Authors:
Xiao Pu
1
;
Mohamed Amine Chatti
2
;
Hendrik Thüs
2
and
Ulrik Schroeder
2
Affiliations:
1
École Polytechnique Fédérale de Lausanne and Idiap Research Institute, Switzerland
;
2
RWTH Aachen University, Germany
Keyword(s):
Learning Analytics, Educational Data Mining, Personalization, Adaptation, Learner Modelling, Interest Mining, Topic Modelling, Twitter.
Related
Ontology
Subjects/Areas/Topics:
Computer-Supported Education
;
Domain Applications and Case Studies
;
Information Technologies Supporting Learning
;
Intelligent Learning and Teaching Systems
;
Learning Analytics
;
Social Context and Learning Environments
;
Web 2.0 and Social Computing for Learning and Knowledge Sharing
Abstract:
Learning analytics (LA) and Educational data mining (EDM) have emerged as promising technology enhanced
learning (TEL) research areas in recent years. Both areas deal with the development of methods
that harness educational data sets to support the learning process. A key area of application for LA and EDM
is learner modelling. Learner modelling enables to achieve adaptive and personalized learning environments,
which are able to take into account the heterogeneous needs of learners and provide them with tailored learning
experience suited for their unique needs. As learning is increasingly happening in open and distributed
environments beyond the classroom and access to information in these environments is mostly interest-driven,
learner interests need to constitute an important learner feature to be modeled. In this paper, we focus on
the interest dimension of a learner model and present Wiki-LDA as a novel method to effectively mine user’s
interests in Twitter. We apply a
mixed-method approach that combines Latent Dirichlet Allocation (LDA),
text mining APIs, and wikipedia categories. Wiki-LDA has proven effective at the task of interest mining and
classification on Twitter data, outperforming standard LDA.
(More)