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.
Highlights
The number of applications downloadable via mobile phone or website is increasing rapidly, and so is the number of users
The downloads started in the first quarter of 2020, when the COVID-19 outbreak started in Indonesia and continued until the downloads reached 466 million on Google Play by the fourth quarter of 2020
The current study aims to extend the body of literature by examining user reviews of educational apps
Summary
The number of applications (apps) downloadable via mobile phone or website is increasing rapidly, and so is the number of users. There are a lot of available educational application services such as Coursera, edX, Khan Academy, LinkedIn Learning, Quipper, Socratic and Udemy, downloadable anywhere and anytime. These apps help students to create effective collaboration with teachers and other learners outside class. The current study aims to extend the body of literature by examining user reviews of educational apps. The proposed method in this study consists of three stages: collecting and pre-processing text reviews, text-mining lexical attributes, and measuring user satisfaction towards educational application services from the lexical attributes.
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