Abstract
This study examined learning processes in undergraduate online general chemistry courses. The study aimed to characterize learners according to their learning patterns and to identify indicators that predict students' success in an online environment. Specifically, we focused on the role of a central factor affecting success in online courses: self-regulated learning and learner engagement. To this end, we used a mixed methods approach that combines semi-structured interviews and statistical analysis. We applied two logistic regression models and a decision tree algorithm and found two parameters that can predict completion of the course: the submission status of an optional assignment and the students' cumulative video opening pattern (SCOP). Recommendations for institutions and lecturers regarding the benefits of implementing these models to identify self-regulated learning patterns in online courses and to design future effective interventions are discussed. Regarding students, we emphasize the importance of time management and how choices they make with respect to their learning process affect their potential for success.
| Original language | English |
|---|---|
| Article number | 100867 |
| Pages (from-to) | 100867 |
| Number of pages | 1 |
| Journal | Internet and Higher Education |
| Volume | 55 |
| DOIs | |
| State | Published - Oct 2022 |
Bibliographical note
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.Keywords
- Adult learning
- Chemistry education
- Educational data mining
- General chemistry
- Logistic regression
- Online learning
- Self-regulated learning
- Undergraduate students
- Video