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.

翻译：学习分析（LA）领域近期迎来了两个重要里程碑：第十五届学习分析与知识国际会议的召开，以及对2011年学习分析定义的更新——这反映了该学科十五年来的发展变迁。然而，尽管学习分析领域持续扩展，学界对其研究主题、资助模式与合作网络的演变历程及其内在关联仍缺乏系统认知。本研究通过无监督机器学习、自然语言处理与网络分析方法，对十五年间发表于学习分析与知识国际会议的936篇长文与短文进行了系统性分析。研究发现：该领域存在稳定的高产出核心作者群，同时新人更替率较高；资助来源与研究取向存在系统性关联；并识别出六个持续存在的主题中心，这些主题在全球范围内共享，但在不同国家的显要性存在差异。这六个涵盖学习分析研究的主题中心包括：自我调节学习、仪表板与理论、社会性学习、自动化反馈、多模态分析以及结果预测。本研究进一步指出未来面临的关键挑战：拓宽学界参与度、降低对有限资助来源的依赖，以及确保新兴研究方向持续响应教育实践与社会需求。