@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 {1742,
	title = {CPFinder: Finding an unknown Caller{\textquoteright}s profession from anonymized mobile phone data},
	journal = {Digital Communications and Networks},
	volume = {8},
	year = {2022},
	month = {08/2022},
	pages = {324{\textendash}332},
	abstract = {<p>Identifying an unfamiliar caller{\textquoteright}s profession is important to protect citizens{\textquoteright} personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users{\textquoteright} mobile phones may exhibit their applications preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users{\textquoteright} mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder {\textemdash}- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0\% and 92.4\% for multiclass and binary classifications, respectively.</p>
},
	doi = {10.1016/j.dcan.2021.08.003},
	url = {https://www.sciencedirect.com/science/article/pii/S2352864821000559},
	author = {Jiaquan Zhang and Hui Chen and Xiaoming Yao and Xiaoming Fu}
}
@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}
}
@conference {1720,
	title = {Identifying unfamiliar callers{\textquoteright} professions from privacy-preserving mobile phone data},
	booktitle = {16th IEEE International Conference on Mobility, Sensing and Networking (MSN 2020)},
	year = {2020},
	month = {12/2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Tokyo, Japan},
	author = {Jiaquan Zhang and Xiaoming Yao and Xiaoming Fu}
}
