Conceptual understanding of osmosis and diffusion by Australian first-year biology students
Bibliographic record
Abstract
Osmosis and diffusion are essential foundation concepts for first-year biology students as they are a key to understanding much of the biology curriculum. However, mastering these concepts can be challenging due to their interdisciplinary and abstract nature. Even at their simplest level, osmosis and diffusion require the learner to imagine processes they cannot see. In addition, many students begin university with flawed beliefs about these two concepts which will impede learning in related areas. The aim of this study was to explore misconceptions around osmosis and diffusion held by first-year cell biology students at an Australian regional university. The 18-item Osmosis and Diffusion Conceptual Assessment was completed by 767 students. From the results, four key misconceptions were identified: approximately half of the participants believed dissolved substances will eventually settle out of a solution; approximately one quarter thought that water will always reach equal levels; one quarter believed that all things expand and contract with temperature; and nearly one third of students believed molecules only move with the addition of external force. Greater attention to identifying and rectifying common misconceptions when teaching first-year students will improve their conceptual understanding of these concepts and benefit their learning in subsequent science subjects.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".