Use Of The Triple-Bottom Line Framework To Examine The Design Tendencies Of First Year Engineering Students
Bibliographic record
Abstract
Engineering design often requires the examination of multiple different factors and a design selection based on compromise between these factors. An engineer's preexisting values and experiences can influence design decisions. Therefore, knowing and understanding these design tendencies can prove valuable in guiding engineering students with their future design selections. The purpose of the project is to examine the design tendencies of first year engineering students using an interactive web-based virtual reality (VR) module focused on the triple-bottom line framework. The triple bottom line sustainability framework measures design in three key areas: people, profit and planet. The course for which the interactive module has been developed is a first-year engineering course called Chemistry of Natural and Engineered Systems. The activity is based around the chemical production of 6- aminopenicllianic acid through hydrolysis of Penicillin-G. This paper presents an explanation of the interactive web-based VR module, explores student design tendencies before an optimization problem, evaluates their design selections while completing the optimization problem and analyzes student reflections. Determining students design tendencies before the VR activity will help the teaching team gain insight into student thinking process about engineering design and determine the extent of variability of first year student design tendencies. We also envision this project as the first step of a longitudinal project to investigate the influence of undergraduate engineering education on student design tendencies.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".