Constructing and Validating a Flipped and Collaborative Learning Model for Fostering Instructional Design Skills of Chinese Pre-Service Physics Teachers
Bibliographic record
Abstract
This study aimed to develop and validate an instructional model integrating a flipped classroom and collaborative learning approaches to enhance instructional design skills of Chinese pre-service physics teachers. Before developing the model, structured questionnaires and semi-structured interviews were used to collect data from teachers’ and pre-service physics teachers’ perspectives on classroom management to enhancing instructional design skill. Results indicated strong awareness of instructional design and appreciation for digital tools and feedback, but revealed weaknesses in pre-class preparation, collaboration, and classroom engagement. Developing instructional model components consisted of six key aspects include generating model principles, defining learning objectives, designing learning steps, examining the roles of teachers and students, and developing assessment methods to evaluate learning. The instructional model was validate by five experts using standardized rating forms, yielding high average scores (4.00–5.00) and strong reliability (ICC = 0.79–0.87), confirming its theoretical soundness and contextual relevance. Lesson plans were designed to structure and guide instruction, aligning with the instructional model’s principles and learning steps. Seven lesson plans received the average appropriateness score 4.20–5.00 with good consistency (ICC = 0.76), demonstrating strong alignment and feasibility. After complete experts’ validation, the lesson plans were piloted with 40 students. The pilot phase showed high actively engagement that suggests the model are feasible and acceptable to participants. This is a positive indicator for the implementation phase. The study offers a practical and scalable framework for instructional design training in teacher education.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 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".