satellite campuses (e.g., Bilsland etal., 2020; Heng, 2018; Wier-
stra etal., 2003). The recent proliferation of online courses further emphasises the need to
Extended author information available on the last page of the article

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understand this aspect of students’ academic experience (Tapanes et al., 2009; Vermunt &
Donche, 2017).
Cross-national studies of students’ learning strategies have been limited in the number of
nations compared, generalisability to other nations and ability to describe different student
profiles of learning strategies within nations. Past analyses have examined learning strategies
of samples from different nations to 1) generalise to and compare “Asian” and “Western” con-
texts, and 2) analyse learning strategies within these geographical labels (e.g., Marambe etal.,
2012; Martínez-Fernández & Vermunt, 2015; Purdie etal., 1996). Such broad categorisations
are often unclear and fail to adhere to the continuum of cultural characteristics between and
within these labels (Eaves, 2011).
A different approach to cultural comparisons is to consider cultural dimensions whose
definitions are independent of geographical location, such as individualism-collectivism and
power distance. Nation-level indices describing agreement with these dimensions have been
developed according to representative samples (Hofstede, 2001; Minkov etal., 2017). Cul-
tural dimensions have been discussed in educational research generally and specifically within
learning strategies research (e.g., Aparicio etal., 2016; Tapanes etal., 2009; Watkins, 2000).
However, these connections have undergone little empirical testing.
Quantitative cross-national comparisons have predominantly been variable-centred which
compares sample-level learning strategy means (Marambe etal., 2012; Vermunt etal., 2014).
However, multiple profiles of learning strategies (i.e., subpopulations employing similar learn-
ing strategies) may exist within a sample. Person-centred analyses such as Latent Profile Anal-
ysis (LPA) employ both quantitative and qualitative perspectives to uncover these profiles.
These analyses allow for comparison between different learning strategy combinations.
In learning patterns research, Vermunt and Vermetten (2004) described learning patterns
to be influenced by learning experiences, but extending across multiple contexts (e.g., assess-
ments, classes or courses). The Inventory of Learning patterns of Students (ILS; Vermunt,
1996, 1998) measures students’ self-reported learning patterns, which includes learning strate-
gies (e.g., Vermunt, 2020; Yu etal., 2021). Learning patterns research differs from the Stu-
dents’ Approaches to Learning (SAL) research tradition, which investigates learning strate-
gies employed in a specific context (Marton & Säljö, 1976; Richardson, 2015). Therefore,
responses to the ILS are likely to incur less context-related bias and provide a more appropri-
ate lens to investigate cultural differences in students’ ongoing use of learning strategies.
The current study draws upon published and unpublished ILS datasets (one from each of
Venezuela, Indonesia, China, Hong Kong, Spain, the Netherlands and two from Belgium) to
investigate the cross-cultural use of learning strategies. First, variable-centred conclusions
are drawn on the appropriateness of using cultural indicators: individualism-collectivism and
power distance for making cross-cultural investigations (Hofstede, 2001; Minkov etal., 2017).
Second, LPAs are used to investigate the characteristics of learner profiles for each individual
sample. Finally, a qualitative comparison of profiles across samples is undertaken to shed light
on profiles that exist across cultures.
Background
Learning strategies
Learning strategy research has focused on the evolving understanding of students’ use of
surface and deep processing. Surface processing is described by rote memorisation to cope

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with assessments; while deep processing is characterised by an intention to understand
meaning when learning (Marton & Säljö, 1976). SAL research began by identifying which
of the two learning approaches students would employ due to specific contextual demands
such as assessments or in response to teaching methods (Marton & Säljö, 1976; Richard-
son, 2015). Biggs et al. (2001) later argued that surface and deep approaches were not
dichotomous. Both could be utilised in the same context in varying amounts dependent on
learner, timing and purpose (Dinsmore & Alexander, 2012; Kember, 2000). In Alexander’s
(2003) Model of Domain Learning, students progress through acclimation, competence and
proficiency stages in domain learning and mastery. Initially, students predominantly rely on
surface-level processing strategies. These eventually decrease as students begin adopting
deep-level processing strategies in later stages. Hattie and Donoghue (2016) pointed to the
sequential application of surface followed by deep acquiring and consolidating strategies,
which lead to transfer towards other learning situations. These learning models map impor-
tant processes in student learning generally but may fail to address differences in learning
strategies between cultures (e.g., Marambe etal., 2012; Marton etal., 2005).
Vermunt (1996, 1998) examined learning strategies including both processing and
regulation strategies as part of the Model of Learning Patterns employing the ILS, a self-
report questionnaire. Regulation strategies describe methods in which students use to plan,
evaluate, steer and monitor their learning. The ILS measures students’ external regulation
(e.g., willingness to accept regulation from external sources such as class materials and
tests), self-regulation (e.g., making study plans, self-testing, monitoring progress, consult-
ing sources outside the syllabus) and lack of regulation (e.g., difficulties with regulating
learning processes). Vermunt and Vermetten (2004) conceptualised a learning pattern as a
superordinate concept in which the processing and regulation strategies that students usu-
ally utilise, their conceptions of learning and their learning orientations are united. Using
factor analysis, Vermunt (1996, 1998) found that scales of the ILS loaded onto four fac-
tors representing overarching learning patterns: meaning-directed, reproduction-directed,
application-directed and undirected. Table1 presents a description of all of the processing
and regulation strategies measured by the ILS, which will be analysed in the current study
(Vermunt & Donche, 2017). For a detailed discussion of learning conceptions and orienta-
tions, see Vermunt and Donche (2017).
Cross‑cultural learning strategies research
Cultural investigation of students’ learning strategies alongside academic achievement has
led to mixed results. Achievement in Western contexts has been associated with employing
a deep approach to learning, however Asian students have been found to succeed academi-
cally using a surface approach (e.g., Biggs, 1994). This discrepancy is known as the Asian
learner paradox. Marton etal. (2005) and Watkins (2000) resolved this incongruency, stat-
ing that such students memorise with an intent to understand and employ a combination
of surface and deep learning strategies. The use of learning strategies beyond and within
“Western” or “Asian” generalisations remains unclear.
In the SAL research tradition, students adopt learning strategies appropriate for their
learning contexts. Cross-cultural studies employing a SAL framework may incur limita-
tions in internal and external validity if compared samples vary on non-cultural factors
(e.g., teaching methods, domains, assessments; Leung et al., 2008). However, the ILS
measures processing and regulation strategies at the learning pattern level which though
shaped by experiences, provide greater consistency over multiple learning contexts

