Analysing user reviews of interactive educational apps: a sentiment analysis approach

ABSTRACT

The aim of this study is to investigate the public opinion (i.e., the learners themselves) of apps incorporating emerging technologies Augmented Reality (AR) and Virtual Reality (VR) and find out how satisfied mobile platform users are with these technologies for educational purposes. We performed sentiment analysis of more than one million reviews from 800 different Android apps collected by systematic scrapping of the Google Play Store. Identified apps were separated into five categories: AR, AR+Educational, VR, VR+Educational, and Educational. We identify reviews from each app as positive, negative, or neutral, and apply aspect labels depending on the content of the review. We adopted Bidirectional Encoder Representations from Transformers (BERT) and classify the labels into seven categories: technical issues, usability, content, user interaction, feature request, learning qualities, and advert-related. The results indicate that the positive sentiments are 54.6% for AR, 49.6% for AR+Educational, 47.7% for VR, 71.4% for VR+Educational, and 75.2% for Educational. The results suggest that education apps that do not incorporate AR or VR are receiving higher user satisfaction than apps that incorporate these emerging technologies. Analyzing and understanding user reviews will help instructional designers, software developers, and hardware designers to resolve the key inhibitors .of these apps.

KEYWORDS:

• Augmented reality
• deep learning
• educational apps
• sentiment analysis
• user reviews
• virtual reality

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.oculus.com/rift/

2 https://www.vive.com/sea/

3 https://www.qualcomm.com/snapdragon

4 https://www.statista.com/statistics/266210/number-of-available-applications-in-the-google-play-store/

5 https://arvr.google.com/intl/en_in/cardboard/

6 https://pypi.org/project/play-scraper/

7 https://pypi.org/project/google-play-scraper/

8 https://cloud.google.com/natural-language

Additional information

Notes on contributors

Aadi Swadipto Mondal

Mr. Aadi Swadipto Mondal is a final year undergraduate student in the Department of Electronics and Electrical Communication Engineering at the Indian Institute of Technology Kharagpur. His software interest includes full-stack web and Android application development using deep learning technologies.

Yuang Zhu

Mr. Yuang Zhu completed his Masters of Engineering Studies at the University of Auckland, specializing in Software Engineering. His software interest includes full-stack software application development, and he is currently working in industry delivering billing and resource management solutions for large international clients.

Kaushal Kumar Bhagat

Dr. Kaushal Kumar Bhagat is currently working as an assistant professor in the Advanced Technology Development Centre at the Indian Institute of Technology (IIT), Kharagpur, India. His research area of interest includes online learning, augmented reality, virtual reality, flipped classroom, formative assessment and technology-enhanced learning.

Nasser Giacaman

Dr. Nasser Giacaman is a senior lecturer at the University of Auckland, New Zealand. He is Director of Software Engineering in the Department of Electrical, Computer, and Software Engineering. His disciplinary research includes parallel programming; his current research of Digital Educational Engineering focuses on delivering digital solutions across a number of different educational domains.