Effectiveness of Life Goal Framing to Motivate Medical Students During Online Learning: A Randomized Controlled Trial
Bibliographic record
Abstract
Introduction: Educators need design strategies to support medical students' motivation in online environments. Prompting students to frame a learning activity as preparing them to attain their life goals (e.g., helping others) via their clinical practice, a strategy called 'life goal framing', may enhance their autonomous motivation, learning strategy use, and knowledge retention. However, for students with low perceived competence for learning (PCL), life goal framing may have an adverse effect. A randomized controlled trial was conducted to test the effectiveness of life goal framing and the moderating effect of students' PCL. Methods: = 128) were randomized to receive a version of an online module with an embedded prompt for life goal framing, or one without. Students' motivation, learning strategy use, and knowledge retention were assessed. Differences between conditions on each outcome were estimated using Bayesian regression. Results: Students' PCL was a moderator for autonomous motivation but no other outcomes. The prompt did not have a statistically significant effect on any outcome, even for learners with high PCL, except for a small effect on link-clicking behaviour. Discussion: their present confidence. We cannot recommend life goal framing as an effective design strategy at this point, but we point to future work to increase the benefit of life goal framing for learners with high confidence.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".