Interdisciplinary science boosts to affective domain learning and student engagement
Bibliographic record
Abstract
Undergraduate interdisciplinary science programs exist, in one form or another, at many Canadian post-secondary institutions. However, data is only recently becoming available on the effectiveness of these programs in achieving their stated outcomes. Even sparser is the research on these programs for achieving affective domain learning goals, known to promote life-long curiosity and a civil society. This talk will present the results obtained from two multi-year research programs: one in SCI 100, an interdisciplinary science first-year undergraduate experience, and one in Science Citizenship, a project-based upper-class undergraduate course. Mixed-methods research was used, including pre/post student surveys, instructor and student focus groups, alumni interviews and correlation between SCI 100 grades obtained and grade point averages in later years, with the initial research goal of measuring the efficacy of these two learning experiences. However, by probing student expectations, experiences and perceptions, critical aspects of the learning pedagogy and curriculum were identified that supported affective domain learning and led to higher student engagement. Not too surprisingly, the results show that both the interdisciplinary nature of these courses (curriculum) and the nature of how the activities were structured (active and/or discovery learning, group work, and student choice of topics) all contributed to internalization of a scientific value system and greater internalization of learning motivation. The correlation results will be discussed in terms of the effectiveness of SCI 100 for future learning in science.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".