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Predicting adult students' online learning persistence: A case study in South Korea using random forest analysis.

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Title: Predicting adult students' online learning persistence: A case study in South Korea using random forest analysis.
Authors: Nam, Na-Ra1 (AUTHOR), Song, Sue-Yeon2 (AUTHOR) suesong@cha.ac.kr
Source: Innovations in Education & Teaching International. Feb2025, Vol. 62 Issue 1, p152-168. 17p.
Subject Terms: *Student engagement, *School attendance, *Instructional systems, *School dropout prevention, Random forest algorithms
Abstract: This empirical study uses a random forest algorithm to examine the factors that influence learners' persistence in online learning at a prominent Korean institution. The data were collected from students who began their studies in Spring 2021, and encompassed a range of variables including individual attributes, academic engagement, academic achievement, course status, and satisfaction with the institution. The study identified several key predictors of student retention, including academic achievement and variables related to academic engagement, such as students' learning time, course completion rate, and number of logins to the online learning system. Students' number of submitted mid-term assignments and attendance at face-to-face classes also emerged as significant factors related to persistence. The predictive model utilised in this study can provide valuable insight, indicating when a learner is at risk of dropping out and thus enabling timely interventions that promote academic persistence and student success. [ABSTRACT FROM AUTHOR]
Copyright of Innovations in Education & Teaching International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Predicting adult students' online learning persistence: A case study in South Korea using random forest analysis.
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  Data: <searchLink fieldCode="AR" term="%22Nam%2C+Na-Ra%22">Nam, Na-Ra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Sue-Yeon%22">Song, Sue-Yeon</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> suesong@cha.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22Innovations+in+Education+%26+Teaching+International%22">Innovations in Education & Teaching International</searchLink>. Feb2025, Vol. 62 Issue 1, p152-168. 17p.
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  Data: *<searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink><br />*<searchLink fieldCode="DE" term="%22School+attendance%22">School attendance</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br />*<searchLink fieldCode="DE" term="%22School+dropout+prevention%22">School dropout prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink>
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  Data: This empirical study uses a random forest algorithm to examine the factors that influence learners' persistence in online learning at a prominent Korean institution. The data were collected from students who began their studies in Spring 2021, and encompassed a range of variables including individual attributes, academic engagement, academic achievement, course status, and satisfaction with the institution. The study identified several key predictors of student retention, including academic achievement and variables related to academic engagement, such as students' learning time, course completion rate, and number of logins to the online learning system. Students' number of submitted mid-term assignments and attendance at face-to-face classes also emerged as significant factors related to persistence. The predictive model utilised in this study can provide valuable insight, indicating when a learner is at risk of dropping out and thus enabling timely interventions that promote academic persistence and student success. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Innovations in Education & Teaching International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/14703297.2024.2305939
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        Text: English
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      – SubjectFull: School dropout prevention
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