Automatic classification of error types in solutions to programming assignments at online learning platform

Artyom Lobanov, Timofey Bryksin, Alexey Shpilman

Research outputpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication20th International Conference, Proceedings, Part II
EditorsSeiji Isotani, Eva Millán, Amy Ogan, Bruce McLaren, Peter Hastings, Rose Luckin
PublisherSpringer Nature
Pages174-178
ISBN (Print)9783030232061
DOIs
Publication statusPublished - 2019
Event20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago
Duration: 25 Jun 201929 Jun 2019

Publication series

NameLecture Notes in Computer Science
Volume11626
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Artificial Intelligence in Education, AIED 2019
CountryUnited States
CityChicago
Period25/06/1929/06/19

Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lobanov, A., Bryksin, T., & Shpilman, A. (2019). Automatic classification of error types in solutions to programming assignments at online learning platform. In S. Isotani, E. Millán, A. Ogan, B. McLaren, P. Hastings, & R. Luckin (Eds.), Artificial Intelligence in Education : 20th International Conference, Proceedings, Part II (pp. 174-178). (Lecture Notes in Computer Science ; Vol. 11626 ). Springer Nature. https://doi.org/10.1007/978-3-030-23207-8_33