Work in Progress: Improving Engineering Students’ Need-finding Abilities
Bibliographic record
Abstract
Design theories, such as the popular design thinking approach, outline several stages of design, typically: needs assessment, problem definition, concept generation, implementation, and evaluation.While engineering students apply design methods, they rarely practice needs finding.All Canadian undergraduate engineering students participate in a capstone project in their fourth year.Engineering instructors at the University of Waterloo have identified a lack of opportunities for students to practice their need finding skills prior to fourth year.As a result, a set of need finding instructional activities were conducted in-class for one term.The objective of this research is to conduct evidence-based program improvement by identifying the teaching practices that improve need finding competencies in engineering graduates.More specifically, in this ongoing study, the authors explore how students identify, select, and justify their capstone project problem; and whether in-class instruction on needs identification and assessment improved capstone project outcomes.To address these objectives, we employed a survey to measure the effects of in-class instruction on student need finding abilities.The survey was disseminated to students halfway through their capstone design project.We compared the need finding and problem identification strategies of those students who received need finding interventions to those who did not and found the intervention encouraged students to begin their projects earlier and engage in a more in-depth problem finding process.Since the introduction of the intervention, capstone instructors recognized the benefit of education on problem-finding which was confirmed by our study findings.As a result, need finding has been implemented into course curriculum.Future work can determine if the effects of need finding interventions improved overall capstone project quality.The results of this project will aid in the design of future interventions and engineering teaching practices.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".