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Table 1 Processing and regulation strategies of the ILS
Learning strategies Traditional learning pattern
(Vermunt & Donche, 2017)Description
Processing Strategies
Memorising (Stepwise) Reproduction-directed Rote memorising, focusing on facts/definitions/characteristics
Analysing (Stepwise) Reproduction-directed Studying subject elements separately in a stepwise manner
Relating-Structuring (Deep) Meaning-directed Connecting and structuring subject elements into a whole
Critical (Deep) Meaning-directed Forming one’s own views and conclusions, critical of others’ drawn conclusions
Concrete Application-directed Applying knowledge practically, connecting with one’s own experience
Regulation Strategies
External Regulation Reproduction-directed External sources drive regulation (e.g., teachers, assessments, directions, provided questions)
Self-regulation Meaning-directed Regulation driven by self (e.g., planning learning activities, diagnosing problems, adjusting, reflection,
consulting self-sought materials)
Lack of Regulation Undirected Difficulties in regulating learning

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(Vermunt & Donche, 2017). Measurements at this magnification could support meaningful
comparisons, within and between cultures.
Cross‑cultural learning patterns (ILS) research
Cross-cultural studies employing the ILS have compared contexts using “Western” and
“Asian” labels, revealing differences in learning strategy use. Marambe etal. (2012) ana-
lysed ILS responses from Sri Lanka, Indonesia and the Netherlands. The Asian learner
stereotype (i.e., including a propensity for rote learning) did not extend to Sri Lanka, which
presented the lowest memorising scores. Vermunt etal. (2014) further investigated sam-
ples from Hong Kong, Spain, Mexico, Colombia, Venezuela, together with the three sam-
ples from Marambe etal. (2012) by comparing sample-level factor analyses on mean scale
responses. In most contexts, a meaning-directed pattern was identified by the presence of
deep processing (both relating-structuring and critical) and self-regulation. However, the
pattern also included unconventional yet meaningful loadings for concrete processing, ana-
lysing and external regulation. Lack of regulation frequently loaded with a reproduction-
directed pattern, which traditionally included only external regulation. Intra-continental
differences between samples were often greater than inter-continental differences.
Martínez-Fernández and Vermunt (2015) conducted a path analysis with Latin-Amer-
ican and Spanish samples. External regulation which is a reproduction-directed strategy,
predicted meaning-directed strategies (self-regulation directly and deep processing indi-
rectly). These unexpected connections were labelled the Spanish/Latin-American learner
paradox. These students more closely resembled Asian students, reporting a mix of strate-
gies from different learning patterns.
The current study shifts away from geographic labels (e.g., Asian, Western, Spanish/
Latin-American) and investigates the specificities and generalisabilities of the reviewed
findings at within-nation and cross-national levels using widely accepted cultural dimen-
sions (Hofstede, 2001; Triandis etal., 1988).
Individualism‑collectivism andpower distance
Instead of geographical location, individualism-collectivism and power distance might
provide more accurate descriptions of how learning strategies are used across cultures.
Individualism-collectivism describes the extent to which individuals in a culture strive
for individual or collective goals and has often been discussed alongside student learning
(Aparicio etal., 2016; Hofstede, 2001; Marambe etal., 2012; Tapanes etal., 2009; Wat-
kins, 2000). Individualistic cultures may strongly value learning itself whereas collectiv-
istic societies may value skills and formal accreditations (Hofstede, 2001). Students from
more individualistic societies have been found to be likely to pursue achievement and mas-
tery goals (e.g., understanding and gaining knowledge) whereas performance goals (e.g.,
demonstrating one’s ability to others) were more salient in collectivistic cultures (Dekker
& Fischer, 2008). These factors may be related to students undertaking different learning
strategies in different cultural contexts (Marton etal., 2005; Vermunt etal., 2014).
Power distance describes the degree to which a culture’s less powerful members expect
and accept unequal power distribution, which in education clarifies the role of the teacher
and nature of the student–teacher relationship (Hofstede, 2001). In high-power distance
cultures, teachers are revered for their knowledge. Education is more student-centred in
low-power distance societies, with students and teachers being viewed as equals. Wierstra

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etal. (2003) found higher perceptions of reproduction-oriented learning in southern Euro-
pean countries that have higher power distance compared to students from north-western
European and German speaking countries.
Individualism-collectivism and power distance can be represented at the nation level by
the IDV (Individualism index) and PDI (Power Distance Index) respectively. These indices
were derived from large-scale studies, providing relative values between nations (Hofstede,
2001). Concerns on face validity, theoretical underpinning, and reliability of the IDV led
to the development of IDV-COLL, an updated index which presents stronger correlations
on known associated variables (e.g., Coefficient of Human Inequality, Rule of Law Index;
Minkov etal., 2017). The nation-level values of the IDV, IDV-COLL and PDI will be used
to test learning strategy relationships along these cultural dimensions.
Person‑centred analysis
Person-centred analyses such as LPA provide advantages over variable-centred methods,
namely finding and analysing differences within samples (Hickendorff etal., 2018). LPA
quantitatively identifies 1) specific profiles within a sample and 2) individuals’ probabili-
ties of membership to each profile. Qualitative level and shape differences between profiles
and theory then support the interpretation of the profiles (Morin & Marsh, 2015). Level
differences between profiles describe a similar increase or decrease across most or all
analysed scales. Shape differences between profiles describe uneven differences between
scales and can characterise the use of different or dominant combinations of learning strat-
egies (e.g., meaning-directed vs. reproduction-directed strategies).
In this study, qualitative cultural inferences are made on these profile differences found
within and between samples. LPAs are employed for each sample individually to identify
within-sample profiles. Second, profiles between samples are qualitatively compared.
Aims
The current study was guided by the following research questions and hypotheses.
RQ1) Variable-centred analysis: How do learning strategies of higher education students
as measured by the ILS vary across international contexts on individualism-collectivism
and power distance (IDV/IDV-COLL/PDI; Hofstede, 2001; Minkov etal., 2017)?
Hypothesis 1: Consistent with learning patterns research (Vermunt & Donche, 2017),
positive correlations within meaning-directed strategies (i.e., relating-structuring and
critical processing, and self-regulation) and within reproduction-directed strategies (i.e.,
memorising and analysing processing, and external regulation) were expected. Lack of
regulation (characterised by little use of learning strategies and representing the undi-
rected pattern) was expected to not positively correlate with any other learning strategy.
Hypothesis 2: The Asian and Spanish/Latin-American learner paradoxes sug-
gest that more individualistic and lower power distance contexts would present
smaller correlations between inter-learning-pattern strategies, while less individ-
ualistic and higher power distance contexts would report larger inter-learning-
pattern strategy correlations (Hofstede, 2001; Martínez-Fernández & Vermunt,
2015; Marton etal., 2005; Minkov etal., 2017). The hypothesis would be sup-

