Practical early prediction of students' performance using machine learning and eXplainable AI
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Yeonju | - |
dc.contributor.author | Choi, Seongyune | - |
dc.contributor.author | Jung, Heeseok | - |
dc.contributor.author | Kim, Hyeoncheol | - |
dc.date.accessioned | 2022-08-15T10:41:03Z | - |
dc.date.available | 2022-08-15T10:41:03Z | - |
dc.date.created | 2022-08-12 | - |
dc.date.issued | 2022-11 | - |
dc.identifier.issn | 1360-2357 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/143263 | - |
dc.description.abstract | Predicting students' performance in advance could help assist the learning process; if "at-risk" students can be identified early on, educators can provide them with the necessary educational support. Despite this potential advantage, the technology for predicting students' performance has not been widely used in education due to practical limitations. We propose a practical method to predict students' performance in the educational environment using machine learning and explainable artificial intelligence (XAI) techniques. We conducted qualitative research to ascertain the perspectives of educational stakeholders. Twelve people, including educators, parents of K-12 students, and policymakers, participated in a focus group interview. The initial practical features were chosen based on the participants' responses. Then, a final version of the practical features was selected through correlation analysis. In addition, to verify whether at-risk students could be distinguished using the selected features, we experimented with various machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Multi-Layer Perceptron, Support Vector Machine, XGBoost, LightGBM, VTC, and STC. As a result of the experiment, Logistic Regression showed the best overall performance. Finally, information intended to help each student was visually provided using the XAI technique. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | DATA MINING TECHNIQUES | - |
dc.subject | ACADEMIC-PERFORMANCE | - |
dc.subject | INTERACTION PATTERNS | - |
dc.subject | ONLINE | - |
dc.subject | ANALYTICS | - |
dc.subject | OUTCOMES | - |
dc.subject | FAILURE | - |
dc.subject | SUCCESS | - |
dc.subject | TECHNOLOGY | - |
dc.subject | KNOWLEDGE | - |
dc.title | Practical early prediction of students' performance using machine learning and eXplainable AI | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Hyeoncheol | - |
dc.identifier.doi | 10.1007/s10639-022-11120-6 | - |
dc.identifier.scopusid | 2-s2.0-85131700415 | - |
dc.identifier.wosid | 000809293800004 | - |
dc.identifier.bibliographicCitation | EDUCATION AND INFORMATION TECHNOLOGIES, v.27, no.9, pp.12855 - 12889 | - |
dc.relation.isPartOf | EDUCATION AND INFORMATION TECHNOLOGIES | - |
dc.citation.title | EDUCATION AND INFORMATION TECHNOLOGIES | - |
dc.citation.volume | 27 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 12855 | - |
dc.citation.endPage | 12889 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Education & Educational Research | - |
dc.relation.journalWebOfScienceCategory | Education & Educational Research | - |
dc.subject.keywordPlus | DATA MINING TECHNIQUES | - |
dc.subject.keywordPlus | ACADEMIC-PERFORMANCE | - |
dc.subject.keywordPlus | INTERACTION PATTERNS | - |
dc.subject.keywordPlus | ONLINE | - |
dc.subject.keywordPlus | ANALYTICS | - |
dc.subject.keywordPlus | OUTCOMES | - |
dc.subject.keywordPlus | FAILURE | - |
dc.subject.keywordPlus | SUCCESS | - |
dc.subject.keywordPlus | TECHNOLOGY | - |
dc.subject.keywordPlus | KNOWLEDGE | - |
dc.subject.keywordAuthor | Learning performance prediction | - |
dc.subject.keywordAuthor | Early Prediction | - |
dc.subject.keywordAuthor | Artificial intelligence in education | - |
dc.subject.keywordAuthor | Educational data mining | - |
dc.subject.keywordAuthor | Explainable AI in education | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.