Board 13: Work in Progress: Exploring Student Disposition in a Foundational Conservation Principles of Bioengineering Course
Bibliographic record
Abstract
Abstract This paper focuses on a second-year required course designed to provide students with a foundational understanding of the conservation of mass, energy, and momentum. This course emphasizes conceptual problem-solving, which necessitates grasping a problem conceptually before solving it. We hypothesize that this type of problem-solving approach, combined with the applied math content, may lead to doubts among students regarding their ability to do well in such a class, potentially affecting their motivation. In a related Fall 2022 study, students were consented and asked to answer a questionnaire with 60 questions that were taken from the validated instruments: the Index of Learning Styles, the Intrinsic Motivation Inventory, the Growth Mindset Scale, and sense of belonging questionnaire [blind citation]. The research team identified a few key indicators that showed a strong correlation in to performance in the course: Reflector/activist traits from the Index of Learning Styles survey, Intrinsic motivation related to both value and expectancy, belonging in the class, and growth mindset [blind citation]. Building upon the insights from Fall 2022 study, a new cohort of students were consented and asked to respond to the survey in week 1 of the course. Additionally, during the course, right before each midterm, in weeks 4, 7, and 12, students were asked to reflect on their goals for the semester and how they plan to reach their goals. In week 7, students were provided their individual mindset survey results from week 1 and were asked a series of questions about their mindset related to the class average and then asked about changes in their mindset along with questions about how they felt their mindset connected to their performance in the course and how or what aspects of the course has contributed to their mindset changes. Results showed that students felt that their plots from week 1 were representative of their incoming mindsets. Over time, many students perceived the course as increasingly valuable and interesting, fostering a more reflective mindset. Thematic analysis performed on the open-ended responses from the survey resulted in 3 main themes: 1) Positive Mindset and Motivation, 2) Mindset Transformation, and 3) the Role of Mindset in Perseverance. When asked about what elements of the course led to changes in mindset, thematic analysis of the open-ended responses revealed 5 main themes: 1) Engaging and interesting instruction, 2) Supportive instructor and teaching team, 3) Effective communication and explanations, 4) Interactive and participatory learning, and 5) Efficient Grading and Feedback. In summary, the thematic analysis of the quotes emphasizes the importance of good pedagogy in impacting the students' motivation and mindset within a course. It also underscores the value of fostering reflective practices into the course.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".