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Automating the Evaluation of Education Apps with App Store Data

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Title: Automating the Evaluation of Education Apps with App Store Data
Language: English
Authors: Haering, Marlo (ORCID 0000-0002-2551-5065), Bano, Muneera (ORCID 0000-0002-1447-9521), Zowghi, Didar (ORCID 0000-0002-6051-0155), Kearney, Matthew, Maalej, Walid
Source: IEEE Transactions on Learning Technologies. Feb 2021 14(1):16-27.
Availability: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Peer Reviewed: Y
Page Count: 12
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Descriptors: Automation, Courseware, Computer Software Evaluation, Computer Software Selection, Data Use, Natural Language Processing, Individualized Instruction, Authentic Learning, Cooperative Learning, Computer Software Reviews
DOI: 10.1109/TLT.2021.3055121
ISSN: 1939-1382
Abstract: 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 personalization, authenticity, and collaboration (iPAC). In this article, 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 based on the app users' language in app reviews. Our approach automatically identifies iPAC relevant apps with promising results ("F"1 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.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1288423
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Haering%2C+Marlo%22">Haering, Marlo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2551-5065">0000-0002-2551-5065</externalLink>)<br /><searchLink fieldCode="AR" term="%22Bano%2C+Muneera%22">Bano, Muneera</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1447-9521">0000-0002-1447-9521</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zowghi%2C+Didar%22">Zowghi, Didar</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6051-0155">0000-0002-6051-0155</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kearney%2C+Matthew%22">Kearney, Matthew</searchLink><br /><searchLink fieldCode="AR" term="%22Maalej%2C+Walid%22">Maalej, Walid</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22IEEE+Transactions+on+Learning+Technologies%22"><i>IEEE Transactions on Learning Technologies</i></searchLink>. Feb 2021 14(1):16-27.
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  Data: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
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  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 personalization, authenticity, and collaboration (iPAC). In this article, 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 based on the app users' language in app reviews. Our approach automatically identifies iPAC relevant apps with promising results ("F"1 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.
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