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

Valdemar Švábenský; Brendan Flanagan; Erwin Daniel López Zapata; Atsushi Shimada

cs.CYFeb 19, 2026

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

Authors: Valdemar Švábenský, Brendan Flanagan, Erwin Daniel López Zapata, Atsushi Shimada

Organizations: Faculty of Informatics, Masaryk University, Brno, Czech Republic · Kyoto University, Kyoto, Japan · Kyushu University, Fukuoka, Japan

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.

Figures & tables

Conference name (with abbreviation) and URL	Publisher	ICORE rank ( Computing Research and Education, 2023 )	First year	Anniversary in 2025
International Learning Analytics and Knowledge Conference ( LAK ) https://www.solaresearch.org/events/lak	Society for Learning Analytics Research (SoLAR) and Association for Computing Machinery (ACM)	A	2011	15 th
Educational Data Mining ( EDM ) https://educationaldatamining.org/conferences	The International Educational Data Mining Society (IEDMS)	B	2008	18 th
Artificial Intelligence in Education ( AIED ) https://iaied.org/conferences	The International AI in Education Society and Springer	A	1983	26 th ( Self, 2016 )

Table 1. The most prominent conferences in each field. Data are bound to the time of writing this paper (submitted February 2025). We note that while LAK and EDM have been held annually since their inception, AIED has been held biannually since 1983, then switching to an annual mode since 2017 ( Self, 2016 ) .

Survey of public datasets	Publication date	Datasets reported	Overlap with datasets across all prior work	Overlap with datasets in our survey (see Table 3 )
Romero and Ventura (2020)	2020, Jan	12	— (the first survey)	8
Mihaescu and Popescu (2021)	2021, Feb	44	12 (27%), superset of ( Romero and Ventura, 2020 )	17
BigData Lab @USTC (2021)	2021, Nov	18	3 (17%)	14
Lin et al. (2024)	2024, Jun	10	9 (90%)	9
Xiong et al. (2024)	2024, Jul	12	10 (83%)	9
Our paper	2025–2026	172	29 (17%)	—

Table 2. Comparison of our work to the related surveys reviewed in Section 2.1 , sorted by publication date. We do not list the surveys by Haim et al. ( Haim et al., 2023b ; Haim et al., 2023a ; Haim et al., 2023c ) because they examined a subset of our candidate papers.

Figure 1. PRISMA flow diagram. Generated using the tool by Haddaway et al. (2022) . Diagram showing the progression of selected papers.

Full \| Short	2020		2021		2022		2023		2024		Subtotal		Total
LAK	50	30	42	29	39	23	49	22	66	29	246	133	379
EDM	30	29	22	44	26	29	18	22	21	39	117	163	280
AIED	49	66	40	76	40	40	53	26	49	27	231	235	466
Subtotal	129	125	104	149	105	92	120	70	136	95	594	531	1125
Total	254		253		197		190		231		1125

Table 4. Distribution of all the 1,125 published papers (full | short) across the three target conferences throughout the years 2020–2024. In two cases, the text of the proceedings preface listed a slightly incorrect total paper count. Therefore, the numbers below are actual counts of all examined papers. (Each year, LAK published a total of 62–95 papers, EDM 40–66 papers, and AIED 76–116 papers.)

Full \| Short	2020		2021		2022		2023		2024		Subtotal		Total
LAK	47	27	41	26	34	21	45	20	63	26	230	120	350 (92.3%)
EDM	30	28	22	44	26	29	17	21	21	38	116	160	276 (98.6%)
AIED	48	64	39	70	38	40	52	26	48	25	225	225	450 (96.6%)
Subtotal	125	119	102	140	98	90	114	67	132	89	571	505	1076 (95.6%)
Total	244 (96.1%)		242 (95.7%)		188 (95.4%)		181 (95.3%)		221 (95.7%)		1076 (95.6%)

Table 5. Distribution of all the 1,076 candidate (eligible) papers. Same as in Table 4 , the papers are divided into full | short across the three target conferences throughout the years 2020–2024. In addition, the row and column Total include a percentage of the candidate papers compared to the corresponding cell in Table 4 .

