Development of Learning Achievement and Motivation in Learning Mathematics of Grade 10 Students by Cooperative Learning
Bibliographic record
Abstract
This research developed academic achievements to attain a pass rate of 70% and assessed student motivation in studying mathematics. The target group consisted of 40 Grade 10 students during the second term of the 2023 academic year at Sarakham Pittayakhom School under the Mahasarakham Secondary Educational Service Area Office. The tools used in this research included (1) a plan for organising 12 cooperative learning activities, (2) a mathematical achievement test on the topic of logarithmic functions, (3) a measure of mathematical learning motivation and (4) a diary record. Statistical data included percentages, means and standard deviations. Content analysis was employed as qualitative data and the action research model was divided into three cycles as Cycle 1 organized cooperative learning activities using the TAI technique with 40 students. Thirteen students (32.50%) passed the 70% criterion, while 27 (67.50%) did not. The average motivation score was 3.46, indicating moderate motivation among all the students. Cycle 2 implemented the STAD technique with the same group. Twenty two students (55%) passed the 70% criterion, while 18 (45%) did not. The average motivation score increased to 4.24, showing high motivation levels. Cycle 3 utilised the Think-Pair-Share. Thirty nine students (97.50%) passed the 70% criterion, with only one (2.50%) failing. The motivation score further increased to 4.46, suggesting very high motivation levels. After completing the operating circuits, almost all the students met the required academic achievement criterion in mathematics and exhibited high levels of study motivation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".