The Impact of Open-ended Exercises on Student Experiences in Introductory Computer Science
Bibliographic record
Abstract
Improving student engagement and learning within introductory Computer Science (CS1) courses has been heavily studied within CS Education (CSE) research due to their fundamental role in a student’s early experiences with learning foundational CS concepts. One issue with many CS1 courses is that the practice of standardized grading leads to limited opportunities for creative thinking, which is problematic since the CS industry often demands and encourages creativity. This dissertation examines the impact of incorporating opportunities for creativity into a CS1 course through the use of open-ended exercises. Data was collected from 284 students in a CS1 course, where roughly half of the students completed exercises which included an open-ended aspect that allowed them to make their own decisions about their projects. The other half completed similar exercises throughout the semester but with a specifically defined, closed-ended checklist of requirements. A mixed-methods analysis of survey data collected throughout the course showed a significant connection between open-ended exercises and (1) increased motivation, especially in terms of confidence and satisfaction, (2) faster self-efficacy gains in the middle of the term, and (3) stronger feelings of enjoyment and ownership about the exercise material. Although no effect on academic performance was found, open-ended exercises had important attitudinal effects, improving personal learning experiences by fostering positive feelings in students about the coursework. The data also suggested the potential for open-ended exercises to promote diversity in CS1 by positively impacting motivation and self-efficacy particularly for female, nonmajor and inexperienced students.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".