Presentation Title

Understanding The Privacy Methods, Implications, and Challenges of Educational Data Mining.

Presenter Information

Tyler BlancoFollow
Liam SmithFollow

Panel Name

Cybersecurity, Privacy, and Artificial Intelligence

Location

Lecture Center Concourse

Start Date

3-5-2019 3:00 PM

End Date

3-5-2019 5:00 PM

Presentation Type

Poster Session

Abstract

Studies on educational data mining have massively advanced the analytical understanding of student learning, outcome predictions, student modeling, and better evaluation. While the information retrieved can improve educational outcomes, such studies must not break the wall of trust or compromise student privacy. To this end, it’s imperative to understand the methods of how privacy is ensured in the field of educational data mining and learning analytics, the limitations of these strategies, the complications that may follow, and the possible resolutions. In this project, we take a systematic approach to review studies in the educational data mining field. Initially, we examine common data types collected in these studies. The characteristics of that data are examined, and the potential privacy implications of it. The security/privacy-preserving methods are surveyed in said studies. Then we provide a discussion on the limitations of these privacy approaches, and identify open challenges. Finally, we discuss future avenues for further privacy research in this field. Our research will serve as a roadmap for those that are looking to understand the current data privacy concerns in the educational data mining field. Additionally, it will also help the researchers in the field understand the implications of their data collection and possible privacy protection methods.

Select Where This Work Originated From

Course assignment/project

First Faculty Advisor

Liyue Fan

First Advisor Email

liyuefan@albany.edu

First Advisor Department

Digital Forensics

The work you will be presenting can best be described as

Finished or mostly finished by conference date

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May 3rd, 3:00 PM May 3rd, 5:00 PM

Understanding The Privacy Methods, Implications, and Challenges of Educational Data Mining.

Lecture Center Concourse

Studies on educational data mining have massively advanced the analytical understanding of student learning, outcome predictions, student modeling, and better evaluation. While the information retrieved can improve educational outcomes, such studies must not break the wall of trust or compromise student privacy. To this end, it’s imperative to understand the methods of how privacy is ensured in the field of educational data mining and learning analytics, the limitations of these strategies, the complications that may follow, and the possible resolutions. In this project, we take a systematic approach to review studies in the educational data mining field. Initially, we examine common data types collected in these studies. The characteristics of that data are examined, and the potential privacy implications of it. The security/privacy-preserving methods are surveyed in said studies. Then we provide a discussion on the limitations of these privacy approaches, and identify open challenges. Finally, we discuss future avenues for further privacy research in this field. Our research will serve as a roadmap for those that are looking to understand the current data privacy concerns in the educational data mining field. Additionally, it will also help the researchers in the field understand the implications of their data collection and possible privacy protection methods.