DEVELOPMENT OF AN INTERVENTION SCHEME TO ADDRESS LOW RETENTION RATE OF A FIRST-YEAR CALCULUS COURSE – A SYSTEMATIC ANALYSIS OF CURRENT TRENDS
Bibliographic record
Abstract
Students in engineering and the sciences often complete their studies in mathematics before they have an opportunity to develop an appreciation for the application of mathematical concepts in their major field. It can be argued that without a solid foundation in mathematics at the calculus level, an engineering or science student will find difficulty in understanding and applying the knowledge involved in upper-level classes. In this study, we examined an Ontario university where the dropout rates could reach as high as fifty percent from a mandatory first-year calculus course and as a response, we would like to develop an intervention mechanism. Using a conceptual framework, we systematically analysed various intervention mechanisms employed by institutions around the world. The framework looks at each intervention strategy and tries to understand how the mechanism identifies who needs intervention, how it is funded, and the steps necessary for a student who would want to receive such an intervention voluntarily. This study will help us to identify key features that are effective in current intervention methods, address the gaps that were observed, and to develop an intervention scheme that addresses high dropout rates from the first-year calculus course.
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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 | 0.139 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".