Bibliographic record
Abstract
This study, utilizing two types of computer-based instruction (CBI) programs developed by the researcher, was to examine the effects of explicit strategies instruction on 105 sixth-grade students' mathematics problem-solving skills. Students drawn from five public elementary schools were divided into three groups (experimental, CBI control, and traditional control) and assessed on their mathematics problem-solving achievement levels (high and low) using a mathematics problem-solving sub-test from the Canadian Test of Basic Skills (CTBS-MPS) (multi-level edition. r =97). The students were given the Mathematics Problem-Solving Test which was developed for this research as well as the CTBS-MPS to measure their mathematics problem-solving performance. In order to measure their estimation of performance on the MPST, Self-Prediction of Performance, Self-Evaluation of Performance, Accuracy of Self-Prediction of Performance, and Accuracy of Self-Evaluation of Performance were measured. The findings indicated that students in the experimental group who were exposed to explicit strategies instruction via computer showed a significantly greater improvement on the CTBS than those in the CEI control or traditional control groups. However, there were no significant differences observed on the MPST, SPP, SEP, ASPP, and ASEP across the three groups.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".