The impact of course delivery approach on student’s academic achievement at Haramaya University / Anwar Ahmed Hussen
Bibliographic record
Abstract
The teaching approach plays a significant role in determining learners’ academic achievements. Hence, the intention of this study was to examine the impact of the Course Delivery Approach (CDA) on students’ academic achievement. To realize this purpose, combinations of both qualitative and quantitative approaches were used. Teachers and students were primary sources of data while senate approval sheets and students grade reports were considered as secondary sources. 51 students and 15 teachers were sampled by using purposive and available sampling techniques respectively. Interview, document analysis, and FGD were used to collect the data. The analysis of quantitative data was carried out by using mean and paired sample t-test while qualitative data was analyzed through the thematic description and word narration. The findings of the study indicated that students’ academic achievement is relatively higher in the block course delivery approach (mean=3.2) than the parallel approach (mean=1.81). The p-value of paired sample t-test, t(49)=32.6, P<0.05 also indicated that the academic achievement of students on parallel and block course delivery approaches was statistically significant. Moreover, the mean score of students in quarter one courses (1.583) was greater than quarter two courses (1.576) with t-test value t(49)=0.39, p>0.05 indicating that the mean difference was not statistically significant.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".