Emotional intelligence in engineering education: exploring the influence of empathetic design approaches in a fourth-year engineering class
Bibliographic record
Abstract
This paper explores the impact of integrating Design Thinking and Systems Thinking frameworks and methodologies into a participatory learning environment to enhance emotional intelligence (EI) among fourth-year engineering students at the University of Victoria. This study was undertaken to address the noted gap in the social competencies of engineering graduates frequently noted in both literature and by engineering practitioners. A course, “Infrastructure Design with Indigenous Communities”, was developed, drawing on theories of identity formation and best practices from educational psychology. A mixed methods approach was used to analyze the data. This included pre- and post-semester EQ-i 2.0 EI inventories, and thematic analysis of qualitative self-reflective writings to triangulate the data. The sample included 17 fourth-year civil engineering students. Statistical analysis of pre- and post-semester EQ-i 2.0 EI inventories from the 17 students indicated an average overall EI increase of 5.4 points with a critical t-value of 3.105 and a p-value of 0.0034, rejecting the null hypothesis that the course did not affect the students' EI. Qualitative data and thematic analysis were used to triangulate the findings to support the hypothesis that the course had a direct impact on the students’ EI. This study is highly relevant to engineering education and practice. It highlights and addresses the need for engineers to possess not only technical competencies, but also emotional competencies to address complex, interconnected challenges at the intersection of technology and society. This is especially crucial when working in cross-cultural contexts with Indigenous communities. This study addresses a gap of relevance to the contemporary training of engineering students. This study suggests that the careful integration of Design Thinking and Systems Thinking into engineering curricula increases many trait parameters of EI, including empathy. This may be beneficial to engineering academia and practices as it produces engineers with more developed social competencies who may be better equipped to consider the social nuances and impacts of engineering designs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".