Let them choose: Optional assignments and online learning patterns as predictors of success in online general chemistry courses

Yael Feldman-Maggor, Ron Blonder, Inbal Tuvi-Arad

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Article number100867
Pages (from-to)100867
Number of pages1
JournalInternet and Higher Education
StatePublished - Oct 2022

Bibliographical note

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  • Adult learning
  • Chemistry education
  • Educational data mining
  • General chemistry
  • Logistic regression
  • Online learning
  • Self-regulated learning
  • Undergraduate students
  • Video


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