ASEE 2024 Paper—Examining Cultural Elements to Enable Change
Bibliographic record
Abstract
Abstract The future of engineering education requires engineering faculty, schools and programs to enact change in the curriculum to respond to the complex challenges in our world today and to recognize the socio-enviro-technical nature of engineering practice. Engineering leadership education is premised on the principle that developing strong leadership competencies is essential to effectively and appropriately enable the contextual application of the traditional technical competencies that are often the primary focus of undergraduate engineering programs. In our 2023 paper entitled Engineering Leadership: Bridging the Culture Gap in Engineering Education [1] we argued that a major barrier to change in engineering education, including the incorporation of engineering leadership into the curriculum, is the culture that exists in our institutions. We proposed that the elements and dynamics of this culture can be examined in the form of co-contraries (or opposites that need each other) and that the relative emphasis in these co-contraries reflects the engineering educational culture in a department, an institution or in engineering education as a whole. Example cultural co-contraries identified include: the power distance dynamic between the student and the professor; the nature of the distribution of effort between teaching and research; and the tension between technical and non-technical content emphasis and delivery. In this paper we aim to look more deeply at cultural constructs associated with engineering education through a targeted literature review and integrative analysis of engineering education culture, the engineering culture of professional practice, the general theoretical constructs of culture and cultural dimensions in societal and organizational contexts with the explicit purpose of developing a foundational understanding of the cultural co-contraries observed and discussed in our 2023 paper. This foundational understanding can then be used to build a model for characterizing engineering education culture and evaluating positive cultural change and resistance to change in engineering education. We believe that capturing and understanding the cultural constructs associated with engineering practice and engineering education will not only be helpful in effecting change in engineering education, it will also support the need for engineering leadership and followership education to be explicitly taught as core concepts at both the undergraduate and graduate level to support the requisite systemic changes in engineering practice and education to address the complex socio-enviro-technical challenges that engineering must address. The paper supports LEAD division priorities of "Inform" and "Explore".
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".