MODELLING WAVES: INTEGRATING TECHNOLOGY WITH MODELLING AND INQUIRY IN AN UNDERGRADUATE PHYSICS EXPERIMENT
Bibliographic record
Abstract
This project focuses on the novel idea of integration of technologies with inquiry skills and modelling (Crook & Sharma, 2013; Cornish et al., 2019; Gilbert, 2004) and associates these with students’ cognitive engagement, behavioural engagement and emotional engagement (Muller, Sharma & Reimann, 2008; Kota, Cornish & Sharma, 2019). Using design-based research methodology, we integrated technology and inquiry to design an experiment on ‘modelling waves on a rope’, a standard topic in first-year undergraduate physics. Furthermore, we investigated how students engaged with the new experiment? It had three features; (1) qualitative description and kinaesthetic feel of waves being created on ropes, (2) taking measurements using video analysis software, and (3) a whole class comparison of experimental and theoretical values using a pre-designed EXCEL spreadsheet. The experiment was trialled in two tutor training sessions, and the final version was implemented in first year physics labs in 2018 and 2019. We used a survey (Barrie et al., 2015) that measures student experiences in labs by evaluating: how technology was integrated, how much inquiry skills are developed, and how well the students understand the modelling. We also collected observational notes and student logbooks and conducted interviews. Tutors were also surveyed. The sample size includes 406 students and 24 tutors. Findings show that students engaged in a hands-on experiment by creating waves on a rope, in using technology for data analysis and in developing ICT skills, and in understanding modelling using EXCEL spreadsheets. The experiment also fostered teamwork and required investment of an appropriate level of mental effort demonstrating that the experiment did engage students in a meaningful manner. The integration of digital technologies with ‘modelling waves on a rope’ resulted in higher overall enjoyment of the experiment and increased student engagement. REFERENCES Barrie, S. C., Bucat, R. B., Buntine, M. A., Burke da Silva, K., Crisp, G. T., George, A. V., & Yeung, A. (2015). Development, evaluation and use of a student experience survey in undergraduate science laboratories: The Advancing Science by Enhancing Learning in the Laboratory Student Laboratory Learning Experience Survey. International Journal of Science Education, 37(11), 1795-1814. Cornish, S., Yeung, A., Kable, S. H., Orgill, M., & Sharma, M. D. (2019). Using teacher voices to develop the ASELL Schools professional development workshops. Teaching Science, 65(1), 4. Crook, S. J. & Sharma, M. D. (2013). Bloom-ing heck! The activities of Australian science teachers and students two years into a 1:1 laptop program across 14 high schools. International Journal of Innovation in Science and Mathematics Education, 21(1), 54-69. Gilbert, J. K. (2004). Models and modelling: Routes to more authentic science education. International Journal of Science and Mathematics Education, 2(2), 115–130. Kota, S. D., Cornish, S, & Sharma, M. D. (2019); Switched on! Student and teacher engagement in an electricity practical, Physics Education, 54(1), 1-9. Muller, D. A., Sharma, M. D., & Reimann P 2008 Raising cognitive load with linear multimedia to promote conceptual change, Science Education, 92, 278–296.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".