Taxonomizing features and methods for identifying at-risk students in computing courses

Arto Hellas, Petri Ihantola, Andrew Petersen, Vangel V. Ajanovski, Mirela Gutica, Timo Hynninen, Antti Knutas, Juho Leinonen, Chris Messom, Soohyun Nam Liao

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

    3 Citations (Scopus)

    Abstract

    Since computing education began, we have sought to learn why students struggle in computer science and how to identify these at-risk students as early as possible. Due to the increasing availability of instrumented coding tools in introductory CS courses, the amount of direct observational data of student working patterns has increased significantly in the past decade, leading to a flurry of attempts to identify at-risk students using data mining techniques on code artifacts. The goal of this work is to produce a systematic literature review to describe the breadth of work being done on the identification of at-risk students in computing courses. In addition to the review itself, which will summarize key areas of work being completed in the field, we will present a taxonomy (based on data sources, methods, and contexts) to classify work in the area.
    Original languageEnglish
    Title of host publicationITiCSE’18 Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
    Subtitle of host publicationJuly 2–4, 2018 Larnaca, Cyprus
    EditorsIrene Polycarpou, Janet C. Read, Panayiotis Andreou, Michal Armoni
    Place of PublicationNew York NY USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages364-365
    Number of pages2
    ISBN (Electronic)9781450357074
    DOIs
    Publication statusPublished - 2018
    EventAnnual Conference on Innovation and Technology in Computer Science Education 2018 - Larnaca, Cyprus
    Duration: 2 Jul 20184 Jul 2018
    Conference number: 23rd
    https://iticse.acm.org/ITiCSE2018/

    Publication series

    NameAnnual Conference on Innovation and Technology in Computer Science Education, ITiCSE
    ISSN (Print)1942-647X

    Conference

    ConferenceAnnual Conference on Innovation and Technology in Computer Science Education 2018
    Abbreviated titleITiCSE 2018
    CountryCyprus
    CityLarnaca
    Period2/07/184/07/18
    Internet address

    Keywords

    • Analytics
    • Educational data mining

    Cite this

    Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018). Taxonomizing features and methods for identifying at-risk students in computing courses. In I. Polycarpou, J. C. Read, P. Andreou, & M. Armoni (Eds.), ITiCSE’18 Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education: July 2–4, 2018 Larnaca, Cyprus (pp. 364-365). (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE). Association for Computing Machinery (ACM). https://doi.org/10.1145/3197091.3205845