The Use of a Reflective Learning Journal in an Introductory Statistics Course
Bibliographic record
Abstract
Reflective learning entails a thoughtful learning process through which one not only learns a particular piece of knowledge or skill, but better understands how one learned it—knowledge that can then be transferred well beyond the scope of the specific learning experience. This type of thinking empowers learners by making them more active participants in the learning process. There is also evidence to suggest that reflective learning can help students manage the negative emotions (e.g., anxiety, and disappointment) that may arise while taking a challenging course. Such emotions can be rampant in statistics courses, especially for non-statistics majors (e.g., psychology students). Because the introductory statistics course is such an important (though often dreaded) course for psychology undergraduates, I believed that the learning experience could be improved if students were encouraged to engage in more reflective thinking. To this end, I introduced a reflective learning journal into my class. In this report, I briefly review my rationale for incorporating a reflective learning journal into an introductory statistics course. I then describe how this was accomplished and share some preliminary evidence of its positive effects on the student learning experience.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".