Effects of text structure, reading goals and epistemic beliefs on conceptual change
Bibliographic record
Abstract
We investigated the online and offline effects of learner and instructional characteristics on conceptual change of a robust misconception in science. Fifty‐nine undergraduate university students with misconceptions about evolution were identified as espousing evaluativist or non‐evaluativist epistemic beliefs in science. Participants were randomly assigned to receive a traditional or refutational text that discussed a misconception in evolution and a general comprehension or elaborative interrogation reading goal. Participants' cognitive and metacognitive processes while reading were measured using a think‐aloud protocol. Postreading, participants' correct and incorrect conceptual knowledge were separately assessed with a transference essay. Results showed that text structure and reading goals affected cognitive conflict, coherence‐building and elaborative processing while reading and promoted correct conceptual knowledge included in essays but failed to affect the inclusion of misconceptions. Further, participants with evaluativist epistemic beliefs engaged in fewer comprehension monitoring processes and were more likely to adapt their coherence‐building processes according to reading goals than their non‐evaluativist counterparts, but epistemic belief groups did not differ in the content of the posttest essay. Theoretical and educational implications of these findings 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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".