Transdisciplinarity and the Dangers and Opportunities of STEM Education: An Interview with Dr. Pratim Sengupta
Bibliographic record
Abstract
Dr. Pratim Sengupta is a professor of learning sciences, and a member of the Graduate Faculty of Computational Media Design at the University of Calgary, where he has also served as Research Chair of STEM Education. Prior to joining the University of Calgary, Dr. Sengupta was a professor at Vanderbilt University's Peabody College, where he co-founded and chaired the Learning Sciences PhD program. He is the recipient of a National Science Foundation CAREER Award (2012) for his research on developing agent-based programming languages and integrating computational modeling in K12 science and math classrooms, and a Paul D. Fleck Award from the Banff Center for Arts and Creativity for his work on public computing. He is a Fellow of the International Society for Design and Development in Education. Significant editorial roles include executive editor of Cognition and Instruction (2017 – 2025), and senior editor for Oxford Research Encyclopedia of Education (2018 – ongoing). Prior to completing his PhD in learning sciences at Northwestern University, Dr. Sengupta attended Presidency College, Kolkata (India), the Indian Institute of Technology, Kharagpur (India) and Northwestern University (USA), where he received his undergraduate and graduate degrees in Physics. I first encountered Pratim’s work about five years ago while researching scholarship on transdisciplinarity in STEM and STEM education. At that time, and still today, there was a challenge in finding work that was truly transdisciplinary. Pratim’s conceptualization of coding as a participatory and embodied process aligned closely with my own view of transdisciplinary STEM. This interview is a slightly modified transcript of our conversation about transdisciplinarity and the dangers and opportunities of STEM education.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 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".