The Teaching and Learning of Diffusion and Osmosis: What Can We Learn from Analysis of Classroom Practices? A Case Study
Bibliographic record
Abstract
Background:The objective of this study is to describe the way in which two important biological phenomena, namely diffusion and osmosis, are addressed in the classroom. The study builds on extensive research conducted over the past twenty years showing that students’ appropriation of these two phenomena remains partial and incomplete.Material and methods:Using a case study (a course made up of eight periods), we collected data in three stages: interviews with the teacher regarding his planning; a video recording of the entire course; and feedback interviews with the teacher subsequent to the course.Results:The study’s results show that the difficulties encountered by the students cannot be attributed solely to their personal characteristics (state of development of the scientific mindset, prior learning, etc.). Instead, they appear to be largely associated with teaching practices and the potential these practices hold in terms of allowing students to appropriate these concepts.Conclusions:The results presented in this article are significant in their contribution to improving teaching methods for diffusion and osmosis, and thus to facilitating their understanding by students.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".