The 2nd International Workshop on
User Modeling for Web-based Learning
(IWUM)
With the rapid development of Massive Online Open Course (MOOC), web 2.0 online communities, social media, and mobile technologies in the big data era, there has been fast proliferation of learning resources such as online learning communities, open course videos, and learning materials (e.g., web pages, animations and documents). Such a large volume of learning data requires users to build up effective skills of information organization and management. To achieve this goal, it is essential to develop powerful and versatile user model, containing various user information such as learning preferences, styles, backgrounds, pre-knowledge and contexts. This user model can be exploited and applied to a range of web-based learning applications such as 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 SETE 2016 will bring together the academia, researchers, and industrial practitioners from computer science, information systems, education, psychology and behavior science disciplines, and provide a forum for recent advances in the field of user modeling, data mining, social computing 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 the following research fields:
- 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 analysis 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)
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 there is a conflict of interests with authors or not. The reviewers write full reviews that will be later returned to the authors anonymously. In unusual cases, such as when an external reviewer fails to deliver a review on time, PC chairs will invite other reviewers who have sufficient expertise but are not among 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.
Please submit your paper here.
