MODELLING WAVES: INTEGRATING TECHNOLOGY WITH MODELLING AND INQUIRY IN AN UNDERGRADUATE PHYSICS EXPERIMENT
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».