Fifteen Years of Learning Analytics Research: Topics, Trends, and Challenges

2026-01-12

AI Summary

This study addresses the lack of a systematic synthesis of thematic evolution, collaboration networks, and funding linkages in the learning analytics field over the past fifteen years. Drawing on 936 papers from 15 years of the Learning Analytics and Knowledge (LAK) conference, it integrates unsupervised machine learning, natural language processing, and network analysis to identify— for the first time—six persistent global thematic clusters. The findings reveal a stable core of influential authors alongside high turnover among newcomers, demonstrate that funding sources significantly shape research trajectories, and show that while these six thematic clusters exhibit uneven global distribution, they remain consistently present over time. This work advances understanding of the structural evolution and underlying drivers of the learning analytics research landscape.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Life-Long and Continual Learning

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The learning analytics (LA) community has recently reached two important milestones: celebrating the 15th LAK conference and updating the 2011 definition of LA to reflect the 15 years of changes in the discipline. However, despite LA's growth, little is known about how research topics, funding, and collaboration, as well as the relationships among them, have developed within the community over time. This study addressed this gap by analyzing all 936 full and short papers published at LAK over a 15-year period using unsupervised machine learning, natural language processing, and network analytics. The analysis revealed a stable core of prolific authors alongside high turnover of newcomers, systematic links between funding sources and research directions, and six enduring topical centers that remain globally shared but vary in prominence across countries. These six topical centers, which encompass LA research, are: self-regulated learning, dashboards and theory, social learning, automated feedback, multimodal analytics, and outcome prediction. Our findings highlight key challenges for the future: widening participation, reducing dependency on a narrow set of funders, and ensuring that emerging research trajectories remain responsive to educational practice and societal needs.

Problem

• learning analytics
• research topics
• funding
• collaboration
• temporal evolution

Innovation

• learning analytics
• unsupervised machine learning
• natural language processing
• network analytics
• research trends