2024/04/20 by Fang Liu, Bosheng Ding, Liu, Fang +9
Computer Science · Psychology · Social Sciences · #FOS: Computer and information sciences #Higher Education Learning Practices #Innovative Teaching and Learning Methods #Online Learning and Analytics #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2404.13267
openalex publication_date 2024/04/20 · openalex created_date 2024/04/24 · openalex updated_date 2026/07/28
Adult learning is increasingly recognized as a crucial way for personal development and societal progress. It however is challenging, and adult learners face unique challenges such as balancing education with other life responsibilities. Collecting feedback from adult learners is effective in understanding their concerns and improving learning experiences, and social networks provide a rich source of real-time sentiment data from adult learners. Machine learning technologies especially large language models (LLMs) perform well in automating sentiment analysis. However, none of such models is specialized for adult learning with accurate sentiment understanding. In this paper, we present A-Learn, which enhances adult learning sentiment analysis by customizing existing general-purpose LLMs with domain-specific datasets for adult learning. We collect adult learners' comments from social networks and label the sentiment of each comment with an existing LLM to form labelled datasets tailored for adult learning. The datasets are used to customize A-Learn from several base LLMs. We conducted experimental studies and the results reveal A-Learn's competitive sentiment analysis performance, achieving up to 91.3% accuracy with 20% improvement over the base LLM. A-Learn is also employed for word cloud analysis to identify key concerns of adult learners. The research outcome of this study highlights the importance of applying machine learning with educational expertise for teaching improvement and educational innovations that benefit adult learning and adult learners.