'What’s the Problem with Learning Analytics?' by Neil Selwyn in (2019) 6(3)
Journal of Learning Analytics 11–19 comments
This article summarizes some emerging concerns as learning analytics become implemented throughout education. The article takes a sociotechnical perspective — positioning learning analytics as shaped by a range of social, cultural, political, and economic factors. In this manner, various concerns are outlined regarding the propensity of learning analytics to entrench and deepen the status quo, disempower and disenfranchise vulnerable groups, and further subjugate public education to the profit-led machinations of the burgeoning “data economy.” In light of these charges, the article briefly considers some possible areas of change. These include the design of analytics applications that are more open and accessible, that offer genuine control and oversight to users, and that better reflect students’ lived reality. The article also considers ways of rethinking the political economy of the learning analytics industry. Above all, learning analytics researchers need to begin talking more openly about the values and politics of data-driven analytics technologies as they are implemented along mass lines throughout school and university contexts.
'It's My Data! Tensions Among Stakeholders of a Learning Analytics Dashboard' by Kaiwen Sun, Abraham H. Mhaidli, Sonakshi Watel, Christopher A. Brooks, and Florian Schaub in
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2019) 1-14 comments
Early warning dashboards in higher education analyze student data to enable early identification of underperforming students, allowing timely interventions by faculty and staff. To understand perceptions regarding the ethics and impact of such learning analytics applications, we conducted a multi-stakeholder analysis of an early-warning dashboard deployed at the University of Michigan through semi-structured interviews with the system's developers, academic advisors (the primary users), and students. We identify multiple tensions among and within the stakeholder groups, especially with regard to awareness, understanding, access and use of the system. Furthermore, ambiguity in data provenance and data quality result in differing levels of reliance and concerns about the system among academic advisors and students. While students see the system's benefits, they argue for more involvement, control, and informed consent regarding the use of student data. We discuss our findings' implications for the ethical design and deployment of learning analytics applications in higher education. Early warning dashboards in higher education analyze student data to enable early identification of underperforming students, allowing timely interventions by faculty and staff. To understand perceptions regarding the ethics and impact of such learning analytics applications, we conducted a multi-stakeholder analysis of an early-warning dashboard deployed at the University of Michigan through semi-structured interviews with the system's developers, academic advisors (the primary users), and students. We identify multiple tensions among and within the stakeholder groups, especially with regard to awareness, understanding, access, and use of the system. Furthermore, ambiguity in data provenance and data quality result in differing levels of reliance and concerns about the system among academic advisors and students. While students see the system's benefits, they argue for more involvement, control, and informed consent regarding the use of student data. We discuss our findings' implications for the ethical design and deployment of learning analytics applications in higher education.
'Big Data in Education. A Bibliometric Review' by José-Antonio Marín-Marín, Jesús López-Belmonte, Juan-Miguel Fernández-Campoy and José-María Romero-Rodríguez in (2019) 8(8)
Social Sciences 223 comments
The handling of a large amount of data to analyze certain behaviors is reaching a great popularity in the decade 2010–2020. This phenomenon has been called Big Data. In the field of education, the analysis of this large amount of data, generated to a greater extent by students, has begun to be introduced in order to improve the teaching–learning process. In this paper, it was proposed as an objective to analyze the scientific production on Big Data in education in the databases Web of Science (WOS), Scopus, ERIC, and PsycINFO. A bibliometric study was carried out on a sample of 1491 scientific documents. Among the results, the increase in publications in 2017 and the configuration of certain journals, countries and authors as references in the subject matter stand out. Finally, potential explanations for the study findings and suggestions for future research are discussed.
'Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking' by Priscilla M. Regan and Jolene Jesse in (2019) 21(3)
Ethics and Information Technology 167-179 comments
With the increase in the costs of providing education and concerns about financial responsibility, heightened consideration of accountability and results, elevated awareness of the range of teacher skills and student learning styles and needs, more focus is being placed on the promises offered by online software and educational technology. One of the most heavily marketed, exciting and controversial applications of edtech involves the varied educational programs to which different students are exposed based on how big data applications have evaluated their likely learning profiles. Characterized most often as ‘personalized learning,’ these programs raise a number of ethical concerns especially when used at the K-12 level. This paper analyzes the range of these ethical concerns arguing that characterizing them under the general rubric of ‘privacy’ oversimplifies the concerns and makes it too easy for advocates to dismiss or minimize them. Six distinct ethical concerns are identified: information privacy; anonymity; surveillance; autonomy; non-discrimination; and ownership of information. Particular attention is paid to whether personalized learning programs raise concerns similar to those raised about educational tracking in the 1950s. The paper closes with discussion of three themes that are important to consider in ethical and policy discussions.
