Teaching and Learning Research Center, University of California, Irvine, CA 92697.
Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697.
CBE Life Sci Educ. 2021 Mar;20(1):ar3. doi: 10.1187/cbe.20-04-0077.
The Classroom Observation Protocol for Undergraduate STEM (COPUS) provides descriptive feedback to instructors by capturing student and instructor behaviors occurring in the classroom. Due to the increasing prevalence of COPUS data collection, it is important to recognize how researchers determine whether groups of courses or instructors have unique classroom characteristics. One approach uses cluster analysis, highlighted by a recently developed tool, the COPUS Analyzer, that enables the characterization of COPUS data into one of seven clusters representing three groups of instructional styles (didactic, interactive, and student centered). Here, we examine a novel 250 course data set and present evidence that a predictive cluster analysis tool may not be appropriate for analyzing COPUS data. We perform a de novo cluster analysis and compare results with the COPUS Analyzer output and identify several contrasting outcomes regarding course characterizations. Additionally, we present two ensemble clustering algorithms: 1) -means and 2) partitioning around medoids. Both ensemble algorithms categorize our classroom observation data into one of two clusters: traditional lecture or active learning. Finally, we discuss implications of these findings for education research studies that leverage COPUS data.
本科阶段 STEM 课堂观察协议（COPUS）通过捕捉课堂中发生的学生和教师行为，为教师提供描述性反馈。由于 COPUS 数据收集的日益普及，重要的是要认识到研究人员如何确定课程组或教师是否具有独特的课堂特征。一种方法是使用聚类分析，最近开发的 COPUS 分析工具突出了这一点，该工具能够将 COPUS 数据特征化为代表三种教学风格（讲授式、互动式和以学生为中心式）的七个聚类之一。在这里，我们检查了一个新的 250 门课程数据集，并提供了证据表明，预测聚类分析工具可能不适合分析 COPUS 数据。我们进行了从头开始的聚类分析，并将结果与 COPUS 分析器的输出进行了比较，确定了关于课程特征的几个对比结果。此外，我们还提出了两种集成聚类算法：1）-means 和 2）基于中位数的划分。这两种集成算法都将我们的课堂观察数据分为传统讲座或主动学习两种类型。最后，我们讨论了这些发现对利用 COPUS 数据的教育研究的影响。
