Interaction Effects of Level and Instability of Motivation on Learning Strategies: Introjected and Identified Regulation
Bibliographic record
Abstract
This study examined the interaction effect of the level and instability of motivation on different learning strategies in university learning at the contextual level. Two motivation levels—introjected and identified regulation—and three types of learning strategies—metacognitive, writing-repetition, and deep-processing—were measured. Self-reported questionnaires were administered to students from two universities in Japan; data of 307 students were included in the analysis. A hierarchical multiple regression analysis on metacognitive and deep-processing strategies revealed an interaction effect of identified regulation and instability of motivation. The results of a simple slope analysis showed that identified regulation had no effect on metacognitive and deep-processing strategies during high instability of motivation. However, during low instability of motivation, higher identified regulation enabled greater use of metacognitive and deep-processing strategies. On the other hand, there was no an interaction effect of level and instability of motivation on writing-repetition strategies. These results revealed the significant role of the level and instability of motivation in the application of metacognitive and deep-processing strategies.
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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 | 0.001 | 0.008 |
| 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) | 0.001 | 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".