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.

翻译：开放数据集在数据科学与教育交叉的三个研究领域中发挥着关键作用：学习分析、教育数据挖掘及教育人工智能。这些领域的研究者运用计算方法分析教育情境数据，旨在更深入理解并改善教学与学习。在科研论文中附有开放数据集有助于支持可重复性、协作性及研究结果的可信度，同时为作者带来个体收益，例如更高的可见性、可信度及引文潜力。尽管具有这些优势，学习分析研究社群（尤其在旗舰会议场合）中开放数据集的可获取性及相关实践仍不明确。我们对学习分析领域科研论文中已发布的数据集进行了系统调查，手动审查了过去五年间来自三个旗舰会议（LAK、EDM、AIED）的1125篇论文，发现、分类并分析了用于204篇出版物中的172个数据集。本研究呈现了迄今为止最全面的开放教育数据集收集与分析成果，以及最精细的分类体系。在识别的172个数据集中，有143个未被此前任何学习分析开放数据调查所收录。我们提供了关于数据集背景、分析方法、使用情况及其他属性的深入见解。基于此调查，我们总结了该领域当前存在的不足。此外，我们提出了实用建议、指导意见及八项以PRACTICE为缩写的指南（附检查清单），以帮助研究者发布数据。最后，我们共享了原始数据集：一份详细标注所发现数据集及对应出版物的清单。我们期望这些发现能进一步推动学习分析社群及其他领域对开放数据实践的采纳。