Development and Validation of Instructional Model Using Project-Based and WebQuest Learning Approaches for Enhancing Innovation Ability of Electrical Engineering Students
Bibliographic record
Abstract
Although innovation is widely recognised as a cornerstone of contemporary engineering, empirical studies consistently show that undergraduate electrical‑engineering students lag behind industry expectations in rapidly translating interdisciplinary concepts into functional prototypes. These findings underscore the need to comprehensively reform teaching practices, curriculum structures, and assessment systems so as to cultivate prototype‑oriented innovation competence. This article aimed to develop and validate an instructional model using project-based and WebQuest learning approaches for enhancing innovation ability of Chinese electrical engineering students. The scope of the presentation focuses on the development process of the instructional model, with particular emphasis on establishing internal validity through expert review. This article aimed to develop and validate an instructional model that combines Project‑Based Learning and WebQuest approaches to enhance prototype‑oriented innovation ability among Chinese undergraduate electrical engineering students. Following a two‑phase research and development framework, Phase 1 conducted expert review to ensure content validity, and Phase 2 involved prototype refinement and pilot implementation. Instrument quality was high, and inter‑rater reliability was good (ICC = 0.76-0.90). Experts rated all seven components—principles, objectives, learning steps, instructor and learner roles, media resources, and evaluation—as highly appropriate, confirming the model’s conceptual soundness and practical applicability. In summary, the majority of experts found the various components of the instructional model to be appropriate at a high to highest level. The ICC values for all instructional model components indicated good reliability, demonstrating the dependability of the developed instructional model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".