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

Hellas, Arto (author)
Ihantola, Petri (author)
Petersen, Andrew (author)
Ajanovski, Vangel V. (author)
Gutica, Mirela (Mirela Gutica (Mirela_Gutica)) (author)
(author)
(editor)
(funder)
(translator)
Hynninen, Timo (author)
Knutas, Antti (author)
Leinonen, Juho (author)
Messom, Chris (author)
Liao, Soohyun Nam (author)

© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Proceedings from the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. 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.

2018-07-02

Peer reviewed

https://dl.acm.org/citation.cfm?doid=3197091.3205845