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ported by significant and meaningful multiple regression analyses with each of 1)
IDV and PDI, and 2) IDV-COLL and PDI as simultaneous predictors.
Hypothesis 3: Alexander’s (2003) and Hattie and Donoghue’s (2016) learning
models proposed a progressive use of stepwise (surface) towards deep process-
ing strategies. Therefore, ILS processing strategies would be organised across a
progressive continuum: memorising, analysing, relating-structuring and critical
(Table1). Higher pairwise correlations between adjacent strategies across sam-
ples and overall (i.e., mean across samples) were expected.
RQ2a) Person-centred analysis: How do learning strategy profiles vary according to
level and shape within nations?
Hypothesis 4: Collectivistic high-power distance contexts are expected to present
profiles that support a blended use of learning strategies (e.g., meaning-directed,
application-directed and reproduction-directed) predominantly differing in level
(Martínez-Fernández & Vermunt, 2015; Vermunt et al., 2014). Individualistic,
low-power distance contexts would present different profile shapes that are con-
sistent with strategies employed in learning patterns research (Table1; Vermunt
& Donche, 2017).
RQ2b) Person-centred analysis: What profiles exist across cultures and how are they
represented across different contexts?
Hypothesis 5: Profiles found across sample LPAs would be consistent with origi-
nal learning patterns research (Vermunt & Donche, 2017). Profiles would present
higher levels of learning strategies consistent with the four learning patterns’
representative strategies and lower levels of other strategies (Table1).
Methods
Meta‑analytic re‑analysis
Rather than amalgamating and analysing the effects reported in individual studies,
entire datasets were obtained and (re-)analysed. Researchers who had previously pub-
lished studies employing the ILS in higher education contexts were contacted to obtain
their datasets. A sample size of n = 250 served as a guiding minimum to identify pro-
files. Some researchers provided previously unpublished samples. All received datasets
were used in the analysis. Where required, ethics clearances were granted for the origi-
nal data collection by their corresponding institutional review boards. The resulting
cross-national dataset included one sample from each of Venezuela, Indonesia, China,
Hong Kong, Spain, the Netherlands and two samples from Belgium, totalling n = 4883
students. The samples from Indonesia, the Netherlands and Hong Kong have been
analysed previously in cross-cultural research (Marambe etal., 2012; Vermunt etal.,
2014; The Hong Kong sample here contains only the tertiary education component
from the original sample). Table2 presents the collection method, year, sample size,
previous publishing status, students’ faculty composition and nation-level IDV, IDV-
COLL and PDI nation values for each sample.

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Instruments andindices
The ILS surveys were completed in the sample’s national language with items measured
from 1 (Seldom/Never) to 5 (Almost Always). Focusing on learning strategies, only the
ILS scales described in Table1 were analysed in the current study. For the samples from
Hong Kong and China, these scales totalled 50 items (100-item ILS; Law & Meyer, 2011;
Yu etal., 2021). In all other samples, the scales totalled 55 items (120-item ILS; Vermunt,
1996, 1998). Scales varied by at most one item between the two versions. The IDV, PDI
and IDV-COLL index values for the samples’ origin countries were obtained directly from
the large-scale studies of Hofstede (2001) and Minkov etal. (2017). As the current study’s
samples were collected over several decades, IDV-COLL provides an updated measure of
individualism-collectivism (Minkov etal., 2017).
Data analyses
Missing data for all datasets (< 1% overall) were imputed in R v.3.5.2 using multiple
imputed chain equations (mice package). Descriptive statistics, composite reliability
(≥ 0.60 acceptable; Raykov, 1997; Tseng etal., 2006), correlations and multiple regression
(lm, lm.beta, vif) were calculated using R (RQ1, Hypotheses 1–3). Multicollinearity was
not considered problematic if r < 0.90 (Tabachnick & Fidell, 2007). Cutoffs for small/mod-
erate/large effects were given by r = 0.10/0.30/0.50 (Cohen, 1992). LPAs on the imputed
datasets were conducted using Mplus 7.0 (Muthén & Muthén, 1998–2013; RQ2a/2b,
Hypotheses 4&5). Code for all analyses is provided in Appendix E.
To address RQ1, pairwise learning strategies correlations means and ranges were cal-
culated for each sample. For Hypothesis 1, sample-level correlations between meaning-
directed strategies and sample-level correlations between reproduction-directed strategies
were examined. Sample-level correlations between each of application-directed, reproduc-
tion-directed and meaning-directed strategies with lack of regulation tested the existence of
the undirected pattern.
For Hypothesis 2, inter-learning-pattern strategy correlations were examined alongside
corresponding nation-level values of individualism-collectivism (IDV, IDV-COLL) and
power distance (PDI) index values (Hofstede, 2001; Minkov etal., 2017). Two multivari-
ate multiple regressions were conducted: 1) IDV and PDI values as independent variables,
and 2) IDV-COLL and PDI values as independent variables. The dependent variables for
both analyses were sample mean correlations between a) application-directed with mean-
ing-directed strategies, b) application-directed with reproduction-directed strategies, and c)
meaning-directed with reproduction-directed strategies.
For Hypothesis 3, mean pairwise correlations across samples for each pair of memo-
rising, analysing, relating-structuring and critical processing were compared to test
how closely each processing strategy related to adjacent strategies in the hypothesised
progression.
Answering RQ2a and RQ2b (Hypotheses 4&5), LPAs tested fit for one to seven pro-
files. The final model (i.e., number of profiles) for each sample was chosen based on best
agreement between indicators and guiding principles. Indicators included three informa-
tion criteria: Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC)
and sample-size adjusted BIC (SABIC) (Akaike, 1987; Schwartz, 1978). Model choice
was supported by a minimum or elbow in BIC, AIC and SABIC (Nylund etal., 2007). An

