Service learning and empathy in technical communication courses within engineering education: A case study to improve the “culture of disengagement” of engineering students
Bibliographic record
Abstract
Both industry and engineering accreditation bodies have outlined outcomes for engineering education to develop more socially minded engineers. However, a recent longitudinal study found a disturbing trend of decreased public engagement in engineering students during and even after their formal engineering studies. The first step towards public engagement is empathizing with them to understand their needs. Empathy is an area of growing interest in engineering education with suggestions that a broad cultural shift is necessary to facilitate increased discussion of empathy and public welfare. \nOne popular technique for engaging students with real-world problems that affect the community is service learning. Service learning pedagogy is an experiential learning technique that exposes students to serving community not-for-profit organizations while simultaneously meeting the academic learning objectives of the course and engaging students in reflection on their experience. The service-learning project under study required engineering students in a technical communication course to work with a not-for-profit client and adapt a technical document for a particular target audience the client was trying to reach. \nThis mixed-methods case study examines a service learning project in ta technical communication course within an engineering program to understand if it can help reverse the trend of decreased engagement in engineering students and enhance student empathy. Quantitative data was gathered by testing student participants using the Toronto Empathy Questionnaire and repeating the public engagement survey to see what effect service learning and humanities-based instruction has on students’ engagement with public welfare. Qualitative data included an analysis of individual student reflections, a client interview, and instructor field notes to triangulate the data. \nThe major findings of this study show a similar quantitative decline in empathy as was already found in public engagement in the original study that prompted this research. However, when paired with the qualitative data, it revealed a picture of students who wanted and believed themselves to be engaged and empathetic with the public, but unable to take the necessary actions because of other factors in engineering education including an overwhelming workload, English as Additional Language challenges, and team dysfunction. It seems these distractions in individual instances become a habit of apathy that becomes an overarching culture of disengagement in engineering education.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".