Inquiry Learning Methodologies and the Disposition to Energy Systems Problem Solving
Bibliographic record
Abstract
In this paper, we argue that it is essential to pay attention to the engineering students’ use of sound methodologies in approaching engineering problems. There are serious challenges created from surfacelearning attitudes that undermine foundational, conceptual understanding and basic methods to solve technical problems. Moreover, such attitudes carry over to how students approach the complexity and human aspect of engineering problems. Senior undergraduate energy systems courses were redesigned to develop students’ inquiry and problem solving skills. Data from a post-course survey, completed by 58 senior engineering students, were analyzed using a thematic analysis and basic categorization. Findings suggest that inquiry learning (IL) and problem based learning (PBL) methods offer much value in the students’ development of researchand analytic skills. As well, students gained a deeper appreciation of complexity and the ethical issues in energy system challenges, which may have some impact on their assumed responsibility as engineers - during the process and in the aimed outcomes of their problem solving tasks. We reflect on the findings to propose how IL and PBL might be effectively designed and implemented for engineering students engaged in system level analyses.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".