Demystify Adult Learning: A Social Network and Large Language Model Assisted Approach

TL;DR

This paper introduces A-Learn, a specialized sentiment analysis tool for adult learning, which customizes large language models with domain-specific data, significantly improving accuracy and providing insights into adult learners' concerns from social media.

Contribution

It presents a novel approach to adapt large language models for adult learning sentiment analysis using domain-specific datasets, enhancing accuracy and practical insights.

Findings

A-Learn achieves up to 91.3% sentiment analysis accuracy.

A-Learn outperforms base LLMs with a 20% accuracy improvement.

Word cloud analysis reveals key concerns of adult learners.

Abstract

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.