Student and Staff Perspectives on the Use of Big Data in the Tertiary Education Sector: A Scoping Review and Reflection on the Ethical Issues

Abstract

While universities routinely use student data to monitor and predict student performance, there has been limited engagement with student and staff views, social and ethical issues, policy development, and ethical guidance. We reviewed peer-reviewed and grey-literature articles of 2007 to 2018 describing the perspectives of staff and students in tertiary education on the use of student-generated data in data analytics, including learning analytics. We used an ethics framework to categorize the findings. There was considerable variation but generally low awareness and understanding amongst students and staff about the nature and extent of data collection, data analytics, and use of predictive analytics. Staff and students identified potential benefits but also expressed concerns about misinterpretation of data, constant surveillance, poor transparency, inadequate support, and potential to impede active learning. This review supports the contention that consideration of ethical issues has failed to keep pace with the development of predictive analytics in the tertiary sector.

Exploring students’ expectations of learning analytics: A person-centered approach

“…Students' expectations of LA are critical for any successful adoption of them in everyday teaching and learning practices. However, there has been a limited -in different ways (e.g., in terms of the limited sample size, the contexts examined, and the methods of analysis employed) -body of research that uncovers students' views (Braunack-Mayer et al, 2020;West et al, 2020), including their related expectations of LA services, in LA decision-making, and few studies that empower students to be co-creators of LA services (e.g., Ifenthaler & Schumacher, 2016;de Quincey et al, 2019;West et al, 2020). Yet, as stressed by Whitelock-Wainwright et al (2021), LA researchers have made some relevant research attempts in this direction (e.g., Arnold & Sclater, 2017;Ifenthaler & Schumacher, 2016;Schumacher & Ifenthaler, 2018;Tsai et al, 2020;West et al, 2020;Whitelock-Wainwright et al, 2021).…”

Perspectives on computing ethics: a multi-stakeholder analysis

“…A systematic literature review approach was employed in selecting relevant literature from a number of key areas that represent some notable present-day computing ethics topics and challenges (similar reviews have been undertaken by researchers such as Braunack-Mayer et al , 2020; Saltz et al , 2019; Saltz and Dewar, 2019). These key areas, also highlighted by Kumar et al (2020), focus on the overlap between three key areas in contemporary computing ethics, the areas of data science, AI and pervasive computing (including surveillance and privacy), thus the focus of this literature review is to critically examine those three areas, and to explore the themes that have emerged in each of those domains in the past five years.…”

Student retention in higher education: the influences of organizational identification and institution reputation on student satisfaction and behaviors

“…The extant literature suggests that university reputation may boost student satisfaction (e.g. Braunack-Mayer et al, 2020;Elsharnouby, 2015;Wilkins and Huisman, 2015) and organizational identification (Heffernan et al, 2018).…”

“…Several researchers have declared a link between an institution's reputation and student satisfaction (Braunack-Mayer et al, 2020;Elsharnouby, 2015;Wilkins and Huisman, 2015). Reputation may influence an individual's overall impressions of a university and the quality of its education, and Mai (2005) found that these impressions are related to satisfaction more than other specific service dimensions.…”