Effect of Integrating Project-Based Learning and WebQuests to Enhance Innovation Ability of Chinese Undergraduate Engineering Students: CAD Course
Bibliographic record
Abstract
This study investigated the effectiveness of an instructional model that integrates project-based learning and WebQuests to enhance Chinese undergraduate engineering students’ innovation abilities. The model was implemented in an engineering drawing and CAD course with 38 third-year students over one semester. A one-group repeated-measures design was used, with a baseline pre-test, three project-based learning tasks during the course, and a final post-test. At each stage, students’ innovative ability was assessed with a rubric comprising twelve indicators across four domains—learning process, creative thinking, knowledge integration, and practical skills. Two independent raters scored all assessments. Qualitative analyses of students’ project work, together with a post-course student feedback survey, complemented the quantitative measures. Students’ innovative abilities were assessed at five points: once prior to the first lesson, three times during the instructional process, and once following the lesson. Mean scores of the students' innovation ability across the five time points were 52.89, 60.34, 68.81, 75.48, and 80.70 respectively. The results of one-way repeated measures ANOVA indicated a statistically significant improvement in students’ innovative abilities across the measurement intervals. In Addition, all twelve competency indicators showed robust gain differences among students’ scores tended to narrow across the five measurement points. Qualitative findings illustrated how students progressively applied technical knowledge more effectively, engaged in more creative problem-solving, and improved in project planning and execution across successive tasks. Students also reported high satisfaction with the learning approach, citing benefits of real-world projects, accessible resources, and structured feedback. These findings demonstrate that the integrated project-based learning and WebQuests substantially strengthens key innovation skills in engineering education. The paper discusses the implications for instructional design and offers recommendations for educators to foster innovation in technical domains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".