The MOOCs (Massive Open Online Courses) have significantly increased scale of
online education, bringing great opportunities to understand learner behaviours and
design intelligent personalized and collaborative learning systems in an unprecedented
way. Large datasets collected from online learning platforms enable researchers from
data science and learning science as well as educators to work together to answer
educational questions in the learning process and improve the overall quality of
education. To this end, educational data mining and learning analytics are emerging as
interdisciplinary research fields that attracts lots of research attention from both
academia and industry.
The DMMOOC 2017 workshop addresses issues of data management and mining in a
wide range of MOOC related scenarios. The goal of the workshop is to provide a
platform for researchers, educators and practitioner from academia and industry to
present their latest progress and discovery from the perspective of diverse MOOCs
related applications. We intend this workshop to act as a place where people from
different disciplines can find a platform to discuss issues of data management and
mining in both conventional and emerging MOOCs related scenarios. Potential
participants may come from research communities such as data management, data
mining, education science, machine learning, information retrieval or any other areas
related to the MOOCs.

Workshop Topics (include but not limited to):
Automated Construction and Optimization of MOOCs Knowledge Graph
Automatic Feedback and Peer Grading on MOOCs
Big Data and Learning Analytics
Data-driven Crowdsourcing on MOOCs
Data Mining and Learning Metrics
Data Mining in Social and Collaborative Learning
Deriving Representations of Domain Knowledge from MOOCs Data
Intelligent Tutoring
Personalized and Adaptive Learning
Text mining and Semantic Analysis on MOOCs
User Analytics on MOOCs