Promoting Deep Learning In First-Year Physiology Through Structured In-class Activities: Building a Bridge for Success
Bibliographic record
Abstract
The transition from high school to university is often a diffcult journey for learners. From data we have collected (unpublished), many students report successful use of surface learning strategies in secondary school, such as highlighting and memorizing their notes or rote recall using flash cards. As such, most students continue using these same techniques in first-year STEM courses1, such as physiology. Furthermore, first-year learners are often unsure how to change their study habits, even after receiving a disappointing midterm grade, as most post-secondary students report that they have not been taught how to study2. This leads to the reinforcement of surface learning, creating a larger knowledge gap as students progress into upper-year courses without having achieved deeper learning in pre-requisite courses. Although many universities offer learning strategy workshops, it has been shown that teaching students these skills within the context of their course work is the most effective3. We hypothesize that modelling deep learning strategies through a variety of weekly hands-on activities will positively influence students’ self-reported study strategies, leading to the incorporation of these active learning techniques in their personal study time. Our first-year physiology course is a pre-requisite for both Kinesiology students and those in the Foods and Nutrition program. Approximately 420 students are enrolled and includes those in science and non-science majors. We have 12 tutorial sections of approximately 40 learners each and 21 weeks of tutorials over the academic year. These tutorials serve as active learning sessions that apply lecture content from the week earlier. Each week, we have designed activities such as concept mapping, case studies, card sorting, problem set worksheets, and Play-Doh modelling, to name a few. Students complete a learning attitude survey after each activity, where quantitative and qualitative data is collected, and two test-your-knowledge exit questions on the content covered by that activity. Students experience each activity in both semester 1 and semester 2, although for different course material. We will present examples of activities used during tutorials as well as preliminary data, which is currently being collected, on the learning attitudes submitted by our students for these activities. We anticipate that as deeper learning strategies are modelled in tutorials, students will be more likely to adapt these techniques as they study. We also anticipate that repeated exposure to the same learning activity in semester 2 will positively influence their attitude toward the usefulness of that tool. Physiology educators have embraced a shift away from didactic lectures to expand the use of problem-based learning in recent years. Our study contributes concrete examples that any educator could implement in their classroom and the associated learning attitudes regarding each activity from a diverse group of students taking their first physiology course. References cited: 1Cook, A. and Leckey, L. (1999) Do expectations meet reality? A survey of changes in first-year student opinion. Journal of Further and Higher Education. 23: 157-171. 2Kornell, K. and Bjork, RA. (2007) The promise and perils of self-regulated learning. Psychonomic Bulletin & Review. 14: 219-224. 3Hattie, J., Biggs, J., and Purdie, N. (1996) Effects of learning skills interventions on student learning, a meta-analysis. The Journal of Experimental Education. 66: 99-136. No support or funding. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".