A Collaborative Student Approach to Address First-Year Academic Challenges in Science
Bibliographic record
Abstract
Attrition rates at postsecondary institutions are highest in the first year of studies [1][2]. It is therefore imperative to determine the core causes of attrition to effectively remedy this problem. In this project, three undergraduate students from different science disciplines conducted a survey of 204 undergraduate Science students to identify and address challenges faced by these students in their first year of studies at the University of Windsor. Data collected were compared across three major categories: discipline, gender and status (domestic vs international). Key points gleaned from the survey data relate directly to students' studying habits, engagement with professors, and the use of external academic resources. On average, students rated their first-year experience in Science 3.25 out of 5, which correlates with a good ranking. A comparison of study habits revealed that students in Biology-related programs tended to spend the most time per week reviewing their class notes, while students in Math, Computer Science, and Physics were more likely to use external resources (e.g. online tools) for academic support. Tutoring services were popularly used among all students and deemed beneficial. Although most students expressed acknowledgement of their professors' support, international students ranked highest in satisfaction and comfort with professors, while females scored lower than males. Finally, students expressed the need for science-focused exam preparation and career workshops to better support the first-year transition. Using this information, the multidisciplinary team of researchers then developed an exam preparation workshop to acutely target difficulties students typically faced in first year examinations. This initiative was recently launched through the Faculty of Science's Undergraduate Science Collaborative and Integrative (USci) experience network. As a result, this project has generated more opportunities for student engagement and the creation of other initiatives aimed at supporting and enriching the first-year academic experience within the Faculty of Science. [1] J. P. Grayson and K. Grayson, Research on Retention and Attrition. The Canada Millenium Scholarship Foundation, 2003. [2] T. Qui and R. Finnie, Moving Through, Moving On: Persistence in Postsecondary Education in Atlantic Canada, Evidence from the PSIS. Statistics Canada: Minister of Industry, 2009.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".