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

open data; public data; data sharing; data management; open science; learning analytics; educational data mining; artificial intelligence in education; AI in education; systematic literature review; systematic mapping study; survey

Links

GN25-15839I, research and development project.

Changed: 27/2/2026 00:51, RNDr. Daniel Jakubík

Abstract

In the original language

br /\>Results and contributions: 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.

Cite

ŠVÁBENSKÝ, Valdemar; Brendan FLANAGAN; Erwin Daniel LÓPEZ ZAPATA and Atsushi SHIMADA. Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE. ACM Transactions on Knowledge Discovery from Data. ACM, 2026. ISSN 1556-4681. Available from: https://doi.org/10.1145/3798096.

@article{101822,
   author = {Švábenský, Valdemar and Flanagan, Brendan and López Zapata, Erwin Daniel and Shimada, Atsushi},
   doi = {https://doi.org/10.1145/3798096},
   keywords = {open data; public data; data sharing; data management; open science; learning analytics; educational data mining; artificial intelligence in education; AI in education; systematic literature review; systematic mapping study; survey},
   language = {eng},
   issn = {1556-4681},
   journal = {ACM Transactions on Knowledge Discovery from Data},
   title = {Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE},
   url = {https://dl.acm.org/doi/10.1145/3798096},
   year = {2026}
}

TY  - JOUR
ID  - 101822
AU  - Švábenský, Valdemar - Flanagan, Brendan - López Zapata, Erwin Daniel - Shimada, Atsushi
PY  - 2026
TI  - Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE
JF  - ACM Transactions on Knowledge Discovery from Data
PB  - ACM
SN  - 1556-4681
KW  - open data
KW  - public data
KW  - data sharing
KW  - data management
KW  - open science
KW  - learning analytics
KW  - educational data mining
KW  - artificial intelligence in education
KW  - AI in education
KW  - systematic literature review
KW  - systematic mapping study
KW  - survey
UR  - https://dl.acm.org/doi/10.1145/3798096
N2  - br /\>Results and contributions: 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.
ER  -

ŠVÁBENSKÝ, Valdemar; Brendan FLANAGAN; Erwin Daniel LÓPEZ ZAPATA and Atsushi SHIMADA. Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE. \textit{ACM Transactions on Knowledge Discovery from Data}. ACM, 2026. ISSN~1556-4681. Available from: https://doi.org/10.1145/3798096.