DIFFERENTIATION IN SAUDI PRE-SERVICE SCIENCE TEACHER PROGRAM
Bibliographic record
Abstract
Saudi students’ science academic performance has declined as evidenced by (TIMSS). Saudi science teachers are characterized as using the lecture format without considering individual student differences and failing to provide differentiated Method (DM). This paper reports on an effort to help female Saudi pre-service science teachers (PSST) develop DI knowledge and skills, striving to discern how they understood and practiced differentiation during their field experience after completing a specially-designed DM-focused university course. A mixed method research design followed a sequential, connected approach wherein quantitative data were collected through classroom observations (N=47) using a Likert scale observation instrument followed by qualitative interviews (n=11). The pre and post averages of differentiated teaching skills in the DM planning stage were statistically significant (p=.0001). The PSSTs moved from very small to moderate mastery on virtually all 10 planning items, from 1.75 to 2.99 on a five-point Likert scale. The DM implementation stage (20 items) also reflected a statistically significant difference with scores moving from 1.68 to 3.01 (moderate mastery). Interview qualitative data confirmed and elucidated the quantitative results. The course was deemed effective in developing PSSTs’ differentiated teaching skills (statistically significant, p=.01). Teaching PSSTs about DM should improve Saudi students’ science academic achievement. Keywords: differentiation, pre-service science teachers, teacher education, Saudi Arabia, TIMSS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".