The Positive Effects of using Reflective Prompts in a Database Course
Bibliographic record
Abstract
Motivation: Prior literature has identified student reflections as a way to encourage students to express their thoughts in a structured and focused manner. Objectives: Our goal is to examine the impact of reflections in a third year database systems course, which employs an active learning approach and classroom environment. Specifically, we are interested in seeing whether reflecting on key concepts covered in a preparatory component before lecture had an impact on student’s immediate and long-term performance. Methods: Students were divided into two groups, and asked to reflect on different topics after watching lecture videos before completing their homework exercises for 3 weeks. Results: We observed that students who reflected on lecture concepts performed better on homework exercises that covered those same concepts than students who did not reflect on those same concepts. Moreover, students who reflected performed better in subsequent assessments than students who did not reflect at all. Implications: Reflection as a part of the preparatory component in flipped classrooms is a useful component in conceptual understanding. Further research and investigation should be pursued into ways of prompting reflection, and assessing this component in database courses.
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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 | 0.009 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".