Using Computer Visualizations to Introduce Grade Five Students to the Particle Nature of Matter
Bibliographic record
Abstract
Secondary school science programs have long included instruction to help students understand the physical world at three interconnected levels—the observable, the particle, and the symbolic (Johnstone, 1993). Elementary school (ages 5-12) science programs, however, emphasize understanding at the observable level only beginning with early childhood explorations of sand and water and progressing to common definitions for the observable properties of solids, liquids, and gases (e.g., liquid flows and takes the shape of the container). Lending support for a focus on the observable is the Common Framework of Science Learning Outcomes K-12 (Council of Ministers of Education, 1997) which expects students ages 10-11 to classify solids, liquids, and gases and identify physical and chemical change all without reference to particles. The National Science Education Standards (National Research Council, 1996) go further by cautioning that for students ages 10-14 it is premature to introduce the particle level as doing so can “distract from the understanding that can be gained from focusing on the observation and description of macroscopic features of substances…at this level…few students can comprehend the idea of atomic and molecular particles” (NRC, 1996, p. 149). These Canadian and American documents reflect a perspective on learning about the physical world that maintains that students must reach a certain developmental level before they have sufficient cognitive capabilities to understand matter at the particle level. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".