Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.17314v1</id>\n    <title>Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE</title>\n    <updated>2026-02-19T12:23:25Z</updated>\n    <link href='https://arxiv.org/abs/2602.17314v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.17314v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Open datasets play a crucial role in three research domains that intersect data science and education: learning analytics, educational data mining, and artificial intelligence in education. Researchers in these domains apply computational methods to analyze data from educational contexts, aiming to better understand and improve teaching and learning. Providing open datasets alongside research papers supports reproducibility, collaboration, and trust in research findings. It also provides individual benefits for authors, such as greater visibility, credibility, and citation potential. Despite these advantages, the availability of open datasets and the associated practices within the learning analytics research communities, especially at their flagship conference venues, remain unclear. We surveyed available datasets published alongside research papers in learning analytics. We manually examined 1,125 papers from three flagship conferences (LAK, EDM, and AIED) over the past five years. We discovered, categorized, and analyzed 172 datasets used in 204 publications. Our study presents the most comprehensive collection and analysis of open educational datasets to date, along with the most detailed categorization. Of the 172 datasets identified, 143 were not captured in any prior survey of open data in learning analytics. We provide insights into the datasets' context, analytical methods, use, and other properties. Based on this survey, we summarize the current gaps in the field. Furthermore, we list practical recommendations, advice, and 8-item guidelines under the acronym PRACTICE with a checklist to help researchers publish their data. Lastly, we share our original dataset: an annotated inventory detailing the discovered datasets and the corresponding publications. We hope these findings will support further adoption of open data practices in learning analytics communities and beyond.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CY'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.DB'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n    <published>2026-02-19T12:23:25Z</published>\n    <arxiv:comment>Recently accepted to ACM Transactions on Knowledge Discovery from Data (TKDD). To appear. (Preprint will be updated with full bibliographic info.)</arxiv:comment>\n    <arxiv:primary_category term='cs.CY'/>\n    <author>\n      <name>Valdemar Švábenský</name>\n    </author>\n    <author>\n      <name>Brendan Flanagan</name>\n    </author>\n    <author>\n      <name>Erwin Daniel López Zapata</name>\n    </author>\n    <author>\n      <name>Atsushi Shimada</name>\n    </author>\n    <arxiv:doi>10.1145/3798096</arxiv:doi>\n    <link href='https://doi.org/10.1145/3798096' rel='related' title='doi'/>\n  </entry>"
}