Impact of Open-Ended Assignments on Student Self-Efficacy in CS1
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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