Use of a Cornerstone Project to Teach Ill-Structured Software Design in First Year
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it