@inproceedings{9b663ddb73b64a029c4878e08e04eda5,
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}",
year = "2018",
month = jul,
day = "2",
doi = "10.1145/3197091.3205845",
language = "English (US)",
series = "Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE",
publisher = "Association for Computing Machinery",
pages = "364--365",
editor = "Panayiotis Andreou and Michal Armoni and Read, {Janet C.} and Irene Polycarpou",
booktitle = "ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education",
note = "23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018 ; Conference date: 02-07-2018 Through 04-07-2018",
}