Workshop Topics
As the rapid development of Massive Online Open Course (MOOC), web 2.0 online communi-ties, social media, and mobile technologies in the big data era, there is a fast proliferation of learning resources such as online learning communities, open course videos, and learning mate-rials (e.g., web pages, animations and documents). Confronting such large volume of learning data, users require an effective and efficiency way to organize and manage them. To achieve this goal, a powerful and versatile user model is essential and critical, which may contain various user information such as learning preferences, plans, pre-knowledge levels and contexts. Such a user model can be exploited and applied in various web-based learning applications like personalized learning paths discovery, learning resource recommendations, course opinions and sentiment analysis.
The International Workshop on User Modeling for Web-based Learning in conjunction with ICWL 2015 will bring together the academia, researchers, and industrial practitioners from computer science, information systems, education, psychology and behavior science discipline, and provide a forum for recent advances in the field of user modeling, data mining, social com-puting and big data analytics, from the perspectives of web-based learning.
Topics of interest include, but are not limited to the exploitation of user modeling for web-based learning, the identification of semantics underlying large volume of user data for user modeling and efficient algorithms for e-learning data management, and the applications of user modeling for web-based learning in research fields related to (but not limited):
User and learning resource modeling
User and learning resource pattern mining
User profiling and personalization
User log mining and analytics
Learning resource semantics extractions
Learning resources recommendation and search
Index and management of learning resources
Ontology mining and modeling for learning users
Context modeling for users
Social computing and analysis for users
Data management on learning resources
Sentiment mining for user review
Opinion mining on learning resources
Cognitive-based user modeling
Learning style and methodology modeling
Learning assessments modeling
User modeling for domain-specific applications (e.g., language learning, mathematics education)
Paper Submission
We welcome the following types of submissions:
Long paper (max. 10 pages)
Short paper (max. 4 pages)
Poster (max. 2 pages)
Paper Reviewing and Selection Process
Each paper will be reviewed by at least two reviewers from the workshop chairs and program committees. Reviewers are selected based on whether they are familiar with topics of the paper or not and whether they have conflict of interests with authors or not. The reviewers all write full reviews that will be later returned to the authors ano-nymously. In unusual cases, such as when an external reviewer fails to deliver a re-view on time, PC chairs will invite other reviewers who have sufficient expertise but are not in the program committee members. In those cases, at least two PC chairs will examine the review comments to ensure the quality of the review. Accepted papers will be selected based on the mean scores by reviewers.

The paper submission webiste: https://cmt.research.microsoft.com/IWUM2015/