The purpose of this study was to understand the complex interactions within a course among motivational climate variables and student evaluations of teaching (SETs) by developing online simulators using probabilistic machine learning. We used data from 2938 undergraduate students in 30 classes to create online simulators based on Bayesian Belief Networks. We created bubble charts, line graphs, and radar charts to show the relationships between the study variables. Findings showed that (a) the motivational climate variables—as measured by the MUSIC Model of Motivation variables—are the largest predictors of SETs, (b) student interest (in the coursework and instructional methods) is the overall largest predictor of SETs, (c) the relationships between the motivational climate variables and SETS are nonlinear, (d) the ease of the course and demographic variables are only weakly associated with SETs, and (e) the largest predictors of instructor and course rating are similar, but somewhat different.

中文翻译：

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使用概率机器学习预测本科生对教学的评价：动机氛围的重要性

本研究的目的是通过使用概率机器学习开发在线模拟器来了解课程中动机氛围变量和学生教学评价 (SET) 之间的复杂相互作用。我们使用 30 个班级 2938 名本科生的数据来创建基于贝叶斯信念网络的在线模拟器。我们创建了气泡图、折线图和雷达图来显示研究变量之间的关系。研究结果表明，(a) 动机氛围变量（通过动机变量的音乐模型测量）是 SET 的最大预测因素，(b) 学生兴趣（对课程作业和教学方法）是 SET 的总体最大预测因素，（ c) 动机气候变量和 SETS 之间的关系是非线性的，(d) 课程的难易程度和人口统计变量与 SET 的相关性很弱，(e) 教师和课程评分的最大预测因子相似，但有些不同。