ONE-MINUTE QUIZZES TO IDENTIFY POTENTIAL STUDENTS AT RISK IN ENGINEERING COURSES
Bibliographic record
Abstract
Abstract – Recent years have witnessed increased interest in reducing student attrition at universities due both to matters of improved student outcomes and the practical fiscal benefits accrued from improved retention. The reason for student attrition can be due to many contributing factors (maturity, motivation, external stresses, etc.) and may be difficult to predict. Usually the home Faculty (and sometimes even the student) is only aware of risked academic failure at the end of the semester. At this point it is generally too late for an intervention to be effective. Although there are numerous intervention models that can be used to help students, these interventions are only effective if students can be identified early in their studies. One method to address this is to assess all students as frequently as possible and in as many courses as possible to create a map of student academic engagement and performance. This paper focuses on the first phase of a long-term study to create such an early warning system to help find students at risk. The first phase consisted of using one-minute quizzes during classes and general attendance to determine the level of engagement of each student. This data was then used to try to identify students in advance that risk academic failure. It was found that students who regularly missed 30% of class within the first 5 to 8 classes provided an indication of the likelihood of failure of the student.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".