Abstract: Learners differ in many dimensions (e.g., knowledge, problem-solving strategies, motivation, self-regulation skills), and they may change along these dimensions over time. Making online learning tailored to each student to be maximally effective remains a grand challenge. Fortunately, massive online interaction log data (e.g., video watching, problem-solving, forum discussion) are captured, reflecting users' similarities and differences, which can be used to personalize online learning. The data-driven interfaces (e.g., data visualization) could serve as a bridge between AI algorithms and humans, especially learners and educators, to customize their needs. In this talk, I will introduce how to develop AI algorithms to model various learning data and, most importantly, design data-driven interfaces for learners and educators to find insights from learning data and turn them into actions for personalized online learning.

Short Bio: Meng Xia is a postdoctoral fellow from Carnegie Mellon University. Her research probes the intersection of Human-Computer Interaction, Data Visualization, and Education Technology. Her current research focuses on designing human-centered data-driven interfaces for personalized online learning. She published 20+ papers at top conferences, including ACM CHI, IEEE VIS, and ACM CSCW. She received the best paper award from CHI 2022, an honorable mention award from VIS 2022, and the best poster award from VIS 2019. She serves as a program committee member for CHI, VIS, IUI, LAK, etc. Before joining CMU, she received her Ph.D. from the Hong Kong University of Science and Technology, and worked as a postdoc at the Korea Advanced Institute of Science and Technology.