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.

本研究考察了本科在线普通化学课程的学习过程。该研究旨在根据学习者的学习模式来描述他们的特征，并确定预测学生在在线环境中成功的指标。具体来说，我们专注于影响在线课程成功的核心因素的作用：自我调节学习和学习者参与度。为此，我们使用了一种结合了半结构化访谈和统计分析的混合方法。我们应用了两个逻辑回归模型和一个决策树算法，发现了两个可以预测课程完成的参数：可选作业的提交状态和学生的累积视频打开模式 (SCOP)。讨论了对机构和讲师关于实施这些模型的好处的建议，以识别在线课程中的自我调节学习模式并设计未来的有效干预措施。对于学生，我们强调时间管理的重要性，以及他们在学习过程中所做的选择如何影响他们的成功潜力。