Full \| Short	2020		2021		2022		2023		2024		Subtotal		Total
LAK	4	0	6	4	1	1	7	1	10	4	28	10	38 (10.9%)
EDM	9	7	6	13	6	7	9	7	7	11	37	45	82 (29.7%)
AIED	6	10	7	7	12	5	13	4	15	5	53	31	84 (18.7%)
Subtotal	19	17	19	24	19	13	29	12	32	20	118	86	204 (19.0%)
Total	36 (14.8%)		43 (17.8%)		32 (17.0%)		41 (22.7%)		52 (23.5%)		204 (19.0%)

Table 6. Distribution of all the 204 selected papers, that is, those that either used or provided an open dataset. The papers are again divided into full | short across the three target conferences throughout the years 2020–2024. In addition, the row and column Total include a percentage of the candidate papers compared to the corresponding cell in Table 5 .

Figure 2. Distributions of dataset frequency across educational topics and levels of students. Bar chart showing the prevalence of natural sciences topics and K-12 contexts.

Figure 3. Geographical contexts of the dataset collection distributed per continent. This map considers 163 datasets (172 minus the 9 that are not applicable). However, some datasets pertained to multiple countries/continents, so the counts in the map sum to 174. The map was automatically generated in Python using the geopandas module ( den Bossche et al., 2024 ) . World map with highlighted continents, showing the prevalence of dataset origin in North America.

Figure 4. Distribution of number of students into bins. Bar chart showing the various categories of the number of students.

Figure 5. Distribution of number of data points into bins. Bar chart showing the various categories of numbers of data points.

Usage	Analytic	Common metrics (sorted by usage)
137×	Supervised ML	accuracy (50×), AUC (45×), F1 (38×), RMSE (19×), recall (17×), precision (13×)
102×	Deep learning	accuracy (34×), AUC (30×), F1 (30×), recall (12×), RMSE (10×), MAE (10×)
75×	Statistics	inferential tests (30×), correlations (19×)
38×	Knowledge tracing	AUC (25×), accuracy (13×)
17×	Large language models	F1 (7×), accuracy (6×)
8×	Unsupervised ML	custom metrics, such as cluster properties

Table 7. The analytical categories and their most common metrics used with the datasets across the 204 selected papers.

ID	Dataset name as URL, description, and citation	Usage in papers at LAK, EDM, and AIED
1	100K Coursera’s Course Reviews – labeled user reviews of online courses ( Adona, 2017 )	1× ( Xiao et al., 2022 )
2	2018 Duolingo Second Language Acquisition Modeling (SLAM) Task ( Settles et al., 2018 ; Settles, 2018 )	2× ( Ruan et al., 2021 ; Wu et al., 2020 )
3	Academic Performance Evolution for Engineering Students – assessment ( Delahoz-Dominguez et al., 2020 )	1× ( Cohausz et al., 2023 )
4	Argument Annotated Essays (Nau) – sentences from Portuguese essays ( Nau et al., 2020 )	1× ( Ferreira Mello et al., 2022 )
$\star$ 5	ASAP-AES (Automated Essay Scoring Prize) – essays and their grades ( Hamner et al., 2012 )	7× ( Lu and Cutumisu, 2021 ; Doewes and Pechenizkiy, 2021 ; Doewes et al., 2022 ; Doewes et al., 2023 ; Wang et al., 2021b ; Aomi et al., 2021 ; Yamaura et al., 2023 )
$\star$ 6	ASAP-SAS (Short Answer Scoring Prize) – textual answers to questions ( Barbara et al., 2012 )	4× ( Funayama et al., 2022 ; Condor et al., 2022 ; Zeng et al., 2023 ; Condor and Pardos, 2024 )

