ReFresh: Retaining First Year Engineering Students and Retraining for Success
Bibliographic record
Abstract
Student retention and support are key priorities at the University of Toronto Faculty of Applied Science and Engineering, as is evidenced by a first year to second year average retention rate of 91% over the past 4 years. At U of T Engineering, academic standing and registration are determined on a term-by-term basis. As a result, student performance in the fall term can result in obligatory withdrawal from their studies in January, including first year students. While approximately only 4% of the first year class have to withdraw, the impact can be very distressing for the individual student, as his/her plans for the year are disrupted and their confidence shaken. Withdrawal from studies can occur for many reasons such as insufficient academic background from their high school, personal crisis, low resilience and lack of study skills and these struggles can be especially acute for international students. The ReFresh Program gives these students an opportunity to continue their education while learning from their mistakes. A small classroom environment allows students to integrate into a collaborative community, create study groups with classmates, communicate easily with their instructors and TA’s and benefit from constant support from First Year Office staff. This format helps students deal with the personal and academic challenges that prevented them from performing at their best in the fall term, relearn the foundations and key concepts of Calculus, Physics, Computer Programming, Chemistry and Linear Algebra and develop a plan to be successful for the next fall to repeat their first semester. This paper will discuss the structure of the ReFresh program as well as the impact on student success, student development and retention.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".