Situated Engineering Learning: Bridging Engineering Education Research and the Learning Sciences
Bibliographic record
Abstract
C ontributors Indigo Esmonde, University of Toronto; Krishna Madhavan, Purdue University; Wolff‐Michael Roth, University of Victoria; Dan L. Schwartz and Jessica Tsang, Stanford University; Estrid Sørensen, Humboldt University and Aarhus University; Iris Tabak, Ben Gurion University of the Negev B ackground The field of engineering education research has seen substantial growth in the last five years but it often lacks theoretical and empirical work on engineering learning that could be supplied by the learning sciences. In addition, the learning sciences have focused very little on engineering learning to date. P urpose This article summarizes prior work in the learning sciences and discusses one perspective—situative learning— in depth. Situativity refers to the central role of context, including the physical and social aspects of the environment, on learning. Furthermore, it emphasizes the socially and culturally negotiated nature of thought and action of persons in interaction. The aim of the article is to provide a foundation for future work on engineering learning and to suggest ways in which the learning sciences and engineering education research communities might work to their mutual benefit. S cope /M ethod The article begins with a brief discussion of recent developments in engineering education research. After an initial overview of the field of learning sciences, situative learning is discussed and three analytical aspects of the perspective are outlined: social and material context, activities and interactions, and participation and identity. Relevant expert commentaries are interspersed throughout the article. The article concludes with an exploration of the potential for contributions from the learning sciences to understanding engineering learning. C onclusion There are many areas of mutual benefit for engineering education and the learning sciences and many potential areas of collaborative research that can contribute not only to engineering learning but to the learning sciences.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".