Data privacy and big data in higher education research: What does the future hold?

Abstract

Big data analytics tools are now being used in higher education (HE) institutions. Learning analytics uses ways to study student behaviors and improve instructional, curricular, and support resources and learning environments by gathering information about students navigating information systems. However, learning analytics raises serious ethical and policy concerns about student privacy. Data confidentiality becomes even more of a concern in higher education research, affecting both qualitative and quantitative analysis. According to the current situation, the impact of massive data in higher education research, as well as the most operative approaches to reconciling huge sample practice with confidentiality limitations, with a focus on a data collector, research participants, and data operator. This commentary aims to qualitatively explore current difficulties in data privacy, secrecy, participants with the agreement, and privacy in data-oriented HEI’s research. The findings suggest that Big Data-centric HE research is becoming a recognized research standard but that it must first solve significant data privacy concerns before being broadly used. It is suggested that present policies focus on data collection and disclosure rather than use, which has procedural consequences for the complex nature of HE study and the types of research data gathered. It is clear by examining several reasons that there is a need to rethink of data confidentiality and access to personal information in HE research.