EXAMINING THE IMPACT OF COOPERATIVE LEARNING STRATEGIES ON STUDENT PERFORMANCE IN GEOGRAPHY, NINE YEARS BASIC EDUCATION PROGRAM, GS MUSHERI, MUSHERI SECTOR, RWANDA
Bibliographic record
Abstract
The study concerns the impact of cooperative learning on students’ performance in Geography in nine years of primary education in Musheri Sector. The study used a descriptive survey design; 46 subjects were used as a sample from 230 as a target population. Stratified sampling and purpose sampling were used to get the sample size, questionnaires and interview guides were used to collect data, and Microsoft Excel was used to analyze the data. The study found that teachers use different cooperative learning methods, including jigsaw, think pair share, three-minute review, teamwork and group performance. The study found that 17.4% of the respondents revealed that teachers use round tables as the simple cooperative learning structure used in the G.S Musheri, enabling them to cover much content, build team spirit, and incorporate writing. Of 46 respondents, 36.9% indicated that the teacher's negative attitude led to the lack of effective use of corporative learning in the classroom. This was brought up by the issue of lack of teachers' motivation. The study recommends having assessments such as daily competitions, quizzes, tests, and other types of assessments to increase student performance. The study recommended that the government train teachers on cooperative learning skills as one way of helping students acquire knowledge, skills and attitudes and also to have continuous professional development for teachers on teaching and learning methods. Article visualizations:
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".