Student-Generated Exam Crib Sheets: What Do They Write? An Examination of Crib Sheet Features
Bibliographic record
Abstract
Many first-year General Chemistry course instructors provide students with exam aids, often including items such as the periodic table of elements, physical constants, tables of physical data, or formulas. Recently, we experimented with allowing first-year General Chemistry students to prepare and use their own single-page exam crib sheets, with almost no restrictions on what they could prepare and write. We studied the student-generated crib sheets to learn more about what students chose to write on their sheets and if there were observable differences in the features on crib sheets for high-performing and low-performing students. Student sheets typically contained short memory aids, were often text-heavy, and frequently included problem-solving aids (more often specific examples rather than generic problem-solving algorithms). These exam aids were generally well-organized and appeared to be useful and readable. Analyzing the crib sheets for the top and bottom quartiles on the final exam showed that the top quartile had three organizational features (Boxes, Headings, and Color) and two content-related features (Specific Examples and Definitions) appear significantly more often.
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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 | 0.008 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".