Can Interaction Patterns with Supplemental Study Tools Predict Outcomes 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
Recent research suggests that one-third of the students enrolled in CS1 courses typically end up failing. Several studies have demonstrated how learning tools can assist struggling students. This work presents the evolution of a practice tool co-designed with student input. BitFit was developed to (1) provide students with an environment to practice weekly material and receive support when needed; and (2) collect student usage data as students progress through programming exercises. Our analysis of 652 students over three semesters highlights a number of predictors for success. Our findings support recent studies that suggest that at-risk students can be identified as early as two weeks into the semester; this group accounted for almost 30% of the students who failed the course in our study. Our results also reveal that interaction patterns with BitFit, in particular with hint features requested by students, allow the identification of another 52% of students who eventually fail. Throughout the semester, students who failed the course used hint features four times as often as top students, while only attempting to compile code one-third as often. The combination of early indicators and interaction patterns identify 81% of students who failed the course during our study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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