From Problem Solvers to Problem Seekers: The Necessary Role of Tension in Engineering Education
Bibliographic record
Abstract
In this paper it is proposed that the current focus on problems in engineering education and technological literacy may be more constructively reframed by focusing on tensions.Priyan Dias claims engineering has an identity crisis that arises from tensions inherent in: 1) the influence of the profession on society, 2) the role engineers play, and 3) what constitutes valid knowledge in engineering.These are ethical, ontological, and epistemological tensions respectively, which Dias frames as a tension between identities of homo sapiens and homo faber.Beyond the tensions in engineering there are additional tensions that arise for engineering educators that impinge on identity, but derive from educators' beliefs about the aims of education and beliefs about teaching.With respect to the aims of engineering education the tension arises between utilitarian and humanistic aims and plays out through debates about the importance of diversity (inclusion vs. professionalization), discussion of which courses should be included in a curriculum, and the long simmering debate on four year vs. five year engineering degrees in the United States.Tensions that arise from beliefs about teaching are seen in the discussions on the relative merits of summative vs. formative assessment, student-vs.instructorcentered learning, and the relative merits of inquiry-based and active learning.Given that one aspect of the identity of an engineering education is being a problem solver, faculty may perceive these tensions as a problem or conflict to be solved.An alternative view is to see tensions as both necessary and generative.Tensions are necessary since they are a natural part of human affairs and generative in that tensions highlight dialectics from which new truths or perspectives emerge.From this viewpoint a key element of faculty development is developing a defensible personal philosophy that both lets one navigate and learn from the inevitable tensions that will arise in practice as well as contribute to larger dialogs from which new systems and forms of education emerge.
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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 | 0.047 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.018 | 0.094 |
| Scholarly communication | 0.032 | 0.048 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".