dorsal/arxiv
View SchemaFifteen Years of Learning Analytics Research: Topics, Trends, and Challenges
| Authors | Valdemar Švábenský, Conrad Borchers, Elvin Fortuna, Elizabeth B. Cloude, Dragan Gašević |
|---|---|
| Categories | |
| ArXiv ID | 2601.07629vv1 |
| URL | https://arxiv.org/abs/2601.07629 |
| DOI | 10.1145/3785022.3785131 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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.
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"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\u0027s 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.",
"arxiv_id": "2601.07629",
"authors": [
"Valdemar \u0160v\u00e1bensk\u00fd",
"Conrad Borchers",
"Elvin Fortuna",
"Elizabeth B. Cloude",
"Dragan Ga\u0161evi\u0107"
],
"categories": [
"cs.CY"
],
"doi": "10.1145/3785022.3785131",
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Fifteen Years of Learning Analytics Research: Topics, Trends, and Challenges",
"url": "https://arxiv.org/abs/2601.07629",
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