In Their Own Best Interest: Data-Based Decisions in the Classroom
Bibliographic record
Abstract
Regular quizzing that requires retrieval (e.g., fill-in-the-blank and open-ended questions) has been found to enhance the retention of information relative to quizzing that requires recognition (i.e., multiple-choice questions). This phenomenon, called the testing effect, has been demonstrated in a variety of laboratory and classroom studies. In past semesters, students in an upper-level psychology course who took fill-in-the blank quizzes performed significantly better on multiple-choice exams than students who took multiple-choice quizzes covering the same material. More recently, students have been provided with information about the testing effect, including data from earlier semesters of the same course, and allowed to individually choose their quiz format. While many students initially chose fill-in-the-blank quizzes, the majority switched to multiple-choice quizzes when allowed to do so one quarter of the way through the semester. Students in three sections of the course have exhibited this same pattern, despite evidence from their own sections that, on average, students taking fill-in quizzes earned higher grades on the first exam. The implications of this behavior, as well as its potential as a "teachable moment," will be considered.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".