Design Decisions Matter: Conveying the Importance of Software Engineering Best Practices through Hybrid PBL
Bibliographic record
Abstract
This Research Full Paper presents the implementation of a hybrid Project-Based Learning (PBL) model in a Software Engineering (SE) course to balance the focus on teaching fundamental knowledge and fostering of applied software development skills through a real-world project, accompanied by contextualized learning and Just-In-Time (JIT) teaching to develop students' scalable knowledge of how to intelligently design with respect to SE best practices. The data is collected from 2 semesters spanning over 2019 and 2020. Based on quantitative and qualitative analysis, this study examines the effectiveness of using the hybrid PBL approach in conveying to students the importance of SE best practices such as the SOLID principles which are deemed as timeless. Results support the claim that JIT lectures help students better evaluate their design decisions and ensure they're on the right track for following optimal design patterns and best practices, and that contextualized learning may be used to develop a notion of why design decisions matter outside of the classroom. Although incorporating these pedagogies in hybrid PBL allows for students' conviction of the significance of SE best practices in academic projects, there still exists room to better convey their significance in industry.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".