Analysis of sharable learning processes and action patterns for adaptive learning support

Xiaokang Zhou, Qun Jin

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this study, we focus on the deep analysis of the learning behavior patterns in the task-oriented learning process, which aims to extract and describe the sharable learning processes for adaptive learning support. The LA-Patterns are extracted to represent an individual's learning behavior patterns. Three categories, named Regular Patterns, Successive Patterns, and Frequent Patterns, are classified to describe users' learning patterns with different features, which can be utilized to recommend users with the adaptive learning process as the learning guidance. The experiment and analysis results in a learning management system are discussed finally.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages173-178
    Number of pages6
    Volume8613 LNCS
    ISBN (Print)9783319096346
    DOIs
    Publication statusPublished - 2014
    Event13th International Conference on Advances in Web-Based Learning, ICWL 2014 - Tallinn
    Duration: 2014 Aug 142014 Aug 17

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume8613 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other13th International Conference on Advances in Web-Based Learning, ICWL 2014
    CityTallinn
    Period14/8/1414/8/17

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    Keywords

    • Adaptive Learning
    • Learning Analytics
    • Learning Pattern
    • Sharable Learning Process

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Zhou, X., & Jin, Q. (2014). Analysis of sharable learning processes and action patterns for adaptive learning support. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8613 LNCS, pp. 173-178). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8613 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-09635-3_19