Automating the Evaluation of Education Apps with App Store Data

With the vast number of apps and the complexity of their features, it is becoming challenging for teachers to select a suitable learning app for their courses. Several evaluation frameworks have been proposed in the literature to assist teachers with this selection. The iPAC framework is a well-established mobile learning framework highlighting the learners' experience of personalisation, authenticity and collaboration (iPAC). In this paper, we introduce an approach to automate the identification and comparison of iPAC relevant apps. We experiment with natural language processing and machine learning techniques, using data from the app description and app reviews publicly available in app stores. We further empirically validate the keyword base of the iPAC framework to increase the reliability of our findings. Our approach automatically identifies iPAC relevant apps with promising results (F1 score ~72%) and evaluates them similarly as domain experts (spearman's rank correlation 0.54). We discuss how our findings can be useful for teachers, students, and app vendors.