More graph comparison techniques on mind maps to provide students with feedback

Abstract

One of the limiting aspects in education research is the techniques available for determining if a student has learned something. In this work, the goal is to extend our exploration of how mind maps can be automatically analyzed using their graph properties to reflect student learning. In particular, a set of student mind maps are created three times during a class in both 2011 and 2012 on digital system design using a common technical vocabulary. These mind maps are analyzed by extracting graph metrics by comparison with a criterion mind map, which is an expert created mind map. The metrics are derived from traditional graph metrics (average degree and graph density), three sets of difference metrics analyzed with a internally created tool, and a graph metric invented for comparing proteins. The results of this exploratory analysis is that five of the six metrics can be used to evaluate if a student is learning and connecting the vocabulary in a given subject over time. Additionally, these five metrics are correlated to one another. This result is promising, but we emphasize that these metrics do not correlate directly to class performance based on student grades over the course, and therefore, the current goal for this measurement technique is to be used to provide the student with automated feedback on their mind maps as related to the technical vocabulary of a course. This work extends our original work by increasing the number of graph metrics that are used to automatically analyze student maps to a criterion map. The idea is to find a number of graph metrics that can then be combined to help analyze a students mind map and provide them with useful feedback. Even though our results show that compare 5 metrics and each metric can be used to observe student improvement, each of these metrics differs in how the metric can be interpreted and related to the process of learning. Therefore, our goal is to find a number of these metrics so that they can be combined to provide the student with a variety of feedback results to help them understand their errors in terms of the structure of their mind map.