Are language‐based activities in science effective for all students, including low achievers?
Bibliographic record
Abstract
Abstract The study investigated achievement status as a factor determining the use of language‐based activities for learning science. A total of 154 eighth‐grade students were randomly assigned to four groups, all stratified for gender and achievement level. The treatments involved various combinations of talk and writing, and descriptive and explanatory tasks. The dependent measures included scores on multiple choice tests obtained at three times during the study. Records of student talk and writing were also analyzed to identify patterns of differences between groups of achievers. The findings suggested that low achievers complete more problems, and develop better understanding and comprehension of ecology concepts when they have engaged in peer discussions of explanatory tasks. In comparison, high achievers benefit more from writing than talking, and writing explanations enhances comprehension more than restricted writing activities. © 2004 Wiley Periodicals, Inc. Sci Ed 88: 420–442, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/.sce10114
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".