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Table 2 Sample details
Ranges: IDV (6–91), IDV-COLL (-291(-177second smallest)–182), PDI (11–104)
nPublishing status Student majors Collection
method/
year
Year of study IDV IDV-COLL PDI
Venezuela 267 Martínez-Fernández
(Unpublished) Social Sciences, Engineering (University) In-class
2016 1st-year 12 –95 81
Indonesia 888 Ajisuksmo and Vermunt (1999) Management, Accountancy, Law, Business Administration,
Electrical Engineering, Mechanical Engineering Students
(University)
In-class
1991 1st-year 14 –171 78
China 318 Yu etal. (2021) Natural sciences, Engineering and Technology, Humanities,
Social science, Medicine (University) In-class
2015 All 20 –31 80
Hong Kong 444 Law and Meyer (2011) Business Administration, Hospitality/Tourism, Language
Studies (Technical School/University) In-class
2005 All 25 –5 68
Spain 242 Shum etal. (2021) Psychology (University) In-class
2016 2nd-3rd years 51 58 57
Belgium 1 1058 Donche etal. (2013) Communication sciences, Electromagnetics, Hotel manage-
ment, Journalism, Office management, Business manage-
ment, Social work, Teacher education (Technical School/
University)
Online
2005 1st-year 75 110 65
Belgium 2 871 Donche (Unpublished) Communication sciences, Information management sys-
tems, Management assistant, Tourism/recreation, Business
management (Technical School/University)
In-class
2003 1st-year 75 110 65
The Netherlands 795 Vermunt (1998) Law, Economy, Econometry, Management information
sciences, Sociology, Psychology, Language and literature,
and Philosophy (University)
Mail
1988 1st-year 80 182 38

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entropy criterion summarised all posterior probabilities derived by the model, where val-
ues closer to one indicate better separation between individuals’ probabilistic membership
to profiles (Celeux & Soromenho, 1996). Furthermore, Vuong-Lo-Mendell-Rubin and Lo-
Mendell-Rubin Likelihood Ratio Tests support a model whose test is significant but is not
significant for a model with one more profile (Lo etal., 2001; Vuong, 1989). Lastly, model
selection was guided by theoretical meaning of the learning strategy combinations associ-
ated with the profiles, profiles satisfying minimum membership (
≥
10% of sample size)
and inclusion of similar profiles from a fewer-profiles model.
Results
Composite reliability (Table3) measures were acceptable except for some marginal values
in lack of regulation (Hong Kong
∶𝜌
=0.58) and memorising (Indonesia
∶𝜌
=0.59; Hong
Kong
∶𝜌
=0.59). Descriptive statistics are provided in Table3. Composite reliability is pre-
ferred to Cronbach’s alpha (see Appendix A) as item loadings are not assumed to be uni-
form (Raykov, 1997).
Research question 1 (variable‑centred analyses)
Both Hypotheses 1 and 2 tested correlations between scale responses within each sample.
Full correlation tables are presented in Appendix B.
Addressing Hypothesis 1, all correlations between meaning-directed strategies were
large across all samples (r = 0.50–0.75). Except for the Hong Kong sample, meaning-
directed strategies did not meaningfully positively correlate with lack of regulation (i.e.,
undirected pattern; Hong Kong sample: r = 0.09–0.17; all other samples: r = -0.22–0.09).
All within reproduction-directed strategies correlations were moderate to large across all
samples (r = 0.37–0.61). All but the Indonesian sample presented at least one small/moder-
ate correlation between reproduction-directed and undirected strategies (r < 0.34). Only the
Hong Kong sample presented a moderate correlation between application-directed strate-
gies and lack of regulation (r = 0.17).
Bonferroni-corrected multivariate multiple regression analyses testing Hypothesis 2
are presented in Table 4 (dependent variable inter-learning pattern correlation means)
and Table 5. Sample application-directed with reproduction-directed strategy correlations
(dependent variable) were meaningful and significant when regressed onto IDV and PDI val-
ues (independent variables; F(2,5) = 15.78, p < 0.05, R2 = 0.86, R2adjusted = 0.81), and when
regressed onto IDV-COLL and PDI values (independent variables; F(2,5) = 11.62, p < 0.05,
R2 = 0.82, R2adjusted = 0.75). Sample meaning-directed/reproduction-directed strategy correla-
tions (dependent variable) were meaningful and significant when regressed onto IDV and
PDI values (independent variables; F(2,5) = 29.84, p < 0.01, R2 = 0.92, R2adjusted = 0.89), and
when regressed onto IDV-COLL and PDI values (independent variables; F(2,5) = 20.17,
p < 0.01, R2 = 0.89, R2adjusted = 0.85). Neither IDV and PDI, nor IDV-COLL and PDI were sig-
nificant predictors for meaning-directed with application-directed strategy correlations.
The significant multiple regressions presented mixed results for significant individual
predictors. These might be partly due to small sample size (n = 8) and closely related inde-
pendent variables (IDV/PDI: r = -0.79; IDV-COLL/PDI: r = -0.83). These do not exceed
either correlation (|r|< 0.90) or Variance Inflation Factor (VIF < 5; Menard, 2001; IDV/
PDI: VIF = 2.68; IDV-COLL/PDI: VIF= 3.30) guidelines for multicollinearity.

