Everyday Problem Solving In Engineering: Lessons For Educators
Bibliographic record
Abstract
Many engineering programs have integrated problem-based learning (PBL) into their instruction.Quite often, the problems that are solved in PBL programs are not authentic.In order to develop more authentic problems that are required to prepare engineering graduates to solve complex, ill-structured workplace problems, we developed a case library of engineering problems as described by practicing engineers.The qualitative analysis of those stories showed that workplace problems are ill-structured because they are constrained by unpredictable, non-engineering parameters; driven by multiple, often conflicting goals; evaluated using non-engineering success criteria; possessing aggregates of smaller well-structured problems; requiring complex collaborations; and replete with unanticipated problems.The implications for developing problem-based learning environments in engineering are clear: problems must represent more complexity, ambiguity, collaboration, and dynamic conditions.Of all of the ABET accreditation standards, undergraduate and graduate engineering students as well as practitioners consider the ability to design and conduct experiments and to identify, formulate, and solve engineering problems as being the most important 1.In an effort to meet ABET accreditation standards and to better prepare engineering graduates, engineering education programs have been implementing a variety of forms of problembased learning (PBL).In fact, several engineering programs around the world (e.g.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".