Summative course evaluation is an important activity to assess how well a course was perceived by the students. The Course Evaluation Questionnaire is a standardized tool which allows such evaluation on a large scale and in a well‐structured way. In this work we evaluate how various parameters of it affect the “Good teaching” scale – an indicator of the teaching quality of a course – especially in the scope of the Faculty of Engineering at Lund University. A correlation analysis, as well as a decision tree‐based machine learning analysis was performed in order to identify potential, possibly unintended, influencers on foremost the “Good teaching” scale. An adequate accuracy of the decision tree‐classifier of 80 % for the three considered departments was achieved. Further, a high correlation of the scales of “Good teaching” and “Clear goals and standards” was found, as was a clear link between “Good teaching”, “Appropriate Assessment”, and “The course seems important for my education”. Several severely correlated items were identified as well, and suggestion on their removal are proposed.
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