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

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

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

3 Scopus citations

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 (US)
Title of host publicationITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
EditorsPanayiotis Andreou, Michal Armoni, Janet C. Read, Irene Polycarpou
PublisherAssociation for Computing Machinery
Pages364-365
Number of pages2
ISBN (Electronic)9781450357074
DOIs
StatePublished - Jul 2 2018
Externally publishedYes
Event23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018 - Larnaca, Cyprus
Duration: Jul 2 2018Jul 4 2018

Publication series

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

Conference

Conference23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018
CountryCyprus
CityLarnaca
Period7/2/187/4/18

All Science Journal Classification (ASJC) codes

  • Management of Technology and Innovation
  • Education

Keywords

  • Analytics
  • Educational data mining

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    Ihantola, P., Gutica, M., Leinonen, J., Hellas, A., Petersen, A., Hynninen, T., Messom, C., Ajanovski, V. V., Knutas, A., & Liao, S. N. (2018). Taxonomizing features and methods for identifying at-risk students in computing courses. In P. Andreou, M. Armoni, J. C. Read, & I. Polycarpou (Eds.), ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (pp. 364-365). (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE). Association for Computing Machinery. https://doi.org/10.1145/3197091.3205845