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Record W7116276331 · doi:10.1177/07342829251409015

Measuring Self-Regulated Learning for Chinese International and Canadian Domestic Undergraduate Students in Canada

2025· article· en· W7116276331 on OpenAlexafffundabout
Meng Qi Wu, Allyson F. Hadwin, Sungjun Won, Ramin Rostampour

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2025
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMeasurement invarianceExtant taxonStudent engagementMetric (unit)Cultural diversityCross-culturalCross-cultural studies

Abstract

fetched live from OpenAlex

Self-regulated learning (SRL) is essential for academic success. Research shows that culture influences the engagement in SRL processes. However, there are limited cross-cultural studies investigating SRL; therefore, we aimed to validate an SRL measurement for Chinese international and Canadian domestic undergraduate students and to examine if the relationships between SRL engagement and academic performance differ across the two groups. First, we investigated the measurement invariance of the Regulation of Learning Questionnaire for both groups. Our findings confirmed the metric invariance across these cultural groups and identified items contributing to the scalar non-invariance. Additionally, we found that goal management was significantly associated with Canadian students' course grades only, at the middle and end of a 4-month academic semester. In contrast, monitoring was significantly associated with semester GPA for both groups at the middle of the semester. Our findings emphasize the significance of evaluating measurement invariance across cultures and contribute to the extant literature on the relationship between culture and SRL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.434
Teacher spread0.410 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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