Using diagrams versus text for spaced restudy: Effects on learning in 10th grade biology classes
Bibliographic record
Abstract
BACKGROUND AND AIM: Spaced restudy has been typically tested with written learning materials, but restudy with visual representations in actual classrooms is under-researched. We compared the effects of two spaced restudy interventions: A Diagram-Based Restudy (DBR) warm-up condition and a business-as-usual Text-Based Restudy (TBR) warm-up condition. SAMPLE: One hundred and twenty-eight consented high school students in 15 classes. METHODS: Students completed daily warm-ups over a 4-week period. Students were randomly assigned to conditions within classrooms. Warm-ups were independently completed at the start of class meetings and consisted of questions about content covered 1-10 days prior to each warm-up. Students received feedback on their answers each week. A series of ANOVAs and ANCOVAs was conducted. RESULTS AND CONCLUSIONS: Results showed equal and significant growth from pre- to post-test for both conditions (d = .31-.67) on three outcomes: Biology knowledge, biology diagram comprehension (near transfer), and geology diagram comprehension (far transfer). ANCOVA results suggested that the magnitude of this increase was linked to the number of questions attempted during the intervention. For the DBR condition only, there were interactions with content knowledge on diagram comprehension gain scores and interactions with spatial scores on biology knowledge gain scores. Students with lower biology knowledge and lower Paper Folding Test scores were disadvantaged in the DBR condition, whereas the TBR condition was equitable across all levels of knowledge and spatial ability.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".