An Intervention-Based Active Learning Strategy Employing Principles of Cognitive Psychology
Bibliographic record
Abstract
The objective of this research is to investigate an intervention-based active learning strategy incorporating the principles of cognitive psychology to enhance student learning in an undergraduate engineering mathematics course. In this strategy, the classroom was completely flipped, i.e., the students were assigned weekly reading assignments and had to take a quiz before joining the classroom. Inside the classroom, the lectures were replaced with group-problem solving sessions. Specifically, students were divided into small groups where they collectively solved worksheets containing several problems. By design, the worksheets integrated the key principles of cognitive science in learning that are conducive to long term retention of the topics, namely, reinforcement, spacing and instant feedback. Subsequently, the students were given take-home practice problem sets to master the concepts. On comparing the student learning outcomes from this strategy with the outcomes from the traditional lecturing approach, it was found that the students indulging in the carefully designed active learning environment performed better. It can be concluded that the improved student learning and retention can be attributed to the combination of active learning and the effective intervention strategy employed in the course
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".