Open AccessExamining Users’ Concerns while Using Mobile Learning Apps
Author(s) -
Senanu Okuboyejo,
Ooreofe Koyejo
Publication year - 2021
Publication title -
international journal of interactive mobile technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.316
H-Index - 16
ISSN - 1865-7923
DOI - 10.3991/ijim.v15i15.22345
Subject(s) - latent dirichlet allocation , computer science , topic model , sentiment analysis , mobile device , thematic analysis , world wide web , information overload , class (philosophy) , the internet , mobile apps , task (project management) , multimedia , data science , information retrieval , artificial intelligence , qualitative research , social science , management , sociology , economics
Mobile learning applications (apps) are increasingly and widely adopted for learning purposes and educational content delivery globally, especially with the massive means of accessing the internet done majorly on mobile handheld devices. Users often submit their feedback on use, experience and general satisfaction via the reviews and ratings given in the digital distribution platforms. With this massive information given through the reviews, it presents an opportunity to derives valuable insights which can be utilized for various reasons and by different stakeholders of these mobile learning apps. This large volume of online reviews creates significant information overload which presents a time-consuming task to read through all reviews. By combining text mining techniques of topic modeling using Latent Dirichlet Algorithm (LDA) and sentiment analysis using Linguistic Inquiry Word Count (LIWC), we analyze these user reviews. These techniques identify inherent topics in the reviews and identifies variables of user satisfaction of mobile learning apps. The thematic analysis done reveals different keywords which guide classification into the topics identified. Conclusively, the topics derived are important to app stakeholders for further modifications and evolution tasks.