Motivation and Cognitive Strategies in the Choice to Attend Lectures or Watch Them Online
Bibliographic record
Abstract
This study explored relations between students' motivational and cognitive orientations as assessed by the Motivated Strategies for Learning Questionnaire (MSLQ), and their attitudes and choices relating to online lecture viewing. Examination performance was also assessed to determine if there were particular affinities between certain motivational or cognitive orientations and success in learning by attending lectures or watching them online. The results of regression analyses revealed that students who considered the course interesting and important and who were motivated extrinsically to do well in it, expressed particularly positive attitudes towards the option to watch lectures online. Students who did not particularly want to learn in interaction with their peers, and who were not inclined to monitor their learning, were particularly likely to watch lectures online rather than to attend them in class. The results suggest that attitudes towards the option to watch lectures by streaming video are related to students' motivational orientations whereas the actual choice to attend lectures or watch them online is related to their cognitive strategies. The extent to which students attended lectures or watched them online was not related to examination performance either alone or in interaction with any motivational orientation or cognitive strategy.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".