The educational value of using bi-lingual instructional practice in students’ Writing Skills in Entomology Course at KUE
Bibliographic record
Abstract
This paper reports on small-scale action research conducted with students in the final year of their degree at Kotebe University of Education. We had identified a problem whereby students majoring in Biology tended to express their content knowledge in the form of lists rather than in coherent sentences and complete paragraphs. We consequently designed an intervention that explicitly guided the students to compose short pieces of academic writing within four scientific genres: description, comparison, components and classification. The intervention was evaluated using pre- and post-tests and a student focus group discussion involving around one third of the class. The results showed that after six weeks, all the students were able to write coherent, well-organized paragraphs using appropriate scientific language. Students attributed their improvement to the formative feedback they received throughout the six-week intervention. This small-scale study suggests that cross-curricular language support has considerable potential for developing pre-service teachers’ writing skills. However, realizing this potential requires collaboration between language and other subject teachers. We relate the findings to previous research in Tanzania, which focused on developing pre-service teachers’ pedagogic skills for supporting learners through language transition. The policy implications of using home language (in this case Amharic) as the medium of instruction in higher education institutions where English is the language of instruction should be considered for science teachers. We conclude by arguing for a joined-up approach to teacher education for multilingual education systems and suggest some priorities for further research.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".