Reciprocal Relationship Between Motivation and Engagement in Out-of-Class Learning Among Japanese Undergraduates
Bibliographic record
Abstract
This study aimed to examine the reciprocal relationship between motivation and engagement in out-of-class learning among Japanese undergraduates by using a cross-lagged panel model. Two online surveys were conducted with 293 university students in Japan. This study measured motivation and engagement with regard to out-of-class tasks (homework) for compulsory classes relating to the university students’ major subjects. Hypothesis 1 posited that there would be a positive reciprocal relationship between intrinsic/identified regulation and engagement in out-of-class learning. Hypothesis 2 posited that there would be a negative or no reciprocal relationships between introjected/external regulation and engagement in out-of-class learning. The results of the cross-lagged panel model partially supported each hypothesis. A positive reciprocal relationship between intrinsic regulation and emotional engagement was found. In addition, identified and introjected regulation in the first survey were positively correlated with behavioral engagement in the second survey. However, no relationship was observed between external regulation and engagement. Thus, intrinsic, identified, and introjected regulation were revealed as important factors in promoting engagement in out-of-class learning. Based on this study’s findings, practical implications for education regarding motivation and engagement in out-of-class learning were proposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".