Contextualization in engineering education: A scoping literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it