Crossword Puzzles: Active Learning in Undergraduate Pathology and Medical Education
Bibliographic record
Abstract
Abstract Context.—Second-year medical students are introduced to many new terms and concepts in a short time frame in the hematology system and the neoplasia section of the undergraduate pathology course. It is a challenge to provide adequate practice and necessary repetition to reinforce key concepts. Objective.—To determine student perceptions of the usefulness of crosswords as a quick and effective way to reinforce essential concepts and vocabulary. Design.—Crosswords with ensured content validity built on a free Internet resource were completed by the students in collaborative and cooperative groups of 6 to 7 with a reward for the first group to successfully complete the puzzle. Student perceptions of the value of crosswords for their learning were examined in 2003 (39 students) with a survey of yes or no responses and in 2004 (41 students) with a survey using questions with a 5-point Likert scale. Results.—Many students (37 of 39 in 2003 and 24 of 41 in 2004) indicated that crosswords were useful and contributed to their learning. Specifically, crosswords were found to be useful for identifying key concepts and vocabulary and for their collaborative and competitive aspects. Written and informal comments indicated student enthusiasm for and a desire to participate in more of these exercises. Students have transferred this review strategy to other classes and the peer teachers have expressed an interest in it as an adjunct teaching tool. Conclusions.—The judicious use of crosswords was useful for near transfer content and provided an opportunity to discuss and recall essential concepts, think critically, and collaborate in small groups.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".