Advances in learning analytics and educational data mining

Mehrnoosh Vahdat, A Ghio, L. Oneto, D. Anguita, M. Funk, G.W.M. Rauterberg

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    24 Citations (Scopus)
    3 Downloads (Pure)

    Abstract

    The growing interest in recent years towards Learning An- alytics (LA) and Educational Data Mining (EDM) has enabled novel ap- proaches and advancements in educational settings. The wide variety of research and practice in this context has enforced important possibilities and applications from adaptation and personalization of Technology En- hanced Learning (TEL) systems to improvement of instructional design and pedagogy choices based on students needs. LA and EDM play an im- portant role in enhancing learning processes by oering innovative methods of development and integration of more personalized, adaptive, and inter- active educational environments. This has motivated the organization of the ESANN 2015 Special Session in Advances in Learning Analytics and Educational Data Mining. Here, a review of research and practice in LA and EDM is presented accompanied by the most central methods, bene- ts, and challenges of the eld. Additionally, this paper covers a review of novel contributions into the Special Session.
    Original languageEnglish
    Title of host publicationESANN 2015 proceedings : European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, 22-24 April 215
    Place of PublicationLeuven
    PublisherKatholieke Universiteit Leuven
    ISBN (Print)978-287587014-8
    Publication statusPublished - 2015

    Fingerprint

    Dive into the research topics of 'Advances in learning analytics and educational data mining'. Together they form a unique fingerprint.

    Cite this