Exploring Engagement and Self-Efficacy in an Introductory Computer Science Course
Bibliographic record
Abstract
Introductory computer science courses often pose unique challenges for non-computer science majoring students, and understanding the factors that contribute to these struggles is crucial for enhancing students' learning experiences. This research delves into the engagement and self-efficacy of 14 international undergraduate students enrolled in an introductory computer science course tailored for non-CS majors. We use a combination of an initial online survey and the Experience Sampling Method (ESM) to gather data on students' experiences and perceptions throughout the course. The ESM interviews conducted during students' tutorials offer real-time insight into the fluctuations of their engagement and self-efficacy. Findings reveal a positive correlation between aspects of engagement and self-efficacy, indicating that students' higher levels of engagement coincide with stronger beliefs in their capabilities to succeed in the course. Moreover, we identified course topics with which students were disengaged and that corresponded to lower self-efficacy. By recognizing the challenges faced by non-CS majoring students and the impact of specific course topics and teaching styles on their engagement and self-efficacy, we provide advice for designing tailored interventions and instructional strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".