Use of a Cornerstone Project to Teach Ill-Structured Software Design in First Year
Bibliographic record
Abstract
Contribution: A first-year programming course was redesigned with a large, open-ended robotics project. The course design aligns with best practices for promoting development of students' self-efficacy in solving ill-structured software design problems. Background: From Jonassen's theory, problem-solving outcomes are dependent on the problem structure, complexity, and representation; and the characteristics of the solver. These characteristics are diverse, including knowledge, familiarity, and psychometric qualities of the solver (e.g., self-efficacy and motivation). Thus, better problem-solving outcomes are dependent on the development of these traits, and on the problem characteristics. Intended Outcomes: Pre-2010, course learning activities and assessments overly focused on syntax. The course was redesigned with a focus on ill-structured problem solving and design in high-fidelity problem domains. Application Design: Complex and ill-structured lecture examples, assignments, and exams were redesigned to reinforce the importance of software design and problem solving. An open-ended cornerstone project using robotics was added as a structured means of providing students practice with solving ill-structured and open-ended problems. The assignment and exam questions, with the course cornerstone project, achieve instructional alignment in the course. Findings: The results show that students' self-efficacy improved from start to end of term. The course design achieves several objectives: 1) students learned the requisite programming skills; 2) students developed their self-efficacy in programming and design; and 3) students demonstrated strong problem-solving outcomes.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".