Experimenting with labs: Practical and pedagogical considerations for the integration of problem‐based lab instruction in chemical engineering
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Laboratory instruction is a core component of the training of chemical engineers. The hands‐on experiences in the laboratory are designed to facilitate the development of critical analytical skills, establish links between theory and reality, and develop transferrable skills. In the Department of Chemical and Biological Engineering (CHBE) at the University of British Columbia (UBC), the senior laboratory course was designed using a Problem‐Based Laboratory (PBL) approach to shift part of the responsibility for the learning experience from the instructor to the students, with the aim to improve learning outcomes. In this course, student teams perform 10‐week open‐ended labs in which they design and execute unique experimental plans to address industrially relevant problem statements. This course leverages student autonomy and ownership of their work, the flexibility of deliverables, and low‐stakes opportunities to make and fix mistakes to increase student engagement, which in turn facilitates the development of critical thinking and decision‐making skills and increases student confidence in their engineering abilities. This paper synthesizes student feedback, performance data, instructor observations, and logistical experiences over several iterations of this course to identify the key elements required for the successful implementation of PBL instruction. The rationale for this shift in pedagogical approaches, the pedagogical grounding underpinning this design, the basic course structure and its reception by students, and the main challenges of this type of course implementation in chemical engineering are also presented.
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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 | 0.002 |
| 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