Multimedia Computer-Based Lessons on Programming with Scratch in Technology Integrated with the TGT Cooperative Learning Technique to Enhance Learning Achievement and Teamwork Ability of Thai Grade 7 Students
Bibliographic record
Abstract
The purposes of the current study were to examine the effectiveness of the multimedia computer-based lessons on programming with Scratch in Technology integrated with the TGT cooperative learning technique based on the 80/80 efficiency criterion, to compare the learning achievement of Grade 7 students between those taught using multimedia computer-based lessons integrated with TGT and those taught using traditional instructional methods, to investigate the teamwork skills of Grade 7 students when learning through multimedia computer-based lessons integrated with the TGT cooperative learning technique, and to explore students' satisfaction with the multimedia computer-based lessons integrated with the TGT cooperative learning technique. The study employed experimental research design, comparing the learning achievement and teamwork skills of students who learned with Scratch and TGT (n=40) to those who received traditional instruction (n=40). The research instruments included a Scratch-TGT integrated learning plan, a learning achievement test, a teamwork skills assessment, and a student satisfaction questionnaire. The findings indicated that the Scratch and TGT integrated learning approach was highly effective. The process and product effectiveness scores met the 80/80 efficiency criterion, and the experimental group outperformed the control group in learning achievement. Additionally, students' teamwork skills were rated at a very high level. Student satisfaction was also at a very high level, which indicates positive engagement with the integrated approach.
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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 | 0.001 |
| 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".