A learning philosophy assignment positively impacts student learning outcomes
Bibliographic record
Abstract
Engaging students in metacognition can improve their learning outcomes. Students’ consideration of their learning philosophy is one way for students to be metacognitive about their learning. This study analyzed the effect of a learning philosophy assignment on students’ intellectual development and mastery of first-year biology and second-year biochemistry course content. All students were invited to complete the Learning Environment Preferences (LEP) survey at the beginning and end of the term to determine if students’ cognitive complexity was impacted by the assignment. The ability to master course content was assessed by comparing students’ midterm and final exam marks. We found that the learning philosophy assignment rescued Bachelor of Science students in the first-year biology course from a decrease in cognitive complexity. Additionally, the guided metacognition rescued second-year biochemistry students from performing poorer on the final relative to the midterm exam and promoted an increase in their cognitive complexity. These results suggest that a learning philosophy assignment may be an effective way of engaging students’ in metacognition of their learning to promote their intellectual development and mastery of course material.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".