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Table 3 Mean(Standard Deviations)/composite reliability: Rho
Overall: discrepancy between 50-item (Hong Kong, China), and 55-item surveys considered missing data
Memorising Analysing Relating-structuring Critical Concrete External regulation Self-regulation Lack of regulation
Venezuela 2.86(.87)/.77 3.14(.77)/.77 2.98(.77)/.80 2.89(.91)/.75 3.46(.82)/.73 3.26(.64)/.79 2.98(.71)/.81 2.60(.78)/.72
Indonesia 3.32(.80)/.59 2.92(.74)/.63 2.56(.81)/.75 2.18(.84)/.70 2.99(.75)/.65 3.19(.61)/.69 2.76(.73)/.79 2.65(.68)/.62
China 2.90(.69)/.61 2.67(.69)/.65 3.23(.75)/.80 2.94(.81)/.66 3.15(.76)/.73 3.04(.54)/.68 3.04(.68)/.81 2.68(.80)/.71
Hong Kong 2.87(.60)/.58 2.51(.57)/.68 2.42(.63)/.75 2.35(.69)/.70 2.81(.64)/.72 2.92(.52)/.74 2.55(.58)/.80 2.86(.61)/.58
Spain 2.88(.91)/.80 3.11(.65)/.69 3.38(.75)/.83 3.05(.83)/.72 3.74(.63)/.68 3.13(.58)/.64 2.87(.65)/.78 2.60(.70)/.64
Belgium 1 3.21(.84)/.73 2.92(.68)/.67 3.11(.77)/.81 2.58(.79)/.67 3.13(.72)/.68 3.18(.55)/.69 2.53(.64)/.78 2.74(.74)/.72
Belgium 2 3.19(.78)/.67 2.90(.66)/.63 2.89(.76)/.80 2.31(.76)/.66 2.84(.71)/.64 3.15(.52)/.63 2.35(.57)/.73 2.51(.69)/.68
The Netherlands 2.83(.96)/.79 2.72(.69)/.63 3.36(.83)/.83 2.81(.92)/.73 2.81(.80)/.73 3.27(.63)/.64 2.30(.68)/.80 2.41(.76)/.72
Overall 3.08(.84)/.71 2.85(.70)/.65 2.96(.83)/.82 2.54(.87)/.72 3.02(.77)/.70 3.16(.58)/.68 2.58(.69)/.79 2.62(.73)/.68

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Overall, the results support Hypothesis 2 suggesting that samples with lower individual-
ism and higher power distance index values correspond to greater uses of application-directed
together with reproduction-directed strategies, and meaning-directed together with reproduc-
tion-directed strategies.
For Hypothesis 3, memorising had the greatest overall correlations with analysing
(rmean = 0.47). Analysing had the greatest overall correlations with memorising and relating-
structuring (rmean = 0.47). Relating-structuring had the greatest overall correlations with ana-
lysing and critical (rmean = 0.60). Results are summarised in Table6 and Fig.1.
Table 4 Sample inter-learning
pattern correlation ranges
andmeans
Range: low/high
Application-
directed &
Meaning-
directed strate-
gies
Application-
directed &
reproduction-
directed strate-
gies
Meaning-
directed &
reproduction-
directed strate-
gies
Range Mean Range Mean Range Mean
Venezuela .64/.72 .67 .28/.62 .45 .23/.66 .48
Indonesia .58/.59 .58 .34/.51 .41 .24/.64 .44
China .60/.62 .61 .18/.40 .30 .10/.65 .39
Hong Kong .49/.60 .56 .35/.44 .40 .18/.64 .40
Spain .47/.60 .53 .01/.34 .21 .07/.62 .31
Belgium 1 .49/.57 .52 .11/.29 .22 .01/.48 .25
Belgium 2 .45/.50 .48 –.05/.17 .07 .01/.42 .19
The Netherlands .49/.53 .54 –.06/.05 –.01 –.16/.21 .02
Table 5 Bonferroni-adjusted multivariate multiple regression results
Independent variables (Table1): IDV and PDI, IDV-COLL and PDI. Dependent variables: inter-learning
pattern correlation means (Table4)
* p < .05,**p < .01
Application-directed
& meaning-directed
strategy
Application-directed &
reproduction-directed
strategies
Meaning-directed &
reproduction-directed
strategies
IDV and PDI – *(p = .02) **(p = .008)
R
2∕R
2
adjusted
.67/.54 .86/.81 .92/.89

𝛽
–.92/–.13 –.69*/.28 –.56*/.46
IDV-COLL and PDI – *(p = .04) *(p = .014)
R2
∕R
2
adjusted
.50/.30 .82/.75 .89/.85

𝛽
–.68/.03 –.68/.26 –.52/.46

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Research questions 2a and2b (Person‑centred analyses)
Sample LPAs
Supporting criteria for LPA models are summarised in Table7. Full tests and indicators are
provided in Appendix C. Figure2 presents the profiles and labelling for each sample. Full
profile means are presented in Appendix D. The following qualitative descriptions discuss
model selection considerations, considered alternative models, level and shape profile dif-
ferences (Hypothesis 4).
Venezuela (V) The three profiles present obvious level differences (i.e., similar differences
in most or all scales). Shape differences (i.e., differences in dominant strategies) were less
pronounced. V2 and V3 present middling and high levels of mixed strategies respectively.
V1 presents low scores indicative of an undirected learning pattern (i.e., higher lack of
regulation with little use of any processing strategies).
Table 6 Mean correlations(Standard Deviations) between processing strategies across samples
Memorising Analysing Relating-structuring Critical
Memorising –
Analysing .47(.06) –
Relating-Structuring .24(.20) .47(.17) –
Critical .01(.14) .37(.16) .60(.07) –
-0.2
0
0.2
0.4
0.6
0.8
Venezuela Indonesia ChinaHong
Kong
Spain Belgium 1 Belgium 2Netherlands
Memorising/Analysing Memorising/Relating-Structuring Memorising/Critical
Correlations for Memorising
-0.2
0
0.2
0.4
0.6
0.8
Venezuela Indonesia ChinaHong
Kong
Spain Belgium 1 Belgium 2Netherlands
Analysing/Memorising Analysing/Relating-Structuring Analysing/Critical
Correlations for Analysing
-0.2
0
0.2
0.4
0.6
0.8
Venezuela Indonesia ChinaHong
Kong
Spain Belgium 1 Belgium 2Netherlands
Relating-Structuring/Memorising Relating-Structuring/Analysing
Relating-Structuring/Critical
Correlations for Relating-Structuring
-0.2
0
0.2
0.4
0.6
0.8
Venezuela Indonesia ChinaHong
Kong
Spain Belgium 1Belgium 2N etherlands
Critical/Memorising Critical/Analysing Critical/Relating-Structuring
Correlations for Critical
Fig. 1 Sample-level processing strategies correlations. Hypothesised progression: Memorising, Analysing,
Relating-Structuring, Critical. Correlations are generally greatest with neighbouring strategies

