Yael Feldman-Maggor , Ron Blonder , Inbal Tuvi-Arad
{"title":"Let them choose: Optional assignments and online learning patterns as predictors of success in online general chemistry courses","authors":"Yael Feldman-Maggor , Ron Blonder , Inbal Tuvi-Arad","doi":"10.1016/j.iheduc.2022.100867","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":48186,"journal":{"name":"Internet and Higher Education","volume":null,"pages":null},"PeriodicalIF":6.4000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet and Higher Education","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1096751622000239","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 8
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.
期刊介绍:
The Internet and Higher Education is a quarterly peer-reviewed journal focused on contemporary issues and future trends in online learning, teaching, and administration within post-secondary education. It welcomes contributions from diverse academic disciplines worldwide and provides a platform for theory papers, research studies, critical essays, editorials, reviews, case studies, and social commentary.