Learning from What Others Know: Privacy Preserving Cross System Personalization
UM '07 Proceedings of the 11th international conference on User Modeling
A Collaborative Filtering Recommendation Algorithm Based on Item Similarity of User Preference
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
Modelling Learning in an Educational Game
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Microblogging for Language Learning: Using Twitter to Train Communicative and Cultural Competence
ICWL '009 Proceedings of the 8th International Conference on Advances in Web Based Learning
Personalisation of Learning in Virtual Learning Environments
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Searching for "People Like Me" in a Lifelong Learning System
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Modeling and Data Mining in Blogosphere
Modeling and Data Mining in Blogosphere
A teaching model exploiting cognitive conflict driven by a Bayesian network
UM'03 Proceedings of the 9th international conference on User modeling
The adaptive web: methods and strategies of web personalization
The adaptive web: methods and strategies of web personalization
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Deriving knowledge profiles from twitter
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling individual and collaborative problem solving in medical problem-based learning
UM'05 Proceedings of the 10th international conference on User Modeling
Subspace clustering of text documents with feature weighting k-means algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Interweaving public user profiles on the web
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Fuzzy user modeling for adaptation in educational hypermedia
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Taming digital traces for informal learning: a semantic-driven approach
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
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Simulated environments for learning are becoming increasingly popular to support experiential learning in complex domains. A key challenge when designing simulated learning environments is how to align the experience in the simulated world with real world experiences. Social media resources provide user-generated content that is rich in digital traces of real world experiences. People comments, tweets, and blog posts in social spaces can reveal interesting aspects of real world situations or can show what particular group of users is interested in or aware of. This paper examines a systematic way to analyze user-generated content in social media resources to provide useful information for learning simulator design. A hybrid framework exploiting Machine Learning and Semantics for social group profiling is presented. The framework has five stages: (1) Retrieval of user-generated content from the social resource (2) Content noise filtration, removing spam, abuse, and content irrelevant to the learning domain; (3) Deriving individual social profiles for the content authors; (4) Clustering of individuals into groups of similar authors; and (5) Deriving group profiles, where interesting concepts suitable for the use in simulated learning systems are extracted from the aggregated content authored by each group. The framework is applied to derive group profiles by mining user comments on YouTube videos. The application is evaluated in an experimental study within the context of learning interpersonal skills in job interviews. The paper discusses how the YouTube-based group profiles can be used to facilitate the design of a job interview skills learning simulator, considering: (1) identifying learning needs based on digital traces of real world experiences; and (2) augmenting learner models in simulators based on group characteristics derived from social media.