SLP: A Multi-Dimensional and Consecutive Dataset from K-12 Education

Abstract

Learning is a complicated process jointly influenced by multiple factors, such as learner’s personal characteristics, family background and school environment. However, the existing public datasets in K-12 education domain seldom fully cover the heterogeneous dimensions, which greatly hinders the research on fully analyzing and understanding the learners and their learning process. In this work, we report a dataset that includes the learners’ demographic information, psychometric intelligence scores as well as their family-school back- ground information. Furthermore, the dataset records the learners’ academic performance data on 8 different subjects in 3 consecutive years. This multi-dimensional dataset from K-12 education can be a valuable information source for learning analytics and would benefit the cross- disciplinary research in education on a broader canvas. The dataset has been publicly available for the research purpose at https://aic-fe.bnu.edu.cn/en/data/index.html.