Using Worked-Out Examples of Written Explanation for Writing-to-Learn in Evolutionary Biology
Bibliographic record
Abstract
Content learning can be enhanced through writing-to-learn. Research into cognitive load theory suggests that the use of backwards faded, worked-out examples increases schema acquisition and concept transfer. However, these effects have not yet been demonstrated for writing-to-learn, particularly for the conceptual understanding of evolution. The effects of two writing conditions were investigated in a pre-test post-test quasi-experimental design. Groups in two conditions wrote explanations of evolution using six Darwinian principles: students in the completion condition completed backwards faded, worked-out examples of explanations; students in the problem solving condition wrote full explanations, thought to require means-end problem solving. The dependent variables included the following: a writing task explaining evolution in a novel scenario; an evolution post-test; the number of total principles and target principles, reported difficulty, and perceived effort for each writing activity. The problem solving group demonstrated significantly higher concept transfer compared to the completion group on the evolution post-test, as well as marginally higher concept transfer on the sixth writing task. In addition, the problem solving group included a significantly higher number of total principles over the first four writing tasks. The completion group reported significantly less perceived effort over the first four writing tasks, compared with the problem solving group. Both writing conditions resulted in positive gains between the pre- and post-tests, suggesting that overall, writing-to-learn is effective for teaching evolutionary concepts. It is proposed that writing full explanations using means-end problem solving provides increased task complexity for learners, leading to concept transfer.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".