The Importance of Various Indicators of Active Learning on the Enhancement of Saudi Students’ Motivation and English Achievement: An Experimental Study
Bibliographic record
Abstract
This experimental study sought to examine the impact of four indicators of active learning (i.e., elaborated feedback, group work, situated learning, and videos and pictures in classroom instruction) on the enhancement of Saudi students’ various motivational constructs (i.e., self-efficacy, task value, and effort expenditure) and English achievement. Participants were 289 university students, and the data were collected at three time points: Time 1 (before the intervention), Time 2 (in the middle of the intervention), and Time 3 (after the intervention). The findings of repeated measures ANOVA and follow-up t-tests revealed that the intervention had small impacts on all the variables at Time 2 (in the middle of the intervention). However, at Time 3 (after the intervention), the intervention had small impacts on effort expenditure and task value, a moderate impact on academic achievement, and a large impact on self-efficacy. In general, the evidence obtained provides important implications for educational practices and further research development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".