New Interdisciplinary Science Course for First-Year Faculty of Science Students: Overview and Preliminary Results from the Pilot
Bibliographic record
Abstract
Transitioning to university can be a daunting endeavour, with student success dependent on a myriad of effects (Pascarella & Terenzini, 2005). Understanding how to navigate university systems, who to meet, how to get help, how to study, and what goals to set can be hard to grasp (Valle et al., 2003). We provide an overview of the new interdisciplinary foundations course, which piloted in fall 2014, for first-year Faculty of Science students at McMaster University. This course provides a taste of research-based learning (Healey, Jenkins, & Lea, 2014) and develops essential skills that are important for an undergraduate degree and future academic or career plans, exposes students to a wide range of departments and programs in the Faculty of Science, and invites students to reflect on their academic journey and how it may be changing as a result of the course. This customized approach intentionally teaches students how to locate and use institutional resources and the expectations that the institution has of its students, while offering opportunities to create networks of support essential for student success and retention (Kuh, Cruce, Shoup, Kinzie, & Gonyea, 2008), and speaks to a number of considerations highlighted in the literature (e.g., Ambrose, Bridges, DiPietro, Lovett, & Norman, 2010). Other factors considered include balancing the needs of the Faculty, the resources available, and the goals, demands, and interests of the students. In this paper, we describe the course’s design, structure and implementation, key components of the course, support from upper-level science students, and preliminary pedagogical results, which assess its impact on and perception by students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".