The effect of talk and writing on learning science: An exploratory study
Bibliographic record
Abstract
This study investigated the role of talk and writing on learning science. The purpose was to explore the effect of talk, writing, and talk and writing on the learning and retention of simple and integrated knowledge, and to describe the mechanisms by which talk and writing mediate these processes. Forty-three students were randomly assigned to four groups, all stratified for gender and ability. At intervals during an instructional unit, three treatment groups received problem tasks that involved constructing scientific explanations for real-world applications of ecological concepts. A control group received simpler descriptive tasks based on similar content. Students in the talk-only treatment group (T) discussed the problem tasks in small peer groups. Students in the writing-only treatment group (W) individually wrote responses for each of the tasks, but without first talking to other students. Students in the combined talk and writing treatment group (TW) discussed the problems in groups prior to individually writing their explanations. Dependent variables included simple, integrated, and total knowledge scores based on multiple-choice tests, essay questions, and concept maps obtained at three timepoints during the study: a pretest; an immediate posttest; and a delayed posttest. Records of student talk and writing were also analyzed to describe the mechanisms involved. The findings suggest that talk is important for sharing, clarifying, and distributing knowledge among peers, while asking questions, hypothesizing, explaining, and formulating ideas together are all important mechanisms during peer discussions. Analytical writing is an important tool for transforming rudimentary ideas into knowledge that is more coherent and structured. Furthermore, talk combined with writing appears to enhance the retention of science learning over time. Moreover, gender and ability may be important mediating variables that determine the effectiveness of talk and writing for enhancing learning. © 2000 John Wiley & Sons, Inc. Sci Ed 84:566–593, 2000.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".