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Predicting student performance by modeling participation in asynchronous discussions in university online introductory mathematical courses.

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Title: Predicting student performance by modeling participation in asynchronous discussions in university online introductory mathematical courses.
Authors: Lee, Ji-Eun (AUTHOR), Recker, Mimi (AUTHOR)
Source: Educational Technology Research & Development. Dec2022, Vol. 70 Issue 6, p1993-2015. 23p.
Subjects: Data mining, Virtual universities & colleges, Virtual communities, Learning management system, Multilevel models, Online education
Abstract: This study examines how student and instructor participation in online discussions impacts students' course performance. The context for the study is university introductory online mathematics/statistics courses, which typically have much higher failure rates than their face-to-face counterparts. Using text-mining techniques, we analyze online discussion data automatically collected by a Learning Management System across five years from 2869 students in 72 online courses, who collectively contributed 20,884 posts. These semi-automated techniques enable a broader and more scalable view of participation behaviors by investigating: (1) student posting and non-posting behaviors (called online speaking and listening, respectively), (2) the textual content of posts, and (3) instructors' strategies for structuring discussions. Multilevel modeling results show that online listening behaviors significantly predict students' course performance. Further, students' posts that built on other contributions or applied new knowledge have the highest predictive value in terms of course performance. Finally, the instructors' use of open-ended prompts is the only variable positively and significantly links to students' course performance. Links to theory, instructional practice, and educational data mining are discussed. [ABSTRACT FROM AUTHOR]
Copyright of Educational Technology Research & Development is the property of Springer Nature 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: This study examines how student and instructor participation in online discussions impacts students' course performance. The context for the study is university introductory online mathematics/statistics courses, which typically have much higher failure rates than their face-to-face counterparts. Using text-mining techniques, we analyze online discussion data automatically collected by a Learning Management System across five years from 2869 students in 72 online courses, who collectively contributed 20,884 posts. These semi-automated techniques enable a broader and more scalable view of participation behaviors by investigating: (1) student posting and non-posting behaviors (called online speaking and listening, respectively), (2) the textual content of posts, and (3) instructors' strategies for structuring discussions. Multilevel modeling results show that online listening behaviors significantly predict students' course performance. Further, students' posts that built on other contributions or applied new knowledge have the highest predictive value in terms of course performance. Finally, the instructors' use of open-ended prompts is the only variable positively and significantly links to students' course performance. Links to theory, instructional practice, and educational data mining are discussed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Educational Technology Research & Development is the property of Springer Nature 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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