NEW ANALYSIS OF VISUALIZATION IN EDUINFORMATICS USING A NETWORK WITH PARAMETRIC AND NONPARAMETRIC CORRELATION COEFFICIENTS WITH THRESHOLD

Eduinformatics, a new term coined by us, is a field that combines education and informatics, and novel techniques will need to be developed for this field. Earlier, we developed a new visualization method to visualize the curriculum of Kobe Tokiwa University using multidimensional scaling (MDS) and a scatter plot. In this study, our focus is on methods to analyze the relationships between answers to questions in eduinformatics questionnaires. MDS methods are very useful, but have limitations in that their results are difficult to interpret. To facilitate the interpretation of these results, we develop a new visualization method using a network with both parametric and non-parametric correlation coefficients with a threshold (VNCC). VNCC has nine steps. We apply the VNCC method to research on nursing education, and provide an example of the visualization of the result. VNCC methods will be useful in dealing with qualitative research in eduinformatics

ANALYZING STUDENTS’ COURSE EVALUATIONS USING TEXT MINING: VISUALIZATION OF OPEN-ENDED RESPONSES IN A CO-OCCURRENCE NETWORK

Japan’s Standards for Establishment of Universities states, “A university shall conduct organized training and research to improve the content and methodology used in courses at said university.” Based on this, most of Japan’s universities have recently implemented course evaluations by students. Student course evaluations are intended to quantify and provide an understanding of students’ satisfaction with their courses, and all universities are implementing them as one way to objectively evaluate courses. These course evaluations often combine computer-graded multiple-choice items with open-ended items. Computer-graded multiple-choice items are easy to assess because the responses are quantifiable. However, open-ended items’ responses are text data, and objectively grasping the students’ general tendencies is challenging. Moreover, it is difficult to avoid risking arbitrary and subjective interpretations of the data by the analysts who summarize them. Therefore, to avoid these risks as much as possible, the so-called “text-mining” method or “quantitative content analysis” approach might be useful. This study shares our experiences using text mining to analyze students’ course evaluations through the visualization of their open-ended responses in a co-occurrence network, and we discuss the potential of this method.&nbsp