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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

ISBN: 978-989-758-179-3

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)

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Paper citation in several formats:
Pu, X.; Chatti, M.; Thüs, H. and Schroeder, U. (2016). Wiki-LDA: A Mixed-Method Approach for Effective Interest Mining on Twitter Data.In Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-179-3, pages 426-433. DOI: 10.5220/0005861504260433

@conference{csedu16,
author={Xiao Pu. and Mohamed Amine Chatti. and Hendrik Thüs. and Ulrik Schroeder.},
title={Wiki-LDA: A Mixed-Method Approach for Effective Interest Mining on Twitter Data},
booktitle={Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2016},
pages={426-433},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005861504260433},
isbn={978-989-758-179-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Wiki-LDA: A Mixed-Method Approach for Effective Interest Mining on Twitter Data
SN - 978-989-758-179-3
AU - Pu, X.
AU - Chatti, M.
AU - Thüs, H.
AU - Schroeder, U.
PY - 2016
SP - 426
EP - 433
DO - 10.5220/0005861504260433

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