Fostering Teachers’ Competencies for Integrated Language Arts, Science, and Technology Instruction: An Exploratory Study
Bibliographic record
Abstract
This study examined the impact of a teacher professional development (TPD) program on primary school teachers’ skills and self-efficacy in integrated language arts, science, and technology (ILS&T) instruction. The program’s design is based on the four-component instructional-design (4C/ID) model, validated in many areas for developing complex professional skills. Nine teachers from one primary school participated in five TPD group meetings during one school year. The TPD program focused on developing prerequisite skills for ILS&T instruction. Data were collected through lesson observations and interviews to assess teachers’ skills in ILS&T instruction, as well as through a self-efficacy questionnaire. Findings demonstrated varying proficiency levels in the required skills for ILS&T instruction among teachers after the program. This suggests that the program may have provided insufficient support for the development of some, potentially more complex, skills. Although teachers showed an overall increase in self-efficacy, the pre-post-test difference was not statistically significant. There were statistically significant increases in student engagement and instructional strategies subscales. This study discusses the exploratory impact of the TPD program for equipping teachers for ILS&T instruction. A discussion of the findings provides indications for optimizing the program for future large-scale implementation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".