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Indonesia (I) The four profiles present level differences. I1, I2 and I3 present different
amounts of dominant reproduction-directed learning strategies. I1’s low scores suggest an
undirected learning pattern. I4 presents high levels of mixed strategies. Decreases in lack
of regulation corresponds to relative increases in all other learning strategies.
China (C) Level differences are pronounced while shape differences are minor. C1 presents
low overall values suggesting an undirected pattern. C2 presents high values in all but lack
of regulation suggesting a rounded strategy approach.
Hong Kong (H) The three profiles have similar shapes. Low scores in H1 and H2 are indic-
ative of an undirected pattern. H3 presents middling scores in meaning-directed, reproduc-
tion-directed and application-directed strategies.
Spain (S) A five-profile solution was chosen over a three-profile solution (supported by a
BIC elbow) because of a minimum in BIC and inclusion of similar profiles (S2, S3, S5).
There are clear shape differences among profiles. S1 presents higher reproduction-directed
strategies while S4 is balanced across strategies. S2, S3 and S5 present progressively
higher meaning-directed and application-directed strategies.
Belgium 1 (B1) A four-profile model was chosen over the two-profile model (supported
by an elbow) due to support from log-likelihood ratio tests and inclusion of similar pro-
files (B13, B14). Shape differences are prominent. B11 and B14 present greater mean-
ing-directed strategies, whereas B12 and B13 report greater use of reproduction-directed
strategies.
Belgium 2 (B2) There are obvious shape differences between the four profiles. B21, B23
and B24 have similar shape, presenting higher scores for reproduction-directed strategies
over meaning-directed strategies. Students in B22 reported greater meaning-directed and
application-directed strategies.
The Netherlands (N) The five-profile model includes a profile with marginal membership
size (9%, n = 74). Three prominent shapes are presented: N2 and N3, N1 and N4, and N5.
Table 7 LPA Model selection criteria
Chosen
model
(Profiles)
Supporting criteria
Information criteria
(AIC,BIC,SABIC) Log-likelihood
ratio tests Entropy Minimum profile
membership size
Venezuela 3 √(Elbow) √ √ 27%
Indonesia 4 √(Elbow) √ √ 10%
China 2 √(Elbow) √ 46%
Hong Kong 3 √(Elbow) √ √ 20%
Spain 5 √(Minimum) 12%
Belgium 1 4 √ 19%
Belgium 2 4 √(Elbow) √ √ 16%
The Netherlands 5 √(Minimum) √ 9%

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N2 and N3 have differing levels of reproduction-directed strategies. N1 and N4 present
different levels of a meaning-application-directed mixture. N5 represents a high strategic
use of learning strategies, reporting high amounts of all processing strategies and external
regulation.
Fig. 2 Sample LPA Profiles and Membership, Relating – Relating-structuring

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Overall results The four samples corresponding to lowest individualism and highest
power distance index scores presented profiles with level differences and minimal shape
differences. The remaining samples presented clear shape profile differences with some
level differences (Hypothesis 4). Addressing Hypothesis 5, profiles across samples pre-
sented mixed consistency with learning patterns research. All except for the Spanish and
Dutch samples presented a profile with all around low levels of learning strategies (V1,
I1, C1, H1, H2, B13, B23), qualitatively reminiscent of the undirected profile. Consistent
with Vermunt etal. (2014), many of these profiles presented relatively higher levels of
memorising, analysing and external regulation. The Dutch, Venezuelan (N2, V2, applica-
tion-directed: concrete processing) and Spanish (S2, external regulation) samples each pre-
sented profiles with only one dominant learning strategy. Profiles presenting a preference
for reproduction-directed strategies (memorising, analysing and external regulation; I2, I3,
S1, B12, B21, B24, N3) were common across samples. Profiles demonstrating a qualitative
preference for meaning-directed strategies (relating, critical processing and self-regulation)
over reproduction-directed strategies (C2, S3, S5, B22, N1, N4) also reported high lev-
els of application-directed strategies (concrete processing). Numerous samples presented a
profile with all-around high strategy use (both processing and regulation; V3, I4, H3, S4,
B11, B14, N5). This description is consistent with “Asian” and “Latin-American” learner
stereotypes described previously (Martínez-Fernández & Vermunt, 2015; Martonet al.,
2005). The results above are drawn only from qualitative similarities and differences of the
profiles across samples. The classifications above are not definitive and indicate qualitative
shape and level trends consistent (or inconsistent) to the existing literature.
Discussion
Two research questions investigated higher education learning strategies across eight inter-
national samples.
The first research question examined learning strategies using variable-centred meth-
ods at the sample and cross-sample levels. Within meaning-directed learning strategies and
within reproduction-directed strategies were strongly correlated in all samples, supporting
the ubiquitous existence of meaning-directed and reproduction-directed learning patterns.
Lack of regulation did not meaningfully positively correlate with meaning-directed strat-
egies in any sample and correlated moderately with memorising and external regulation
strategies in some samples (Hypothesis 1). Increasing collectivism and power distance cor-
responded to stronger mean correlations between each of meaning-directed strategies with
application-directed strategies and meaning-directed strategies with reproduction-directed
strategies (multivariate multiple regression analyses; Hypothesis 2). Individualism-col-
lectivism and power distance provide more accuracy in describing cultural differences in
learning strategies over broad geography-based Asian and Spanish/Latin-American learner
stereotypes. Correlations along the hypothesised memorising, analysing, relating-structur-
ing, critical processing continuum were largest among adjacent strategies and decreased
between pairs further apart (Hypothesis 3).
The second research question explored differences within and between samples using
person-centred analyses. Profiles from contexts corresponding to lower individualism
(Venezuela, Indonesia, China and Hong Kong samples) differed in level whereas profiles
from contexts with higher individualism presented obvious shape differences (Hypothesis
4). Profiles consistent with a reproduction-directed learning pattern and profiles consistent

