Impact of Open-Ended Assignments on Student Self-Efficacy in CS1
Bibliographic record
Abstract
A goal of many Computer Science Education (CSE) researchers is reconceptualizing aspects of introductory Computer Science (CS1) to increase student engagement and retention. The measure of self-efficacy, or one's personal judgment about their ability to accomplish a task, is a valuable component of student learning as it affects one's level of effort and perseverance against obstacles. A potential way to restructure aspects of CS1 to increase self-efficacy is by allowing students to have more room for freedom/experimentation within assignments. The purpose of this study is to analyze the impact of a specific, open-ended assignment structure on self-efficacy and academic performance, through a quasi-experimental study involving undergraduate CS1 students. Two concurrent lecture sections (Section A and B) with the same instructor were given two different versions of an assignment --- (1) a control version with a typical, standard structure, and (2) an open-ended version with an additional requirement to add enhancements of the student's own choosing to the project. For assignment 1, Section A completed the control assignment, while Section B completed the open-ended assignment. For assignment 2, to counterbalance the groups, Section B completed the control assignment while Section A completed the open-ended one. We found both average self-efficacy and average assignment grades were consistently (although not significantly) higher for students who completed the open-ended versions, and that self-efficacy significantly affected the average grade of both assignments, regardless of the type of assignment structure.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".