Peer Learning Amongst Students of Higher Technical Teachers’ Training College (HTTTC) of the University of Buea in Kumba, Cameroon
Bibliographic record
Abstract
This paper examines the use of peer learning in students’ success at the Higher Technical Teachers’ Training College in Kumba, Cameroon. The study uses a quantitative descriptive data to determine the effectiveness of peer learning amongst students through the impact of study groups and peer tutoring on students’ achievement. The study employed the descriptive survey design. Participants of the study were made up of 234 students drawn from both the first and second cycles of the 14 departments of the Technical Teachers’ Training College (HTTTC), Kumba. The study sought to find out the effects of study groups and peer tutoring on students’ achievement at HTTTC, Kumba. A structured questionnaire was used as the instrument for data collection. The data collected was analyzed descriptively using frequencies and percentages computed with the help of the SPSS V.20.0. Evidence from students’ responses indicated that the importance of studying in groups with classmates and peer tutoring by other students in the success of their end-of-semester and final examinations cannot be overemphasized. By providing these learning environments in schools, students are able to form a cohesive group where they can express their ideas and help each other succeed. Based on the study findings, it is recommended that students who are more knowledgeable and have a good mastery of the subject or the concepts taught be paired by the teacher with other students for group work, class discussions and more purposeful structured learning. Teachers should also take advantage of the peer tutoring technique to encourage student-teachers to work in small mixed ability groups that will allow everyone whether fast or slow to share their ideas and build knowledge as well as interpersonal skills in the training process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".