Measuring User Satisfaction of Educational Service Applications using Text Mining and Multicriteria Decision-Making Approach

Abstract

Rapid growth of educational technology services today means that there are more applications in the market. Users may find it hard to choose the most suitable application, so they look for references. Experience shared in the form of text reviews and numerical rating can provide references. Text re-views are particularly specific and so they can provide insights to user satis-faction. In this study, we use text mining and multicriteria decision-making approach to measure the user satisfaction. The data is crawled and collected from seven educational applications: Coursera, edX, Khan Academy, LinkedIn Learning, Quipper, Socratic and Udemy. Nine attributes are used to measure the user reviews according to quality model of e-learning sys-tems. The result is in favor of Khan Academy, while Quipper is ranked the lowest. The v-values used range between 0 and1 and what is unique is that the rank of Khan Academy and Quipper are not affected by v-value while the ranks of the other applications are. It indicates that Khan Academy has high user satisfaction in terms of utility and low complaint from individuals. Quipper shows the opposite.