Teaching High School Biology Students to Coordinate Text and Diagrams: Relations with Transfer, Effort, and Spatial Skill
Bibliographic record
Abstract
There is growing evidence that targeted instruction can improve diagram comprehension, yet one of the skills identified in the diagram comprehension literature—coordinating multiple representations—has rarely been directly taught to students and tested as a classroom intervention. We created a Coordinating Multiple Representation (CMR) intervention that was an addition to an intervention focused on Conventions of Diagrams (COD) and tested their joint effects on diagram comprehension for near transfer (uninstructed biology diagrams), far transfer (uninstructed geology diagrams), and content learning (biology knowledge). The comparison group received instruction using a previously validated intervention that focused exclusively on COD. Participants were 9th–10th grade biology students (N = 158 from two schools), whose classes were randomly assigned to COD alone or COD + CMR conditions and studied with a pretest–posttest experimental design. Both groups showed significant growth in biology knowledge (d = .30–.53, for COD and COD + CMR, respectively) and biology diagram comprehension (d = .28–.57). Neither group showed far transfer. Analyses of student work products during the interventions suggest that gains were not simply due to the passage of time, because student effort was correlated with gains in both treatment groups. Directions for improving future CMR interventions are discussed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".