Dr. Yizhou Fan joined the FLoRA project in July 2019 and worked as a post-doc researcher in the School of Informatics at the University of Edinburgh. At the heart of his research program is analytics of self-regulated learning. Specifically, in the FLoRA project, he looked at the novel methodological approaches for the measurement of self-regulated learning through the integration and analysis of multichannel data (e.g., log data, eye-tracking, mouse movements, think-alouds). He then aimed to make use of these data to provide personalized scaffolds and feedback that can be offered to the learners in real-time to enhance their decision making and performance. He published three Journal articles and one conference paper based on the FLoRA project (see below). In early 2023, he joined Peking University as an Assistant Professor and Research Fellow at the Graduate School of Education.
Our team extends our sincere wishes for Dr. Fan’s success in embarking on his new endeavour and we look forward to maintaining a collaborative relationship with him.
Some notable publications authored by Dr. Fan during the FLoRA project include:
[1] Fan, Y., Lim, L., van der Graaf, J., Kilgour, J., Raković, M., Moore, J., … & Gašević, D. (2022). Improving the measurement of self-regulated learning using multi-channel data. Metacognition and Learning, 1-31.
[2] Fan, Y., van der Graaf, J., Lim, L., Raković, M., Singh, S., Kilgour, J., … & Gašević, D. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17(3), 949-987.
[3] Fan, Y., Rakovic, M., van Der Graaf, J., Lim, L., Singh, S., Moore, J., … & Gašević, D. (2023). Towards a fuller picture: Triangulation and integration of the measurement of self‐regulated learning based on trace and think aloud data. Journal of Computer Assisted Learning.
[4] Srivastava, N., Fan, Y., Rakovic, M., Singh, S., Jovanovic, J., Van Der Graaf, J., … & Gasevic, D. (2022, March). Effects of internal and external conditions on strategies of self-regulated learning: A learning analytics study. In LAK22: 12th international learning analytics and knowledge conference (pp. 392-403).