Secondary School Teachers’ and Students’ Perspectives on Cooperative Group Work Assessment Challenges in Ethiopia
Bibliographic record
Abstract
Cooperative Learning (CL) has been encouraged in Ethiopia’s secondary schools as an important strategy to facilitate effective student learning. However, the effectiveness of CL hinges, among other factors, on appropriate assessment of students’ group work. Challenges faced by teachers and students in implementing assessment of group work have remained an obstacle to the effective use of CL. The aim of this study was therefore to examine what Ethiopian secondary school teachers and students, respectively, consider to be problems and obstacles in the way of efficiently implementing student the cooperative group work assessment. Accordingly, 213 teachers and 212 students were randomly selected for a questionnaire survey. In addition, two teachers and five students were also interviewed and a focus group discussion (FGD) was carried out in each of the five schools selected for data gathering. The data acquired through the questionnaire was analyzed through one-sample t-test while the data obtained through interviews and FGD were analyzed through qualitative verbal descriptions. The findings indicate the main challenges from the point of view of the teachers to be their inadequate training on the assessment of group work process and individual contributions; uncertainty on what should be assessed, and heavy workloads. From the students’ perspective, the main challenges were inadequate teacher support and follow up and equal reward for unequal contribution by members to group work.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".