Heterogeneity in Class: Clustering Students' Attitudes Towards Statistics

Abstract

Following the growing need for statistical knowledge and the ability to handle data (data literacy), an increasing number of degree programs include statistics courses and lectures in their curricula. Not only but especially in non-technical programmes one finds heterogeneous student groups in terms of attitudes towards these topics, and therefore, deeper insights into these attitudes can help to improve curricula and teaching to better fit with the respective student cohort. In a case study, we analyse the results of a survey in which students were asked about their attitude towards statistics, based on the well-known and widely-used SATS-36 questionnaire. Our aim is to identify different attitudinal profiles and to make the heterogeneity of student groups visible. By conducting several cluster analyses, we are able to find separable student groups that can be differentiated, e.g., by their interest towards statistics, by their self-confidence towards their own abilities, or by their willingness to invest more or less effort into learning for the respective class. It turns out that using the individual items instead of the proposed SATS-36 attitudinal constructs leads to a better separation of the student clusters, as does taking gender into account in a cluster analysis of mixed-type data.