Latent Dirichlet Allocation-Based Topic Mining Analysis of M-learning Applications for Higher Education
Year: 2024
Type: conference-paper
Abstract: Mobile learning has transformed the educational process, making learning more flexible, engaging, and accessible. As mobile applications (APPs) have become an increasingly important tool for higher education, users’ experiences and needs are valuable to educational institutions. This study collected user-generated comments from APP stores and established a database of 48,025 valid text data. With a Latent Dirichlet Allocation (LDA) based topic model, 16 topics were extracted and then summarized into 7 themes, including learning settings, resources, experiences, functions, group study, drawbacks, examination system, and customer service. Sentiment analysis was conducted and the negative tendency shows the need for improvement.
Authors: Yingqiao Ma, Shuai Wang
Institution: Dalian Neusoft University of Information
Language: English
Topic: Mobile Learning in Education
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4410210844