The Impact of Cooperative Learning Activities using the Team-Assisted Individualization (TAI) Technique Combined with Activity-Based Learning on Problem-Solving Abilities and Mathematical Connections Abilities in the Subject of Congruence
Bibliographic record
Abstract
This research is a quasi-experimental study with the following objectives: 1) To examine the effectiveness of cooperative learning activities using the TAI technique combined with activity-based learning on the topic of congruence for students at Roi Et Rajabhat University, aiming to achieve the 75/75 efficiency criterion. 2) To compare mathematical problem-solving abilities before and after implementing the learning activities. 3) To compare mathematical connection abilities before and after the learning activities. The sample group consisted of 24 students enrolled in the Number Theory course during the first semester of the academic year 2024, selected through cluster sampling. The research instruments included: 1) Four lesson plans for cooperative learning activities using the TAI technique combined with activity-based learning, 2) A subjective test measuring mathematical problem-solving abilities consisting of five items, and 3) A subjective test measuring mathematical connection abilities consisting of five items. The statistical methods used were mean, percentage, standard deviation, and the Wilcoxon Signed Rank Test. The research findings revealed that: 1) The cooperative learning activities using the TAI technique combined with activity-based learning achieved an efficiency of 80.83/78.85, which was higher than the specified criterion. 2) The students’ mathematical problem-solving abilities on the topic of congruence were significantly higher after the learning activities at the .05 level of significance. 3) The students’ mathematical connection abilities on the topic of congruence were significantly higher after the learning activities at the .05 level of significance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".