Effect of Student’s Team Achievement Division (STAD) on Academic Achievement of Students
Bibliographic record
Abstract
Due to the increasing diverse nature of the world’s education system, it is important that learning strategies are beneficial in educating a wide variety of students. For the retention and comprehension of the subject matter taught in the classrooms, teachers must engage students and provide them with the proper social skills needed to succeed beyond the classroom environment. The focus of the present study investigated the effect of a form of cooperative learning instruction that is students’ team achievement division (STAD) with that of traditional lectures method. The population of the study was all the students studying chemistry at higher secondary level in Khyber Pukhtunkhwa (Pakistan). 30 students of chemistry grade 12 in government higher secondary school Jamrud were selected as a convenient sample of the study. The students were divided into two groups one was called control group and the other was experimental group based on stratified random sampling techniques. The true experimental design of the posttest only control group design was applied in this study. The control group was taught with the traditional lecture method while the experimental group with the cooperative learning instruction STAD. Students academic achievements were find out by teacher made test composed of multiple choice questions, short questions and long questions. The credit of the test was of 50 marks, the posttest consist of multiple choice questions of 16 marks, short questions of 24 marks and one long question of two subsections having 10 marks. Student ttest of non-dependent sample was used to analyze the data. The result showed that the students’ achievements of both the groups were not significant. The implications were discussed.
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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 | 0.005 |
| 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 | 0.000 | 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".