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

• URL: http://arxiv.org/abs/2602.17314v1
• Date: Thu, 19 Feb 2026 12:23:25 GMT
• Title: Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE
• Authors: Valdemar Švábenský, Brendan Flanagan, Erwin Daniel López Zapata, Atsushi Shimada,
• Abstract 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.<n>Providing open datasets alongside research papers supports, collaboration, and trust in research findings.<n>Despite these advantages, the availability of open datasets and associated practices within the learning analytics research communities, especially at their flagship conference venues, remain unclear.
• Score: 0.4666493857924357
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value [74.80873109856563]
  OpenDataArena (ODA) is a holistic and open platform designed to benchmark the intrinsic value of post-training data.<n>ODA establishes a comprehensive ecosystem comprising four key pillars: (i) a unified training-evaluation pipeline that ensures fair, open comparisons across diverse models; (ii) a multi-dimensional scoring framework that profiles data quality along tens of distinct axes; and (iii) an interactive data lineage explorer to visualize dataset genealogy and dissect component sources.
  arXiv  Detail & Related papers  (2025-12-16T03:33:24Z)
• A Comprehensive Survey on Imbalanced Data Learning [56.65067795190842]
  imbalanced data is prevalent in various types of raw data and hinders the performance of machine learning.<n>This survey systematically analyzes various real-world data formats.<n>It concludes existing researches for different data formats into four categories: data re-balancing, feature representation, training strategy, and ensemble learning.
  arXiv  Detail & Related papers  (2025-02-13T04:53:17Z)
• Insights from Publishing Open Data in Industry-Academia Collaboration [3.458783333044753]
  This paper explores the motivations and lessons learned from publishing open data sets in such collaborations.<n>We surveyed participants in a European research project that published 13 data sets.<n>We found that planning the data collection is essential, and that only few datasets had accompanying scripts for improved reuse.
  arXiv  Detail & Related papers  (2025-01-24T07:30:46Z)
• The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track [1.5993707490601146]
  This work provides an analysis of dataset development practices at NeurIPS through the lens of data curation.<n>We present an evaluation framework for dataset documentation, consisting of a rubric and toolkit.<n>Results indicate greater need for documentation about environmental footprint, ethical considerations, and data management.
  arXiv  Detail & Related papers  (2024-10-29T19:07:50Z)
• SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents [49.54155332262579]
  We release a new entity and relation extraction dataset for entities related to datasets, methods, and tasks in scientific articles. Our dataset contains 106 manually annotated full-text scientific publications with over 24k entities and 12k relations.
  arXiv  Detail & Related papers  (2024-10-28T15:56:49Z)
• Data-Centric AI in the Age of Large Language Models [51.20451986068925]
  This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs) We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
  arXiv  Detail & Related papers  (2024-06-20T16:34:07Z)
• A Survey on Data Selection for Language Models [148.300726396877]
  Data selection methods aim to determine which data points to include in a training dataset. Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive. Few organizations have the resources for extensive data selection research.
  arXiv  Detail & Related papers  (2024-02-26T18:54:35Z)
• Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face [46.60562029098208]
  We analyze all 7,433 dataset documentation on Hugging Face. Our study offers a unique perspective on analyzing dataset documentation through large-scale data science analysis.
  arXiv  Detail & Related papers  (2024-01-24T21:47:13Z)
• CoCon: A Data Set on Combined Contextualized Research Artifact Use [0.0]
  CoCon is a large scholarly data set reflecting the combined use of research artifacts in academic publications' full-text. Our data set comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications. We formalize a link prediction task for "combined research artifact use prediction" and provide code to utilize analyses of and the development of ML applications on our data.
  arXiv  Detail & Related papers  (2023-03-27T13:29:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.