A study of educational data mining: evidence from a Thai university

Ruangsak Trakunphutthirak, Yen Ping Cheung, Vincent C.S. Lee

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    2 Citations (Scopus)


    Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students' academic performance. Current data sources used to predict students' performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students' academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk of failure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students' Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.

    Original languageEnglish
    Title of host publicationProceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence
    EditorsPascal Van Hentenryck, Zhi-Hua Zhou
    Place of PublicationPalo Alto CA USA
    PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
    Number of pages8
    ISBN (Electronic)9781577358091
    Publication statusPublished - 2019
    EventAAAI Conference on Artificial Intelligence 2019 - Honolulu, United States of America
    Duration: 27 Jan 20191 Feb 2019
    Conference number: 33rd

    Publication series

    NameProceedings of the AAAI Conference on Artificial Intelligence
    PublisherAAAI Press
    ISSN (Print)2159-5399
    ISSN (Electronic)2374-3468


    ConferenceAAAI Conference on Artificial Intelligence 2019
    Abbreviated titleAAAI 2019
    Country/TerritoryUnited States of America
    Internet address

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