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with an undirected learning pattern (overall low strategies) appeared consistently across
samples. Profiles that reported higher values for meaning-directed strategies over repro-
duction-directed strategies also reported higher application-directed strategies. Profiles
presenting both high meaning-directed and reproduction-directed strategies were present in
nearly all samples including individualistic (traditionally Western) contexts (Hypothesis 5).
Theoretical implications
While hypotheses were mostly confirmed, there were several unexpected findings includ-
ing cultural differences in employed learning strategies.
The greater correlations observed in the neighbouring strategies in the memorising, ana-
lysing, relating-structuring, critical continuum suggest that use of one strategy implies a
greater likelihood of using an adjacent strategy. Beginning with memorising, and that deep
processing yields higher quality outcomes (Vermunt & Donche, 2017), the results support
the sequential use of stepwise/surface followed by deep processing strategies described in
models involving learning strategies (Alexander, 2003; Hattie & Donoghue, 2016).
Previous cross-cultural studies found an application-directed pattern (whose only learn-
ing strategy is concrete processing) only in the Dutch sample (Vermunt, 1996, 1998). In
this study, concrete processing is reported alongside both meaning-directed and reproduc-
tion-directed strategies supporting some previous findings (Martínez-Fernández & Ver-
munt, 2015; Vermunt etal., 2014).
Cultural dimensions: individualism‑collectivism andpower distance
The results indicate that individualism-collectivism and power distance provide robust
frameworks to compare learning strategies across cultures. The results encourage a shift
away from generalisations based on geography towards comparing national contexts
against accepted cultural dimensions. As the samples’ original data collection occurred
over several decades, evolving cultural dimensions could influence the results. However,
the similar results obtained from multiple regression and sample LPAs using both the IDV
and the updated IDV-COLL indices support the cultural interpretations made in this study.
The relationships found from regression analyses lend support to students’ varied strat-
egy use in more collectivistic and higher power distance cultures. Higher multiple strategy
use is consistent with obtaining skills and qualifications through teacher-centred educa-
tion (in collectivistic, high-power distance contexts) over pursuing interests and valuing
the learning process indicative of student-centred education (in individualistic, low-power
distance contexts; Hofstede, 2001).
If student learning strategy development occurred between the profiles found within
each sample, students in primarily collectivistic cultures may likely increase in level but
not change shape. Such students focus on learning skills through teacher-centred instruc-
tion in high-power distance cultures. They may view that all processing strategies, self-
regulation and external regulation are required. Those in more individualistic low-power
distance contexts could also focus on how to learn and more readily adopt different profile
shapes (Hofstede, 2001).
The results also address questions regarding “Western” patterns of learning strategy
use, namely that Western students might employ combinations of memorisation and under-
standing (Kember, 2016; Leung et al., 2008). The Asian and Spanish/Latin-American
learner paradoxes are described by higher levels of both stepwise and deep processing and

Higher Education
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both external and self-regulation strategies respectively (Martínez-Fernández & Vermunt,
2015; Marton etal., 2005; Watkins, 2000). Profiles that qualitatively fit these descriptions
are consistently found in traditionally “Western” contexts (e.g., S4, B14, and N5). There-
fore, the results of this study question the existence and validity of these stereotypes.
Practical implications
Students’ learning strategy use is influenced by cultural factors, teaching practices and
learning practices. Tightly tied to a student’s self-regulation and willingness to be exter-
nally regulated are the teaching practices that support them. Process-oriented instruction
describes the relationship or “friction” between a student’s self-regulation paired with a
teacher’s external regulation (Vermunt & Verloop, 1999). This friction can be constructive
(e.g., when students have low self-regulation and teachers challenge students by expecting
a shared role of regulation) or destructive (e.g., when neither students nor teachers pro-
vide the necessary regulation to succeed). Power distance may hinder or bolster this guid-
ance through its effects on teacher-student relationships (Hofstede, 2001). Teachers can
have strong effects when intervening which if not carefully planned, could impede learning
strategy use and development.
The qualitative interpretations of the profiles found across sample-LPAs have implica-
tions on teacher regulation practices. Students belonging to profiles that have characteris-
tics qualitatively similar to an undirected pattern present low self-regulation and moder-
ate external regulation. These together would suggest constructive friction led by teacher
external regulation (Vermunt & Verloop, 1999). The accompanying low levels of process-
ing strategies suggest that teachers should begin by instructing, facilitating and assessing
the use of stepwise processing (memorising then analysing). As students’ strategy use and
domain knowledge develop, teachers should gradually shift towards supporting deep pro-
cessing (relating-structuring then critical). These practices are consistent with results found
in the proposed processing strategies continuum and learning strategy models (Alexander,
2003; Hattie & Donoghue, 2016).
Profiles demonstrating a preference for memorising, analysing and external regulation
are qualitatively reminiscent of a reproduction-directed pattern. In higher-power distance
samples, these profiles present moderate self-regulation which could suggest possible
destructive friction (Vermunt & Verloop, 1999). These students may benefit from a taper-
ing-off teaching approach, allowing development of processing strategies with less guid-
ance. In lower-power distance samples, these profiles present lower accompanying self-
regulation and may require additional external support in employing relating-structuring,
critical and concrete processing strategies.
Profiles with higher levels of meaning-directed strategies (relating, critical process-
ing and self-regulation) generally presented comparable levels of external regulation and
concrete processing (Vermunt etal., 2014). Employing student-centred teaching practices
could support constructive friction in learning strategy development through shared regula-
tion (Vermunt & Verloop, 1999).
In profiles with all-around high strategy use, high levels of both external and self-
regulation would traditionally suggest destructive friction (Vermunt & Verloop, 1999).
However, accompanying high levels of all processing strategies would signify that
these students act opportunistically, using strategies that best suit their achievement
goals. Especially in higher-power distance contexts, careful design of higher quality

