Everyday Problem Solving In Engineering: Lessons For Educators
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Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Everyday Problem Solving in Engineering: Lessons for Educators1 David Jonassen, Johannes Strobel, Chwee Beng Lee University of Missouri/Concordia University/Nanyang Technology University 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 problem- based learning (PBL). In fact, several engineering programs around the world (e.g., Aalborg University on Denmark, McMasters University in Canada, Monash University in Australia, Manchester University in England, Glasgow University in Scotland, Eindhoven University in the Netherlands, and Republic Polytechnic in Singapore) deliver the majority of their curricula via PBL. Additionally, PBL modules or courses have been implemented in numerous engineering programs, including biomedical engineering 2, chemical engineering 3, software engineering 4,5, thermal physics 6, design processes 7, aerospace engineering 8, computing 9, microelectronics 10, construction engineering 11, control theory 12. Limited efforts have even examined the use of PBL for engineering workplace training 13. While PBL represents an important pedagogical innovation in engineering education, the nature of the problems that are solved by students are inconsistent with those that engineers solve in the workplace. Workplace problems are assumed to be complex and ill-structured problems because they have vaguely defined or unclear goals and unstated constraints; possess multiple solutions, solution paths, or no solutions at all; possess multiple criteria for evaluating solutions; where there is uncertainty about which rules and theories are necessary for a solution 14. These problems often require engineers to make judgments and express personal opinions or beliefs about the problem. While engineering education programs are beginning to engage students in more authentic forms of problem solving, as evidenced by Proceedings of the 2005 American Society for Engineering Education Annual Conference& Exposition Copyright © 2005, American Society for Engineering Education
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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 | 0.001 | 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 it