Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE

2026-02-19

AI Summary

This study addresses the lack of systematic understanding regarding the use and sharing practices of open datasets in the learning analytics field. Through manual content analysis, the authors systematically reviewed 1,125 papers from the past five years published in the LAK, EDM, and AIED conferences, identifying and structurally annotating 172 open datasets with respect to their sources, types, applications, and metadata. The work presents the most comprehensive inventory of open datasets in learning analytics to date, including 143 datasets not previously cataloged in existing reviews. Furthermore, it introduces the PRACTICE guidelines—a set of eight key principles—accompanied by a supporting checklist to promote standardized open data practices. The annotated dataset registry is publicly released to foster community reuse and collaborative advancement.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Evaluation and Analysis
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• open datasets
• learning analytics
• reproducibility
• data sharing
• educational research

Innovation

• open datasets
• learning analytics
• reproducibility
• PRACTICE guidelines
• educational data mining