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

doi:10.1145/3197091.3205845

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
Editors	Panayiotis Andreou, Michal Armoni, Janet C. Read, Irene Polycarpou
Publisher	Association for Computing Machinery
Pages	364-365
Number of pages	2
ISBN (Electronic)	9781450357074
DOIs	https://doi.org/10.1145/3197091.3205845
State	Published - Jul 2 2018
Event	23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018 - Larnaca, Cyprus Duration: Jul 2 2018 → Jul 4 2018

Publication series

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

Conference

Conference	23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018
Country/Territory	Cyprus
City	Larnaca
Period	7/2/18 → 7/4/18

All Science Journal Classification (ASJC) codes

Management of Technology and Innovation
Education

Keywords

Analytics
Educational data mining

Access to Document

10.1145/3197091.3205845

Cite this

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

Ihantola, Petri ; Gutica, Mirela ; Leinonen, Juho et al. / Taxonomizing features and methods for identifying at-risk students in computing courses. ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. editor / Panayiotis Andreou ; Michal Armoni ; Janet C. Read ; Irene Polycarpou. Association for Computing Machinery, 2018. pp. 364-365 (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE).

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title = "Taxonomizing features and methods for identifying at-risk students in computing courses",

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.",

keywords = "Analytics, Educational data mining",

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

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month = jul,

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doi = "10.1145/3197091.3205845",

language = "English (US)",

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Ihantola, P, Gutica, M, Leinonen, J, Hellas, A, Petersen, A, Hynninen, T, Messom, C, Ajanovski, VV, Knutas, A & Liao, SN 2018, Taxonomizing features and methods for identifying at-risk students in computing courses. in P Andreou, M Armoni, JC Read & I Polycarpou (eds), ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, Association for Computing Machinery, pp. 364-365, 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018, Larnaca, Cyprus, 7/2/18. https://doi.org/10.1145/3197091.3205845

Taxonomizing features and methods for identifying at-risk students in computing courses. / Ihantola, Petri; Gutica, Mirela; Leinonen, Juho et al.
ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. ed. / Panayiotis Andreou; Michal Armoni; Janet C. Read; Irene Polycarpou. Association for Computing Machinery, 2018. p. 364-365 (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Ihantola, Petri

AU - Gutica, Mirela

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AU - Hellas, Arto

AU - Petersen, Andrew

AU - Hynninen, Timo

AU - Messom, Chris

AU - Ajanovski, Vangel V.

AU - Knutas, Antti

AU - Liao, Soohyun Nam

PY - 2018/7/2

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N2 - 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.

AB - 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.

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Y2 - 2 July 2018 through 4 July 2018

ER -

Ihantola P, Gutica M, Leinonen J, Hellas A, Petersen A, Hynninen T et al. Taxonomizing features and methods for identifying at-risk students in computing courses. In Andreou P, Armoni M, Read JC, Polycarpou I, editors, ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery. 2018. p. 364-365. (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE). doi: 10.1145/3197091.3205845

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

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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