Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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 language | English |
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Title of host publication | Artificial Intelligence in Education |
Subtitle of host publication | 20th International Conference, Proceedings, Part II |
Editors | Seiji Isotani, Eva Millán, Amy Ogan, Bruce McLaren, Peter Hastings, Rose Luckin |
Publisher | Springer Nature |
Pages | 174-178 |
ISBN (Print) | 9783030232061 |
DOIs | |
State | Published - 2019 |
Event | 20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago, United States Duration: 25 Jun 2019 → 29 Jun 2019 |
Name | Lecture Notes in Computer Science |
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Volume | 11626 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference | 20th International Conference on Artificial Intelligence in Education, AIED 2019 |
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Country/Territory | United States |
City | Chicago |
Period | 25/06/19 → 29/06/19 |
ID: 43773654