Unlocking Engineering Threshold Concepts Through Stakeholder Engagement
Bibliographic record
Abstract
A threshold concept is a core idea that is conceptually challenging for students, but once grasped, has the potential to radically transforms students’ perception of the subject. Given threshold concept’s central role to student learning, it is key that they are identified and constructively aligned within the engineering curricula. In this paper, we propose an approach to facilitate the identification of threshold concepts in undergraduate engineering courses. The approach is based on the framework of transactional curriculum inquiry where educators work with a group of stakeholders (students, curriculum designers, industry practitioners) to identify threshold concepts. The process is facilitated by a participatory simulation developed using agent-based modeling. We perform two experiments in a senior automation and controls course to identify threshold concepts. Our results show that this approach can be used to identify threshold concepts and that these concepts do have a significant link to student success in the course’s subject area.
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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 | 0.041 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".