@article {1790,
	title = {Alleviating Educational Inequality in Math with the Aid of Online Shadow Education - The impact of equal access and equal quality mechanisms},
	journal = {Education and Information Technologies},
	year = {2023},
	month = {09/2023},
	abstract = {<p>This paper aims to investigate whether online private supplementary education, also known as shadow education, can alleviate educational inequality and what types of mechanisms can help alleviate it. We investigate this using an online learning platform dataset (3,603 anonymous students from China) with additional data from multiple sources and employ geospatial analyses to measure students{\textquoteright} socioeconomic, regional and rural/urban inequalities. We find that taking part in online education narrows the performance gap in mathematics between privileged and unprivileged students in terms of school status and regional disparity in China. Theoretically, two micro-level mechanisms explain the alleviating differences: 1) equal access mechanism: students from lower city tiers and low-status schools show greater score improvement when having equal access to online education; 2) equal quality mechanism: students from rural regions improve their in-class rankings more substantially if they receive equal quality online education with the same tutoring and learning environment alongside urban students. This study comprehensively looks at the different effects of two mechanisms of online education{\textemdash}equal access and equal quality{\textemdash}for alleviating various types of inequality. Thus we speak to both educational inequality and digital inequality theory, finding that equal access to online education is not enough for rural students, as they also need access to classes of equal educational quality with their urban counterparts.</p>
},
	doi = {10.1007/s10639-023-12214-5},
	url = {https://doi.org/10.1007/s10639-023-12214-5},
	author = {Xin Gao and Jar-Der Luo and Hui Chen and Yuanyi Zhen and Jiaquan Zhang and Xiaoming Fu}
}
@article {1792,
	title = {The influencing factors and predictability of primary school students{\textquoteright} learning performance in online supplementary classes},
	journal = {Education and Information Technologies},
	year = {2023},
	month = {10/2023},
	abstract = {<p>Online supplementary education has been prevalent in recent years due to the advent of technology (e.g., live streaming) and the COVID-19 pandemic. However, the performance of students in this mode of education varies greatly, and the underlying reasons are yet to be investigated. This study aims to understand the impact of various factors and giving their quantified importance, including student information, family information, and course information by applying Machine Learning method to one of the world{\textquoteright}s largest online learning platforms. Big data analysis is employed for this purpose via leveraging the abundance of data generated from online education platforms, providing insights that are not attainable through conventional methods such as panel surveys or questionnaires used in offline education. The findings indicate that the most significant factor affecting student performance is the disparity in access to educational resources and the socioeconomic status of families. Finally, we predict students{\textquoteright} online learning performance by using explainable machine learning with different groups of features. Our results indicate approximately 70\% accuracy in predicting students{\textquoteright} performance progress.</p>
},
	doi = {10.1007/s10639-023-12246-x},
	url = {https://link.springer.com/article/10.1007/s10639-023-12246-x},
	author = {Zhengze Li and Hui Chen and Xin Gao}
}
@conference {1731,
	title = {A Data-Driven Analysis of  K-12 Students{\textquoteright} Participation and Learning Performance in an Online Supplementary Learning Platform},
	booktitle = {The 30th IEEE International Conference on Computer Communications and Networks (ICCCN 2021)},
	year = {2021},
	month = {07/2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Athens, Greece},
	author = {Jiaquan Zhang and Xin Gao and Hui Chen and Lei Chen and Liang Wang and Jar-Der Luo and Xiaoming Fu}
}