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learning outcomes requiring deep processing could improve the quality of learning
(Vermunt & Donche, 2017).
Teachers may need to swim against the current of culturally accepted teacher
regulation practices to support students’ learning strategy development according to
the profiles. Supporting student development of processing strategies is an enduring
practice that must go beyond introduction of novel techniques (Brown etal., 2017).
Process-oriented instruction requires teachers to first diagnose learners and determine
the appropriate level of external regulation (i.e., strong, shared, loose) along with cor-
responding effective instructional strategies (Vermunt & Verloop, 1999). Teachers
should model appropriate learning strategies (e.g., self-testing, looking for applica-
tions), challenge and activate students to use and practice them, and promote the scaf-
folding and the instruction of processing techniques (e.g., Dignath & Veenman, 2020).
The current study draws attention to the abundant use of reproduction-directed
strategies across student profiles, and their complementary role to meaning-directed
strategies. Memorising and analysing may require support as a potential precursor to
deep processing strategies. Teachers should focus on facilitating the interplay and tran-
sition towards deep processing (Alexander, 2003; Hattie & Donoghue, 2016). Once
ready, deep processing strategies required in innovative teaching approaches such as
problem-based, project-based and self-directed specialisation learning could be devel-
oped using appropriate teacher-regulation strategies (e.g., Vermunt, 2007). While stu-
dents should be supported to demonstrate deep processing in achieving meaningful
outcomes, students may require varying pathways to get there.
Limitations andfuture directions
The current study’s results were based on data from a single self-report instrument.
This limitation is balanced by the large-scale and international nature of the study. The
ILS and other long multi-construct surveys used in higher education learning research
were designed prior to widespread use of latent approach to predictive analysis. As a
result, responses to these instruments generally yield marginal fit on construct valid-
ity tests (e.g., Confirmatory Factor Analyses; García & Pintrich, 1996; Midgley etal.,
2000). However, meta-analyses are limited to the available results and datasets. Shum
etal. (2021) presented a systematic method to remove items to improve fit. This prac-
tice was not adopted to maintain consistency with previous cross-cultural studies and
LPA being a mean-based, rather than a latent construct approach to measurement and
analysis (Marambe etal., 2012; Vermunt etal., 2014).
Change in culture over time and within-nation cultural differences may also pose
limitations. Despite using an updated index (IDV-COLL in addition to IDV), this limi-
tation can only be redressed by continuing research refining cultural dimensionsresults.
Noncultural contextual factors may also play a role. For example, first-year students
might place a stronger emphasis on rote learning as a survival strategy. Increasing dif-
ferentiation between meaning-directed and reproduction-directed learning strategies
has been observed as students progress through formal education (Vermunt & Ver-
metten, 2004). A wider range of educational contexts would help clarify the results.
Future studies could consider the full model of learning patterns and outcome vari-
ables (e.g., achievement, GPA) to capture a more comprehensive picture of students’
experience.

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Conclusion
Learning strategies were compared across eight samples in seven countries employing
Hofstede’s individualism-collectivism and power distance cultural indices taking vari-
able-centred and person-centred meta-re-analysis approaches. Variable-centred results
revealed the increasing prevalence of all-around strategy use in more collectivistic high-
power distance contexts. Latent profile analyses supported variable-centred results,
finding that more collectivistic high-power distance contexts presented profiles with
primarily level differences, suggesting mixed use of learning strategies. Individualistic
low-power distance contexts presented different shapes of learning strategies. Further-
more, traditionally “Western” contexts often presented at least one profile that described
students employing learning strategies consistent with “Asian” and “Latin-American”
stereotypes. The results point to the limitations of geographical learner stereotypes and
support the use of individualism-collectivism and power-distance as cultural dimen-
sions to describe cultural differences in learning strategies. Future cross-cultural studies
on learning strategies should be supplemented with person-centred analyses to account
for varying practices within and across contexts.
Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s10734- 023- 01062-4.
Data availability The data is not publicly available.
Declarations
Conflicts of interest/competing interests The authors have no relevant financial or non-financial interests to
disclose.
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Authors and Aliations
AlexShum1· LukeK.Fryer1· JanD.Vermunt2· ClaraAjisuksmo3· FranciscoCano4·
VincentDonche5· DennisC.S.Law6· J.ReinaldoMartínez‑Fernández7·
PeterVanPetegem5· JiYu8
* Alex Shum
alexshum@hku.hk
Luke K. Fryer
fryer@hku.hk
Jan D. Vermunt
j.d.h.m.vermunt@tue.nl
Clara Ajisuksmo
clara.as@atmajaya.ac.id
Francisco Cano
fcano@ugr.es
Vincent Donche
vincent.donche@uantwerpen.be
Dennis C. S. Law
dlaw@cihe.edu.hk
J. Reinaldo Martínez-Fernández
JoseReinaldo.Martinez@uab.cat
Peter Van Petegem
peter.vanpetegem@uantwerpen.be
Ji Yu
yuji2020@mail.tsinghua.edu.cn
1 Centre fortheEnhancement ofTeaching andLearning, University ofHong Kong, Pokfulam, SAR,
HongKong, China
2 Eindhoven School ofEducation, Eindhoven University ofTechnology, Eindhoven, Netherlands
3 Faculty ofPsychology, Atma Jaya Catholic University ofIndonesia, SouthJakarta, Indonesia
4 Faculty ofPsychology, University ofGranada, Granada, Spain
5 Department ofTraining andEducation Sciences, Faculty ofSocial Sciences, University
ofAntwerp, Antwerp, Belgium
6 Caritas Institute ofHigher Education, HongKong, SAR, China

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7 Department ofCognitive, Developmental andEducational Psychology, Faculty ofEducational
Sciences, Universitat Autónoma de Barcelona, Bellaterra, Spain
8 Institute ofEducation, Tsinghua University, Beijing, China