TY - GEN
T1 - Taxonomizing features and methods for identifying at-risk students in computing courses
AU - Ihantola, Petri
AU - Gutica, Mirela
AU - Leinonen, Juho
AU - Hellas, Arto
AU - Petersen, Andrew
AU - Hynninen, Timo
AU - Messom, Chris
AU - Ajanovski, Vangel V.
AU - Knutas, Antti
AU - Liao, Soohyun Nam
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/7/2
Y1 - 2018/7/2
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.
KW - Analytics
KW - Educational data mining
UR - http://www.scopus.com/inward/record.url?scp=85052019931&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052019931&partnerID=8YFLogxK
U2 - 10.1145/3197091.3205845
DO - 10.1145/3197091.3205845
M3 - Conference contribution
AN - SCOPUS:85052019931
T3 - Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE
SP - 364
EP - 365
BT - ITiCSE 2018 - Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
A2 - Andreou, Panayiotis
A2 - Armoni, Michal
A2 - Read, Janet C.
A2 - Polycarpou, Irene
PB - Association for Computing Machinery
T2 - 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018
Y2 - 2 July 2018 through 4 July 2018
ER -