Introductory Engineering Decision-Making: Guiding First-Year Students to Relativism in Software Design
Bibliographic record
Abstract
Contribution: A semester-long, open-ended design project was implemented to promote intellectual development of first-year students while reinforcing event-driven/procedural programming principles. This paper describes this approach, and an evaluation strategy using Perry's model for intellectual development. The results show that students can reach the relativism stage of Perry's model in their first year of studies. Background: Students must move beyond a dualistic worldview to be engineers. Felder and Brent provided a list of recommendations to promote this type of intellectual development in students; active learning strategies strongly align with these recommendations. While active learning is common in programming courses (sometimes taking the form of project-based learning), they are typically tightly controlled by the instructor, and limited in scope (both in time and complexity), potentially reducing their impact on students. Intended Outcomes: This course and term-long project provided students a supportive environment in which to develop their decision-making skills, and promoted their intellectual development through Perry's stages related to software design. Application Design: Aligned with Felder and Brent's recommendations and Kuh's High Impact Practices, a course was built to: 1) teach students procedural programming, with 2) a focus on software design and open-ended problem solving, while maintaining 3) a supportive environment for skill acquisition. Findings: A mixed methods study showed the majority of students align themselves with the relativism stage of intellectual development at the end of the course. This study also illuminated the decision-making processes of teams of students in a first-year software design course.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".