Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/37208
Title: | The good, the bad, and the ugly: mining for patterns in student source code | Authors: | Mens, Kim Nijssen, Siegfried PHAM, Hoàng Son |
Issue Date: | 2021 | Publisher: | ASSOC COMPUTING MACHINERY | Source: | EASEAI '21: PROCEEDINGS OF THE 3RD INTERNATIONAL WORKSHOP ON EDUCATION, THROUGH ADVANCED SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE, p. 1 -8 | Abstract: | Research on source code mining has been explored to discover interesting structural regularities, API usage patterns, refactoring opportunities, bugs, crosscutting concerns, code clones and systematic changes. In this paper we present a pattern mining algorithm that uses frequent tree mining to mine for interesting good, bad or ugly coding idioms made by undergraduate students taking an introductory programming course. We do so by looking for patterns that distinguish positive examples, corresponding to the more correct answers to a question, from negative examples, corresponding to solutions that failed the question. We report promising initial results of this algorithm applied to the source code of over 500 students. Even though more work is needed to fine-tune and validate the algorithm further, we hope that it can lead to interesting insights that can eventually be integrated into an intelligent recommendation system to help students learn from their errors. | Notes: | Mens, K (corresponding author), UCLouvain, ICTEAM Inst, Louvain La Neuve, Belgium. kim.mens@uclouvain.be; siegfried.nijssen@uclouvain.be; hoangson.pham@uhasselt.be |
Keywords: | Pattern mining; source code mining; CS education; programming | Document URI: | http://hdl.handle.net/1942/37208 | ISBN: | 978-1-4503-8624-1 | DOI: | 10.1145/3472673.3473958 | ISI #: | WOS:000773035600001 | Rights: | © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-8624-1/21/08. . . $15.00 | Category: | C1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
3472673.3473958.pdf Restricted Access | Published version | 807.6 kB | Adobe PDF | View/Open Request a copy |
WEB OF SCIENCETM
Citations
2
checked on Jul 21, 2024
Page view(s)
24
checked on Sep 6, 2022
Download(s)
10
checked on Sep 6, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.