STUDENT EXPERIENCES IN LABORATORY PROGRAMS ACROSS THREE UNIVERSITIES: A SNAPSHOT DURING COVID-19 SEMESTER
Notice bibliographique
Résumé
This project focuses on the novel idea of integration of technologies with inquiry skills and modelling (Crook & Sharma, 2013; Gilbert, 2004; Gordon et al., 2019) and associates these with students’ cognitive engagement, behavioural engagement and emotional engagement (Muller, Sharma & Reimann, 2008; Kota, Cornish & Sharma, 2019; Cornish et al., 2019). We use a survey (Barrie et al., 2015) that measures student experiences in laboratories: how technology was integrated, how much inquiry skills are developed, and how well the students understand the modelling. While the survey is designed for hands-on labs, the emergence of COVID-19 pandemic at the start of the 2020 semester has necessitated for undergraduate lab programs to shift to online delivery mode. This has created an interesting opportunity to use this survey for measuring the impact of online labs on student engagement, and we have obtained modified ethics approval for that. The data is being collected in the final weeks of semester 1 from the first-year undergraduate students enrolled in physics units at the University of Melbourne, the University of Sydney and Monash University. Two types of responses to the engagement in labs will be explored: (1) from students who experienced some face to face labs at the start, then moved to online labs; and (2) from students who only had online labs. It would be interesting to see which aspects of the laboratory practice have retained their original learning intention, and which aspects have been affected by the online delivery, and to what extent. 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. Gordon, T., Georgiou, H., Cornish, S., & Sharma, M. (2019). Science in your pocket: Leaving high school students to their own 'devices' while designing an inquiry-based investigation. Teaching Science, 65(1), 17. 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 ».