Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Tübingen, Germany.
School of Education, University of California, Irvine, California, United States of America.
PLoS One. 2022 Nov 3;17(11):e0276839. doi: 10.1371/journal.pone.0276839. eCollection 2022.
The importance of online learning in higher education settings is growing, not only in wake of the Covid-19 pandemic. Therefore, metrics to evaluate and increase the quality of online instruction are crucial for improving student learning. Whereas instructional quality is traditionally evaluated with course observations or student evaluations, course syllabi offer a novel approach to predict course quality even prior to the first day of classes. This study develops an online course design characteristics rubric for science course syllabi. Utilizing content analysis, inductive coding, and deductive coding, we established four broad high-quality course design categories: course organization, course objectives and alignment, interpersonal interactions, and technology. Additionally, this study exploratively applied the rubric on 11 online course syllabi (N = 635 students) and found that these design categories explained variation in student performance.
在线学习在高等教育环境中的重要性日益增加，这不仅是因为新冠疫情的爆发。因此，评估和提高在线教学质量的指标对于提高学生的学习效果至关重要。虽然教学质量通常是通过课程观察或学生评估来进行评估的，但课程教学大纲提供了一种在上课第一天之前预测课程质量的新方法。本研究为理科课程教学大纲制定了在线课程设计特征量表。我们利用内容分析、归纳编码和演绎编码，确定了四个广泛的高质量课程设计类别：课程组织、课程目标和一致性、人际互动和技术。此外，本研究还探索性地将该量表应用于 11 份在线课程教学大纲（N=635 名学生），并发现这些设计类别可以解释学生表现的差异。
