Improved method for creating criterion maps for automatic mind map analysis

Abstract

In this work, we continue our study on analyzing student created mind maps automatically by providing a new methodology to select the technical vocabulary that students use in their mind maps. The basis of our previous experiments is an instructor chooses a set of twenty words used within the course that will be the set of words to test in mind maps. The instructor then creates their own mind map with this set of words, which is called the criterion map. Next, students create their mind maps using the same twenty words, and the criterion map and student map are analyzed with each other using various algorithms to produce metrics that quantify how similar the two maps are. When this activity is repeated longitudinally over a semester we can show that students are learning if their metrics of similarity are improving over time. One challenge, however, is which twenty words should be selected by the teacher. Similarly, will the set of twenty words impact the quality of observed learning. In 2011 and 2012, we collected our mind map data based on twenty words selected with no methodology (random). In 2013 and 2014, we created a methodology where approximately forty words are initially chosen, and these forty words are reduced down to 20 by creating a larger mind map and picking the words that have low connectivity. The hypothesis here is that less connectivity in the mind map will make it easier for the student to create their own quality maps. Our results show that this new methodology improves the arithmetic average of one of our best comparison metrics for all data points by a worse case of 2.4% better and best case 75% better.