Fuzzy logic in engineering education and evaluation of graduate attributes
Bibliographic record
Abstract
Fuzzy logic, which was invented in 1960’s, in response to emerging needs to deal with complex techno-social concepts, is becoming more and more relevant to today’s problems. Nowadays, fuzzy logic should not only become a part of the engineering curriculum but also a part of the engineering education standards. For example a fuzzy approach can be used in evaluating graduate attributes (GAs). Most graduate attributes are fuzzy and need to be evaluated using a fuzzy logic methodology.The present paper is an attempt to introduce fuzzy tools (such as fuzzy sets and fuzzy linguistic value systems) to provide a metric for defining and evaluating graduate attributes. Proper definition and scaling of fuzzy attributes can provide a common language, through which educators, industry, and regulators can communicate and collaborate more effectively in the process of assigning jobs to engineers with attributes which best fit the task. Also, by using a fuzzy method, the uncertainty of attributes is neither magnified nor dampened in the analytical process (contrary to most conventional approaches).A properly defined fuzzy metric for GAs can provide flexibility in the implementation of the system, while reducing the overall errors in evaluation. Graduate attributes are proposed to be divided into three major classes or spaces (i.e. knowledge, social and ethical), each consisting of a number of fuzzy attributes and sub-attributes, which can be summed up with appropriate weighting factors. A neural network engine can be used to find the optimal weighting factors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".