Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
Automatic classification of error types in solutions to programming assignments at online learning platform. / Lobanov, Artyom; Bryksin, Timofey; Shpilman, Alexey.
Artificial Intelligence in Education : 20th International Conference, Proceedings, Part II. ред. / Seiji Isotani; Eva Millán; Amy Ogan; Bruce McLaren; Peter Hastings; Rose Luckin. Springer Nature, 2019. стр. 174-178 (Lecture Notes in Computer Science ; Том 11626 ).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
}
TY - GEN
T1 - Automatic classification of error types in solutions to programming assignments at online learning platform
AU - Lobanov, Artyom
AU - Bryksin, Timofey
AU - Shpilman, Alexey
PY - 2019
Y1 - 2019
N2 - Online programming courses are becoming more and more popular, but they still have significant drawbacks when compared to the traditional education system, e.g., the lack of feedback. In this study, we apply machine learning methods to improve the feedback of automated verification systems for programming assignments. We propose an approach that provides an insight on how to fix the code for a given incorrect submission. To achieve this, we detect frequent error types by clustering previously submitted incorrect solutions, label these clusters and use this labeled dataset to identify the type of an error in a new submission. We examine and compare several approaches to the detection of frequent error types and to the assignment of clusters to new submissions. The proposed method is evaluated on a dataset provided by a popular online learning platform.
AB - Online programming courses are becoming more and more popular, but they still have significant drawbacks when compared to the traditional education system, e.g., the lack of feedback. In this study, we apply machine learning methods to improve the feedback of automated verification systems for programming assignments. We propose an approach that provides an insight on how to fix the code for a given incorrect submission. To achieve this, we detect frequent error types by clustering previously submitted incorrect solutions, label these clusters and use this labeled dataset to identify the type of an error in a new submission. We examine and compare several approaches to the detection of frequent error types and to the assignment of clusters to new submissions. The proposed method is evaluated on a dataset provided by a popular online learning platform.
KW - Automatic evaluation
KW - Classification
KW - Clustering
KW - MOOC
KW - Programming
UR - http://www.scopus.com/inward/record.url?scp=85068320560&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-23207-8_33
DO - 10.1007/978-3-030-23207-8_33
M3 - Conference contribution
AN - SCOPUS:85068320560
SN - 9783030232061
T3 - Lecture Notes in Computer Science
SP - 174
EP - 178
BT - Artificial Intelligence in Education
A2 - Isotani, Seiji
A2 - Millán, Eva
A2 - Ogan, Amy
A2 - McLaren, Bruce
A2 - Hastings, Peter
A2 - Luckin, Rose
PB - Springer Nature
T2 - 20th International Conference on Artificial Intelligence in Education, AIED 2019
Y2 - 25 June 2019 through 29 June 2019
ER -
ID: 43773654