Leveraging Student Misconceptions to Improve Teaching of Biochemistry & Cell Biology
Bibliographic record
Abstract
Students come to science class with many ideas of how the natural world works, some of which do not match the consensus of the scientific community and can lead to misunderstandings. Because a growing body of educational research indicates that these misconceptions can serve as resources for learning, we developed a four-point plan to leverage knowledge of common misconceptions to improve classroom teaching by refining instructional focus, providing opportunities for reflective practice, applying evidence-based practices, and promoting exploration of learning theories. By sharing this plan with our teaching colleagues, we were able to foster a collaborative approach to our and others’ practice. To do this, we compiled a resource bank of common student misconceptions using data collected from the University of Toronto’s National Biology Competition, developed a guide for using this misconception resource bank to promote best teaching practices, then shared this plan with our teaching colleagues in order to foster a collaborative approach to our pedagogy. In this article, we present the resource bank and guide and provide teaching tips that can be applied to a wide array of scientific course types and educational levels.
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 | 0.053 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".