Rafting Together with Lipids ‐ Team Teaching & Active Learning
Bibliographic record
Abstract
Effective oral and written communication skills are key assets for success in and outside of academia. However, students completing undergraduate degrees are often not equipped with these skills. Among the challenges that contribute to this problem is the lack of interest by teachers to conduct in‐class activities that provide students the opportunities to nurture these skills. It can be very time consuming for a single instructor to construct and implement multiple active learning activities, designed to encourage and give students the chance to develop these important communication skills. Additionally, the traditional lecture format hinders development of higher Bloom’s Taxonomy skills and limits opportunities for engagement. In order to overcome this problem, we joined forces with Biochemistry professor, Vanina Zaremberg at the University of Calgary to execute active learning techniques in a fourth year Lipid Biochemistry course. Having multiple instructors allowed us to implement three in‐class activities: construction of concept maps, debate, and guided writing assignment , thoughtfully designed to give students the opportunity to learn and utilize communication and writing skills. Anonymous surveys were conducted following completion of these activities, giving us valuable feedback. The first activity was centered around building concept maps and was designed to tackle the challenge of the varying background knowledge of the students at the beginning of the semester. In short, students drew a concept map on the very first and last day of classes, which not only allowed the instructors to see what information they were bringing into the course, but also allowed the students to connect and organize their knowledge in a thoughtful manner. Rich discussion among instructors on these maps helped to develop lesson plans and outline lesson outcomes. The concept maps conceived on the last day of classes gave an explicit visual of what the students learned throughout the semester. The second activity, the in‐class debate on lipid rafts, allowed students to openly discuss and learn about a key controversial topic in Lipid Biochemistry in a unique way and without the pressure of a typical quiz or assignment. Surprisingly for the instructors, the debate took up the entire 75‐minute lecture. One hundred percent of the responses answered that the debate was an effective way to learn about the topic and students enjoyed learning from their peers. Finally, a guided writing assignment was executed, as it has been reported that students lack experience critiquing published work or a peer’s writing. The writing assignment gave students the opportunity to exercise and develop communication and writing skills while participating in a guided peer‐review process. 79% of the class reported that this was the first time they did a peer‐review exercise, even though most were in their final semester of their undergraduate degree. Overall, the feedback gathered from the survey indicated that students were involved, more confident in the topics, and enjoyed these activities more than typical lectures, suggesting that team teaching enhances student experience and engagement.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".