Contextualization in engineering education: A scoping literature review
Bibliographic record
Abstract
Abstract Background Engineering educators prepare students for responsible, ethical, and socially aware engineering practice by contextualizing engineering in a variety of ways. Recently, ABET and the NAE have prioritized engineers' ability to make judgments considering a variety of contexts and specifically advocated for building engineers' contextual competencies. Purpose Educators can often agree that contextualizing engineering work and problems is beneficial, while taking different approaches to that contextualization. It is important for engineering educators to know how their modes of contextualization compare with others, as well as how they define and achieve success. Scope/Method This scoping literature review answers two research questions: How are engineering educators contextualizing engineering through their programs, courses, and pedagogies? And what are the justifications, motivations, or desired ends of engineering educators' contextualization? The original search yielded 500 articles from pertinent engineering education venues. After detailed exclusion and inclusion criteria were applied, 104 relevant articles were analyzed. Results These remaining articles were sorted into six modes of contextualization: context tools, professional skills, real‐world problems, design, sociotechnical thinking, and social impact. The categorization and analysis led to a complex understanding of the multiplicity of contextualization in engineering education. Conclusions The wide variety of modes of contextualization results in a variety of bettering strategies, or ways that these forms of pedagogy can improve engineering education, and, in turn, larger engineering contexts. We conclude that engineering content and context are “interactional” and co‐constructed, showing how different modes of contextualization demarcate different images of what engineering content and contexts are and ought to be.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".