Wenjun WU
School of Computer Science and Engineering, Beihang University, CHINA
Research interests:
collective intelligence, educational data mining, big data analytics, microservice and cloud computing
Keynote address:
Human-Machine Artificial Intelligence for Cognitive Modeling and Learning Analytics
Abstract:
The MOOCs have significantly increased scale of online education, bringing a grand challenge on how to achieve both goals of personalized learning and development of practice competency at a large scale. In order to address the challenge, it is necessary to synergize both machine intelligence and human intelligence in an integrated framework to infer the latent state of student learning and provide personalized instructions and assistance for every student in an adaptive way. In this talk, I will summarize the state-of-art of models and algorithms in this emerging area and introduce our study in the deep neural network based cognitive models, reinforcement learning for generating instruction strategies and intelligent peer-grading frameworks. I will also briefly discuss how these models and algorithms can be integrated in an online collaborative learning platform and support educational applications and empirical studies.
Wenjun WU is a Vice Director of State Key Lab of Software Development Environment and a Professor in the School of Computer Science and Engineering at the Beihang University. He was previously a research scientist from 2006 to 2010, at the Computation Institute (CI) at the University of Chicago and Argonne National Laboratory, specializing the development of collaborative science gateways for bioinformatics and social science research. He has published over 120 peer-reviewed research papers, an academic book on software crowdsourcing, and organized multiple international workshops such as IEEE Software Crowdsourcing and Dagstuhl seminar on Crowdsourcing.
