Needs-Supportive Teaching Interventions in an Intro Computer Science Course: Exploring Impacts on Student Motivation and Achievement
Bibliographic record
Abstract
The instructor of a large, introductory computer science (CS) course at a public Canadian university implemented two interventions designed to support students' academic success and basic psychological needs as posited by self-determination theory (SDT). Interventions involved providing grading scheme choice for all students and sending targeted support emails to students who struggled on early term assessments. In keeping with SDT, we assessed the possible effect of these interventions on students' perceptions of competence (self-efficacy), autonomy, relatedness (via measures of instructor warmth), and final grades, by comparing the intervention cohort with a previous control cohort. Results indicate that all students in the intervention term may have benefited from grading scheme choice, as they earned higher final grades and felt more autonomous than the control group students. Moreover, struggling students who received support emails earned an average final grade 11.3% higher than struggling students in the control term. These students also performed closer to their non-struggling counterparts than those in the control group, reducing the achievement gap between early struggling and non-struggling students by 8.1%. Furthermore, even when controlling for past achievement, perceptions of self-efficacy and autonomy support positively predicted students' final grades across groups, with a small effect size. These results offer theoretical and practical insight into effective, light-touch teaching interventions which CS instructors can implement in large courses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".