Developing a TPACK-based Course to Promote the Pre-service Preschool Teachers’ Instructional Design Competence
Bibliographic record
Abstract
This study integrates the TPACK framework into a Preschool Language Education course to examine its effects on pre-service preschool teachers' instructional design competence (IDC). The research aims to explore effective methods for IDC development and analyze its growth characteristics. Using an R&D methodology, this study employed an experimental design with an experimental group and a control group. Data were collected from 41 pre-service preschool teachers at Putian University, Fujian Province, China, through cluster sampling. The experimental group (n=21) participated in a 13-week TPACK-based course, while the control group (n=20) received traditional instruction. The IDC Scale and Lesson Plan Scoring Rubric were used for data collection. Data analysis included independent and paired t-tests, as well as assessments of lesson plan scores and grade distributions. Key findings indicate that (1) the TPACK-based course encompasses course objectives, content, learning organization, and assessment, with all participants achieving satisfactory or higher lesson plan scores and a 95% satisfaction rate; (2) the course significantly enhances pre-service preschool teachers' IDC; and (3) at the early stage of professional development, IDC growth exhibits characteristics of simplification, linearity, and dogmatism.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".