Cross-disciplinary thermoregulation and sweat analysis laboratory experiences for undergraduate Chemistry and Exercise Science students
Bibliographic record
Abstract
Cross-disciplinary (CD) learning experiences benefit student understanding of concepts and curriculum by offering opportunities to explore topics from the perspectives of alternate fields of study. This report involves a qualitative evaluation of CD health sciences undergraduate laboratory experiences in which concepts and students from two distinct disciplines [chemistry (CHEM) and exercise physiology (EPHE)] combined to study exercise thermoregulation and sweat analysis. Twenty-eight senior BSc Kinesiology (EPHE) students and 42 senior BSc CHEM students participated as part of their mutually exclusive, respective courses. The effectiveness of this laboratory environment was evaluated qualitatively using written comments collected from all students as well as from formal focus groups conducted after the CD laboratory with a representative cohort from each class (n = 16 CHEM students and 9 EPHE students). An open coding strategy was used to analyze the data from written feedback and focus group transcripts. Coding topics were generated and used to develop five themes found to be consistent for both groups of students. These themes reflected the common student perceptions that the CD experience was valuable and that students enjoyed being able to apply academic concepts to practical situations as well as the opportunity to interact with students from another discipline of study. However, students also reported some challenges throughout this experience that stemmed from the combination of laboratory groups from different disciplines with limited modification to the design of the original, pre-CD, learning environments. The results indicate that this laboratory created an effective learning opportunity that fostered student interest and enthusiasm for learning. The findings also provide information that could inform subsequent design and implementation of similar CD experiences to enhance engagement of all students and improve instructor efficacy.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".