Reflection in Engineering Design: Student Perceptions on Usefulness
Bibliographic record
Abstract
Reflection in engineering design promotes the development of personal and professional skills, helping students to document the steps they took, examine the outcomes, and looking ahead to the following weeks.This reflective practice contributes to adopting a growth mindset and becoming life-long learners.In a study of 1,278 reflections of 83 second-year engineering students over two years, this paper is an exploratory examination of the act of reflecting in a twosemester engineering design course.Reviewing an end-of-year survey on the act of reflecting as well as the reflections themselves, this study presents student perceptions of reflections and whether the reflections changed throughout the design process.We found that 55% of participants describe reflections as useful, and 78% of participants describe the reflections as impacting their design project, team dynamics, or personal development.Seven themes are documented about student perceptions of reflections, including: expansive thinking, examining the project more deeply, team dynamics, goal-setting, looking back at progress, planning next steps, and functional critiques.We also found that the number of words for each reflection changes with the design process.
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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 | 0.000 | 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) | 0.000 | 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".