USING HAUSA LANGUAGE IN THE ASSESSMENT OF STUDENTS: A CASE STUDY OF HIGHER INSTITUTION IN BAUCHI, NIGERIA<u> </u><u></u>
Bibliographic record
Abstract
Nowadays, use of native languages such as Hausa in teaching and learning is becoming an order of the day. This study utilized Hausa language to assess students of higher institution in Bauchi using a quasi-experimental approach involving 21 (test) students and 17 (control) participants. The participants were assed using Montreal Cognitive Assessment in Hausa language. The study participants included; 55.3% males and 43.7% females; 65.8% pre-100 level students and 34.2% 100 level students; 68.4% Hausa, 7.9% Yoruba, and 23.7% Fulani; 92.1% Muslims and 7.9% Christians. This research investigates the impact of language on academic performance, comparing students assessed in Hausa language and English language. The results show that students assessed in Hausa language scored higher (mean score: 26.57+5.670) compared to those assessed in English language (mean score: 24.157+2.577). This finding suggests that using mother tongue in teaching and assessment may improve students' understanding and academic performance. The study's findings are consistent with previous research, and it is recommended that incorporating native languages in educational systems could improve academic achievement and performance. The study suggests that incorporating native languages in educational systems could improve academic achievement and performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.005 |
| 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 teacher head, 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".