Program SAGES: Promoting collaborative teaching development through graduate student/faculty partnerships
Bibliographic record
Abstract
Each year, graduate students shoulder hours of instructional time with undergraduate students and some have more contact hours with students than academic staff in large introductory undergraduate courses. However, many graduate students are given minimal opportunities for teaching development, and there is a great need to help them develop a scholarly and reflective teaching practice (Kenny et al., 2014; Chick & Brame, 2015). Enhancing the teaching skills of graduate students is a critical investment that will also create a culture of educational leadership, and foster innovation and teaching development. To support STEM graduate students in the development of an evidence-based teaching practice, we designed and implemented the SAGES Program (SoTL Advancing Graduate Education in STEM) at a research-intensive university. This program was designed to provide graduate students with opportunities to learn about scholarly teaching and learning (SoTL) within the context of STEM through a semester-long course, followed by a semester-long practicum. The practicum gives graduate students an opportunity to apply their learning in an undergraduate class, in partnership with a faculty member acting as a mentor. Through a mixed-methods approach based on the use of semi-structured interviews and pretest and posttest surveys (DeChenne et al., 2012; Trigwell and Prosser, 2004), we will show that SAGES not only increased teaching self-efficacy, knowledge and skills in graduate students, but also led to collaborative teaching development for both mentors and mentees. We will also invite participants to conceptualize how such a program could be designed for their own institutions.\nChick, N. L. & Brame, C. (2015). An investigation of the products and impact of graduate student SoTL programs: observations and recommendations from a single institution. International Journal for the Scholarship of Teaching and Learning, 9(1), article 3.\nDeChenne, S. E., Enochs, L. G., & Needham, M. (2012) Science, technology, engineering, and mathematics graduate teaching assistants teaching self-efficacy. Journal of the Scholarship of Teaching and Learning, 12(4), 102-123.\nKenny, N., Watson, G. P. L., & Walton, C. (2014) Exploring the context of Canadian graduate student teaching certificates in university teaching. Canadian Journal of Higher Education, 44(3), 1-19.\nTrigwell, K. & Prosser, M. (2004) Development and use of the approaches to teaching inventory. Educational Psychology, 16(4), 409-424.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".