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Record W4416379572 · doi:10.5539/hes.v15n4p536

Effect of Integrating Project-Based Learning and WebQuests to Enhance Innovation Ability of Chinese Undergraduate Engineering Students: CAD Course

2025· article· W4416379572 on OpenAlexvenueno aff
Julamas Jansrisukot, Pattawan Narjaikaew

Bibliographic record

VenueHigher Education Studies · 2025
Typearticle
Language
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRubricQualitative propertyRepeated measures designCreativityEngineering educationCADQualitative researchQualitative analysis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.456
Teacher spread0.439 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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