Problem-solving in biology vs. engineering: What can engineering educators learn from biology educators
Bibliographic record
Abstract
Problem-solving (PS) is a universal skill inherent to nearly all disciplines. This study’s objective is to explore the types of PS assessments that engineering and biology undergraduate students are exposed to and what PS approaches they use to complete these assessments. Comparing PS assessments and approaches between the two disciplines will help reveal important lessons that engineering educators can apply when immersing their undergraduate students into PS. Qualitative data was obtained from focus groups with students in engineering (n = 6), and biology (n = 5). Notable differences were found across disciplines, with students mentioning different skill sets pertinent to PS, assessment features, and PS strategies. A posteriori analysis of students’ focus group responses revealed that an epistemic lens is an appropriate framework for interpreting students’ response. Schommer’s epistemic dimensions of knowledge (i.e., structure and stability of knowledge) are used to classify results and indicate that biology students are frequently exposed to the complex structure of knowledge through multi-factorial systems whereas engineering students are typically exposed to the instability of knowledge, particularly through design projects. Other interesting observations related to biology students’ tendency to engage in discussion as a helpful study approach, while engineering students may view group discourse as a hindrance. Our results can inform engineering educators of how they can incorporate PS practices used by biology educators into their classrooms to promote better learning outcomes and encourage deeper learning approaches in students, while cultivating more mature epistemic beliefs.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".