The last 10 years have witnessed an explosion of new educational technologies (edtech), some touting amazing potential to reach the next generation with new learning methods that will teach not only content, be it history, mathematics or engineering, but also intra- and inter-personal competencies, such as resilience and teamwork. The edtech sector is actively marketing these learning tools, especially to elementary and secondary schools, although the efficacy of technology enhanced learning is still under investigation. Edtech applications have appeared at a political, policy, and commercial moment favorable to the capabilities and advantages offered. The increase in the federal, state and local costs of providing K-12 education and government and voter concerns about financial responsibility generate interest in new techniques that promise to improve efficiency of educational operations. Focus on student achievement and the rankings of US schools with those of other countries has led to heightened consideration of accountability and results. Elevated awareness of the range of teacher skills, as well as variations in student learning styles and needs, has drawn attention to the value of understanding unique characteristics of students and teachers. As a result, the K-12 school environment is conducive to the promises offered by online software and edtech. Edtech companies recognize the huge market offered by K-12 education—an arena that has a vast and renewable population base, but also a particularly vulnerable population involving minor children who experience a range of developmental milestones during the K-12 years.
This uptick in adoption of a variety of edtech applications at the K-12 level has also generated myriad policy debates, including proposed updates to existing federal laws and the introduction and adoption of numerous new state laws. Much of the policy debate is subsumed under the label of “privacy,” although there are a range of ethical issues associated with edtech applications that have not received the same amount of consideration as privacy, and some issues have been conflated with privacy. Privacy is certainly an issue, as the use of edtech entails collection of more, and more granular, information about students, teachers, and families, as well as administrative details regarding the functioning of educational institutions. Edtech applications enable sophisticated searching and analysis of collected information linking changes in the education arena to the larger debates about the challenges of big data generally. One of the most problematic aspects of edtech, and least addressed from a policy perspective however, involves the capability of edtech to deliver more personalized learning based on the needs and skill levels of individual students.
Personalized learning applications are currently among the most heavily-marketed, exciting and controversial applications of edtech. These applications involve evaluating students likely learning profiles on applications that use big data to categorize individual learning styles and then direct appropriate learning activities to those students. Known under several labels—personalized learning, student-centered learning, and adaptive learning—they are advocated by edtech companies and foundations, including the Bill and Melinda Gates Foundation and the Chan Zuckerberg Foundation. In 2016, 97% of school districts surveyed by the Education Week Research Center indicated they were investing in some form of personalized learning (Herold 2017). Although exactly what types of programs constitute personalized learning is not always clear and whether and how much these programs incorporate edtech is hard to determine, RAND in the third of its reports on personalized learning cautions that the evidence for the effectiveness of personalized learning is currently weak and needs more research in a range of school settings (Pane et al. 2017).
A critical ethical concern raised with personalized learning is whether such programs constitute tracking and sorting of students that might be considered discriminatory. The history of tracking in the United States is especially problematic, suggesting the need for caution when sorting children. Student tracking in the 1950s resulted in classrooms that were often divided by race, ethnicity, gender and class. Such tracking was glaringly obvious to parents, students, teachers and administrators—and thus the implications and wisdom of tracking became subjects of policy and social debate. In contrast, the student tracking that appears to be occurring in 2018 is hidden from the view of students, parents and even teachers as it takes place behind computer screens. The extent to which students might recognize they are being tracked through computer programs, and the impact that might have on learning outcomes is rarely discussed or researched. Similarly, the extent to which edtech software embeds subtle discrimination is also unclear, despite the current dialog about algorithmic bias.
This article seeks to first analyze the range of ethical issues raised by the increased use of edtech and big data in school systems throughout the United States—how these issues are framed; whether the major concerns are receiving the appropriate level of attention and analysis; and what policy implications there are around how issues are being presented. Second, the paper briefly explores policy responses to big data educational innovations—what discourse has resulted; and what policy trends are emerging. Third, the paper is particularly interested in personalized learning systems and whether and how they might incorporate categories such as race, gender, ethnicity, and class, as well as their intersections, and whether discussions about these systems mirror the concerns of the policy and social debates in the 1950s about educational tracking. Finally, the paper closes with some themes that are important to consider in ethical and policy discussions addressing personalized learning systems.
