Earl Hunt and Tara Madhyastha
Modern educational and psychological measurements are governed by models that do not allow for identification of patterns of student thought. However, in many situations, including diagnostic assessment, it is more important to understand student thought than to score it. We propose using entropy-based clustering to group responses to both a standard achievement test and a test specifically designed to reveal different facets of student thinking. We show that this approach is able to identify patterns of thought in these domains, although there are limitations to what information can be obtained from multiple choice responses alone.