Which Factors, Personal or External, Most Influence Studentsʼ Generation of Learning Goals?
Bibliographic record
Abstract
BACKGROUND: While concern has been expressed about the validity of self-assessments, external feedback is likely filtered through self-assessment. This paper explores the relationship between self-assessments and feedback uptake. METHOD: During an objective structured clinical examination, students were asked to evaluate their performance and rate the quality of feedback provided by observers. Afterward, they were asked to list learning goals they generated, to indicate what activities they would undertake to fulfill those goals, and to identify which station(s) led them to generate each response. Regression analyses were used to determine which variables predicted the generation of goals/activities. RESULTS: Students' perceptions of their own performance were more likely to result in the generation of goals/strategies than was observer feedback or student perceptions of observer feedback quality. Later stations were more likely to result in goal/strategy generation than earlier stations. CONCLUSIONS: While self-assessments may not validly indicate ability, it is still critical to determine how students perceive their ability because their opinions drive their learning goals.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".