Efficacy of Learning Modules to Enhance Study Skills
Bibliographic record
Abstract
A key roadblock in students' success in higher education involves a lack of preparedness.That is, students may be ill-equipped for demands inherent in the pursuit of an academic degree (such as managing one's time, taking class notes, and preparing for examinations).To address these missing resources, educators have turned to offering students learning modules as face-to-face or online educational supplements.Two studies were conducted to investigate both the usefulness and effectiveness of learning modules for students in an introductory psychology course.Specifically, we compared students' midterm and examination scores by those who received two modular skill sets (both examination-taking strategies and time management) before or after the course midterm.Students' relative levels of each of perceived motivation, interest, and effectiveness of the modules were measured at the conclusion of the course.Results showed a significant association between module receipt and improved performance on the midterm and final examination, regardless of when the modules were presented (that is, either before or after the midterm).Additionally, students who completed the modules indicated that they enjoyed them, scoring significantly higher on their final examination.Based on these results, we encourage instructors and educational developers to design and offer learning modules to students (in first-year courses in particular) to enhance student success across their college or university experience.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".