Maximizing Science Learning through Practical Work in Secondary Schools in Tanzania: Student-Teachers’ Adaptation to Language Supportive Pedagogy
Bibliographic record
Abstract
There is a lack of clarity about the role of practical work in promoting learning and thinking, due to mediocrity in the way it is being handled. This study used mixed methods design to develop through microteaching, LSP integrated practical work to explore its characteristics in order to examine how the student-teachers adapted to the pedagogy in relation to its efficacy in developing science, English language, and pedagogy. The research was conducted at the University of Dodoma. The study involved 63 student-teachers and five lecturers with different specializations. The study was carried out in three cycles at the College of Education. It involved the second-year students who were studying Bachelor of Education in Science, who undertook a Physics Teaching Methods course. Review of lesson plans, achievement tests, classroom observations, and interviews with student-teachers, together with focus group discussions among the lecturers, were used to collect data. The qualitative data were analysed using the thematic analysis approach, while the quantitative data were analysed through repeated measures t-test. The student-teachers were able to plan and implement LSP practical lessons in which case students reported to have enjoyed their participation, sharing of experiences, and the bilingual classrooms. The findings from the repeated measures t-test show that there was a significant rise in the mean scores from pre-test (67.9) to the post-test (80.2), with very much reduced variability (SD) of scores among students from 24.9 (pre-test) to 6.6 (post-test). From interviews with 8 case students after each lesson, it was concluded that the students improved in science content, English language competence, and pedagogy.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".