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：开放数据集在三个数据科学与教育交叉的研究领域中发挥着关键作用：学习分析、教育数据挖掘和教育人工智能。这些领域的研究者应用计算方法分析教育情境中的数据，旨在更好地理解并改进教学与学习过程。在研究论文中同时提供开放数据集有助于支持研究的可复现性、协作性及研究结果的可信度。同时，这也为作者带来个人收益，例如更高的可见度、可信度和引用潜力。尽管存在这些优势，学习分析研究社区（尤其是在其旗舰会议场合）中开放数据集的可用性及相关实践现状仍不明确。我们对学习分析领域研究论文中发布的可用数据集进行了系统调查。我们人工审查了过去五年间三大旗舰会议（LAK、EDM和AIED）的1,125篇论文，从中发现、分类并分析了204篇出版物中使用的172个数据集。本研究提出了迄今为止最全面的开放教育数据集收集与分析框架，并提供了最细致的分类体系。在识别出的172个数据集中，有143个未被先前任何学习分析开放数据调查所收录。我们深入剖析了这些数据集的情境背景、分析方法、使用状况及其他属性。基于本次调查，我们总结了该领域当前存在的不足。此外，我们以PRACTICE为缩写提出了包含8项条款的实用建议、指导原则及核对清单，以帮助研究者规范发布数据。最后，我们共享了原创数据集：一份详细记载已发现数据集及对应出版物的标注清单。我们期望这些发现能推动开放数据实践在学习分析社区乃至更广范围的进一步普及。