Further / Prior reading on Enriching Qualitative Video Analysis with Computational Techniques: A Focus on Naturalistic Classroom Settings
Kubsch, M., Krist, C., & Rosenberg, J. M. (2023). Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research. Journal of Research in Science Teaching, 60(2), 423-447.
Example of video: Paul Hur and Nigel Bosch. 2022. Tracking Individuals in Classroom Videos via Post-processing OpenPose Data. In LAK22: 12th International Learning Analytics and Knowledge Conference (LAK22), March 21–25, 2022, Online, USA. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3506860.3506888
Example of audio: Krist, C., Dyer, E. B., Rosenberg, J., Palaguachi, C., & Cox, E. (in press). Leveraging computationally generated descriptions of audio features to enrich qualitative examinations of sustained uncertainty. To be published in the 2023 Proceedings of the International Conference of the Learning Sciences.
Example of text: Rosenberg, J. M., & Krist, C. (2021). Combining machine learning and qualitative methods to elaborate students’ ideas about the generality of their model-based explanations. Journal of Science Education and Technology, 30, 255-267.
Further / Prior reading on Multimodal collaboration analytics: Investigating how group processes influence persistence and well-being in STEM education
Schulten, C., Nolte, A., Spikol, D., & Chounta, I. A. (2022). How do participants collaborate during an online hackathon? An empirical, quantitative study of communication traces. Frontiers in Computer Science, 4, [983164]. https://doi.org/10.3389/fcomp.2022.983164
Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. Journal of Computer Assisted Learning, 34(4), 366-377. https://doi.org/10.1111/jcal.12263
Further / Prior reading on GrAIND-MASER: Generative AI for Non-Data Scientists in MASER
Further / Prior reading on Optimal Learning Moments: Measuring Academic, Social, and Emotional Learning in Daily Life
Schneider, B., Krajcik, J., Lavonen, J., Salmela-Aro, K., Broda, M., Spicer, J., Bruner, J., Moeller, J., Linnansaari, J., Juuti, K., & Viljaranta, J. (2016). Investigating Optimal Learning Moments in U.S. and Finnish Science Classes. Journal of Research in Science Teaching, 53(3), 400–421.
Schneider, B., Krajcik, J., Lavonen, J., & Salmela-Aro, K. (2020). How Learning Science Affects Emotions and Achievement. In Learning Science: The Value of Crafting Engagement in Science Environments (pp.79-94). Yale University Press.
Schneider, B., Chen, I., Bradford, L., & Bartz, K. (2022). Intervention initiatives to raise young people’s interest and participation in STEM. Frontiers in Psychology, 13, 960327.
Schmidt, J. A., Shernoff, D. J., & Csikszentmihalyi, M. (2014). Individual and Situational Factors Related to the Experience of Flow in Adolescence. In M. Csikszentmihalyi (Ed.), Applications of Flow in Human Development and Education: The Collected Works of Mihaly Csikszentmihalyi (pp. 379–405). Springer Netherlands. https://doi.org/10.1007/978-94-017-9094-9_20
Schmidt, J. A., Rosenberg, J. M., & Beymer, P. N. (2018). A person-in-context approach to student engagement in science: Examining learning activities and choice. Journal of Research in Science Teaching, 55(1), 19–43. https://doi.org/10.1002/tea.21409
Vongkulluksn, V. W., & Xie, K. (2022). Multilevel Latent State-Trait Models with Experience Sampling Data: An Illustrative Case of Examining Situational Engagement. Open Education Studies, 4(1), 252-272.
