How is science taught? A program level measurement of how we teach 21st century undergraduates
Bibliographic record
Abstract
Background There is much evidence for the benefits of active learning (Freeman et al., 2014), especially for less advantaged students (Eddy & Hogan 2014). Despite their importance for our graduates, the use of these approaches by staff can be low (Dolan 2015), and surprisingly little is known about the use of evidence-based teaching approaches at the program level (Wieman & Gilbert, 2014). Aims Our main aim was to measure the extent to which evidence-based teaching approaches are used across a Bachelor of Science program at our large, research-intensive institution. We compared our results to a similar Canadian university. We determined how the use of evidence-based teaching approaches differs, within our institution, by class size, year level, and discipline. Design and methods We measured the use of evidence-based teaching with the Teaching Practices Inventory (Wieman & Gilbert, 2014). This is a 72-item questionnaire that asks staff objective questions about the use of specific evidence-based practices in a course. It provides scores for each course in eight different categories of instruction. We applied the instrument to all 136 lecture-based courses in Semester 1 of our BSc program. The completion rate was 95% and the average completion time was 11 minutes. Results We found a wide range of evidence-based practice in our institution, similar to the comparison institution. The individual teaching categories revealed differences. Our institution was stronger in the Course Information category: a central policy requires written learning objectives for every course. Our institution was weaker in both the In-class Activities and Feedback categories: some practices could be improved easily, such as our common use of videos or demonstrations without asking students to first predict the results. Within our institution the first-year courses scored higher than later year courses in the Supporting Material, Collaboration (between staff) and Feedback categories. Conclusions Preparing science graduates with complex 21st century skills is dependent on scientists' pedagogical practices and how they fit together across the curriculum to influence student learning. The Wieman and Gilbert (2014) Teaching Practice Inventory provides actionable data that offers a whole of program view of teaching practices. References Dolan EL (2015). Biology Education Research 2.0. CBE-Life Sci Educ, 14, 1-2. Eddy SL, & Hogan KA (2014). Getting Under the Hood: How and for Whom Does Increasing Course Structure Work?. CBE-Life Sci Educ, 13, 453-468. Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, & Wenderoth MP (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. Wieman C, & Gilbert S (2014). The teaching practices inventory: a new tool for characterizing college and university teaching in mathematics and science. CBE Life Sci Educ 13, 552-569.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".