Reflections on Active Teaching and Learning of Research Methodology from Undergraduates’ and Instructor’s Perspectives
Bibliographic record
Abstract
Research methodology courses are challenging for students and instructors. They demand students and teachers to master abstract knowledge of the content. Therefore, the present study attempted to shed light on active teaching and learning—an instructional approach that engages learners in interactions and reflection on learning—of research methodology from the perspectives of undergraduates and the course instructor at Majmaah university. Classroom observations and follow-up interviews with 14 undergraduate students and the course instructor were conducted to achieve this. While the participants highlighted some benefits (learning about research methods, enhancing their assignments, raising their interests in research methodology, learning through group work, and discussions and feeling satisfied and self-confident), they also faced several challenges (content-related challenges, task-related challenges, and active learning-related challenges). Thus, the study offers useful theoretical and pedagogical implications for instructors and future research on circumventing challenging issues in such courses.
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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 | 0.034 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".