Smartphone Applications as a Catalyst for Active Learning in Chemistry: Investigating the Ideal Gas Law
Bibliographic record
Abstract
This chapter examines how interactive research-based smartphone applications can be used to engage students in hands-on chemistry learning both at school and at home. Two different smartphone applications are being discussed: the PhET (Physics Education Technology, https://phet.colorado.edu/) suite of interactive simulations and the Phyphox (Physics Phone Experiments, https://phyphox.org/) data collection and analysis smartphone application. PhET chemistry simulations let students conduct virtual experiments, while the Phyphox app allows students to collect and analyze real time data. We illustrate the pedagogical applications of these apps through an example of an ideal gas investigation. We also suggest how smartphone applications can be introduced in science teacher education to actively engage future teachers in smartphone enabled science investigations. Finally, we discuss how smartphone-based science experiments can help educators increase access to active science learning for all students and consequently reduce the educational inequality in STEM classrooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".