Table 8. All 172 datasets sorted alphabetically. The

\star

marks datasets used 3+ times. URL validity last checked on 31 January 2025.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

1	P	Prepare for Open Data Sharing Before Collecting the Data.	[Reusable]
		$\square$ Create a research consent form that explains to participants the data anonymization and sharing.
		$\square$ Obtain an IRB approval (if needed). In the application, explain data anonymization and sharing.
2	R	Refine and Preprocess the Data for Open Use.	[Interoperable, Reusable]
		$\square$ Anonymize and clean the dataset, especially removing participants’ sensitive information.
		$\square$ Have an independent human validator to check for issues. If using AI tools, ensure human oversight.
3	A	Add Clear Documentation and Metadata.	[Findable, Accessible, Interoperable]

Appendix

Table 9. The 8-item PRACTICE guidelines for open data management in LA research. Each item has 2 actionable checklist sub-items. Also, each item includes a note (in gray) regarding which aspect of the FAIR guidelines ( Wilkinson et al., 2016 ; European Commission and Directorate-General for Research and Innovation, 2018 ) it supports.

Educational Data Mining

Educational Data Mining studies computational methods for analyzing learning data to understand learners, improve instruction, and support educational decisions.

See all 72 papers

Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support
Feb 23, 2026Deborah N. Jakobi, David R. Reich, Paul Prasse +3Eye Tracking
Department of Computational Linguistics, University of Zurich. · Department of Computer Science, University of Potsdam. · Department of Informatics, University of Zurich.
DODA: A Database of Datasets for Aesthetics Research
Jul 30, 2026Lisa Koßmann, Ralf Bartho, Christoph Redies +1Personalized Image Aesthetics Assessment Dataset Discovery
Laboratory of Experimental Psychology, Department of Brain and Cognition, University of Leuven (KU Leuven), Leuven, Belgium · Leuven AI Institute, University of Leuven (KU Leuven), Leuven, Belgium · Experimental Aesthetics Group, Institute for Anatomy I, University Hospital Jena, Germany
Edustories: A Collection of Real-world Case Studies from Classroom Practices
Sep 17, 2026Michal Štefánik, Jan Nehyba, Jirina Karasova +4AI in Education Educational Data Mining
Faculty of Education, Masaryk University, Czechia · R&D Center for Large Language Models, National Institute of Informatics, Japan

Feb 23, 2026J/K move · Enter open · S save

Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support

Deborah N. Jakobi, David R. Reich, Paul Prasse, Jana M. Hofmann+2

Department of Computational Linguistics, University of Zurich. · Department of Computer Science, University of Potsdam. · Department of Informatics, University of Zurich.

Eye Tracking

Open paper AskPDF arXiv

Eye-tracking-while-reading corpora are a valuable resource for many different disciplines and use cases. Use cases range from studying the cognitive processes underlying reading to machine-learning-based applications, such as gaze-based assessments of reading comprehension. The past decades have seen an increase in the number and size of eye-tracking-while-reading datasets as well as increasing diversity with regard to the stimulus languages covered, the linguistic background of the participants, or accompanying psychometric or demographic data. The spread of data across different disciplines and the lack of data sharing standards across the communities lead to many existing datasets that cannot be easily reused due to a lack of interoperability. In this work, we aim at creating more transparency and clarity with regards to existing datasets and their features across different disciplines by i) presenting an extensive overview of existing datasets, ii) simplifying the sharing of newly created datasets by publishing a living overview online, https://t.uzh.ch/1Yh, presenting over 55 features for each dataset, and iii) integrating all publicly available datasets into the Python package pymovements which offers an eye-tracking datasets library. By doing so, we aim to strengthen the FAIR principles in eye-tracking-while-reading research and promote good scientific practices, such as reproducing and replicating studies.

Abstract

Educational Data Mining

Explore similar work

Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support

DODA: A Database of Datasets for Aesthetics Research

Edustories: A Collection of Real-world Case Studies from Classroom Practices