Explicit/implicit training of cooperative learning with junior elementary students /
Bibliographic record
Abstract
This experimental study examined the effects of cooperative learning and expliciUimpliGit instruction on student achievement and attitudes toward working in cooperative groups. Specifically, fourth- and fifth-grade students (n=48) were randomly assigned to two conditions: cooperative learning with explicit instruction and cooperative learning with implicit instruction. All participants were given initial training either explicitly or implicitly in cooperative learning procedures via 10 one-hour sessions. Following the instruction period, all students participated in completing a group project related to a famous artists unit. It was hypothesized that the explicit instruction training would enhance students' scores on the famous artists test and the group projects, as well as improve students' attitudes toward cooperative learning. Although the explicit training group did not achieve significantly higher scores on the famous artists test, significant differences were found in group project results between the explicit and implicit groups. The explicit group also exhibited more favourable and positive attitudes toward cooperative learning. The findings of this study demonstrate that combining cooperative learning with explicit instruction is an effective classroom strategy and a useful practice for presenting and learning new information, as well as working in groups with success.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".