Session Chair:
Li ZHENG,
Tsinghua University, China
Session Co-Chair:
Chao LI,
Tsinghua University, China
Email:
zhengli at
tsinghua.edu.cn, li-chao at
tsinghua.edu.cn
Nowadays, big data play a more and more important role in lots
of areas. The emergence of Massive Open Online Courses (MOOCs)
and the accompanying huge shift in thinking about education,
such as flipped classroom and blended learning, has also
inspired lots of research and applications. The Big data sources
in online education are generated from blogging, chats, micro
blogging ( or twitter), web and mobile platforms, wearable
computing or immersive learning environments, intelligent
agents, online discussion forums, shared workspaces, social
networking media, whiteboards, wikis, and distance face-to-face
interaction systems, etc. And the related research and
applications are spread from analytic approaches and theories to
measures and supporting tools.
This session is intended to provide a forum for researchers and
engineers to present their latest innovations and share their
experiences in big data technologies and their applications in
online education areas. Topics are included but not limited to:
● Analytic approaches: algorithms, architectures, behavior
modeling, clustering, data integration, data mining, research
about design, evaluation methods, information visualization,
knowledge representation, machine learning, natural language
processing, predictive analytics, recommendation engines,
sequential analysis, social network analysis, statistical
analysis, etc.
● Theories and Concepts: activity theory, actor-network theory,
learning sciences, conceptual models of learning enabled by
analytics, distributed cognition, networked individualism,
reflective learning, social learning, etc.
● Measures of education: accreditation, emotions, attendance and
retention (as predictors of learning), attention, attitudes,
collaboration and cooperation, community structure, degree of
competence, educational performance, expectations, learner
behavior modeling, learning dispositions, motivation,
participation, satisfaction, etc.
● Analytic tools for: collaborative learning, course management
systems, decision-support systems for learning, instructor
support, intelligent tutoring systems, learning communities,
learning environments enhanced with analytics, mentoring,
student monitoring, teacher analytics, teaching learning
analytics, etc.
