Data Analytics in Higher Education: Key Concerns and Open Questions

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas

Student Privacy in Learning Analytics: An Information Ethics Perspective

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods

Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy

By tracking, aggregating, and analyzing student profiles along with students’ digital and analog behaviors captured in information systems, universities are beginning to open the black box of education using learning analytics technologies. However, the increase in and usage of sensitive and personal student data present unique privacy concerns. I argue that privacy-as-control of personal information is autonomy promoting, and that students should be informed about these information flows and to what ends their institution is using them. Informed consent is one mechanism by which to accomplish these goals, but Big Data practices challenge the efficacy of this strategy. To ensure the usefulness of informed consent, I argue for the development of Platform for Privacy Preferences (P3P) technology and assert that privacy dashboards will enable student control and consent mechanisms, while providing an opportunity for institutions to justify their practices according to existing norms and values

Advising the whole student: eAdvising analytics and the contextual suppression of advisor values

Institutions are applying methods and practices from data analytics under the umbrella term of “learning analytics” to inform instruction, library practices, and institutional research, among other things. This study reports findings from interviews with professional advisors at a public higher education institution. It reports their perspective on their institution’s recent adoption of eAdvising technologies with prescriptive and predictive advising affordances. The findings detail why advisors rejected the tools due to usability concerns, moral discomfort, and a belief that using predictive measures violated a professional ethical principle to develop a comprehensive understanding of their advisees. The discussion of these findings contributes to an emerging branch of educational data mining and learning analytics research focused on social and ethical implications. Specifically, it highlights the consequential effects on higher education professional communities (or “micro contexts”) due to the ascendancy of learning analytics and data-driven ideologies

Learning Analytics and Its Paternalistic Influences

Learning analytics is a technology that employs paternalistic nudging techniques and predictive measures. These techniques can limit student autonomy, may run counter to student interests and preferences, and do not always distribute benefits back to students–in fact some harms may actually accrue. The paper presents three cases of paternalism in learning analytics technologies, arguing that paternalism is an especially problematic concern for higher education institutions who espouse liberal education values. Three general recommendations are provided that work to promote student autonomy and choice making as a way to protect against risks to student academic freedom

Reconsidering data in learning analytics: opportunities for critical research using a documentation studies framework

In this article, we argue that the contributions of documentation studies can provide a useful framework for analyzing the datafication of students due to emerging learning analytics (LA) practices. Specifically, the concepts of individuals being ‘made into’ data and how that data is ‘considered as’ can help to frame vital questions concerning the use of student data in LA. More specifically, approaches informed by documentation studies will enable researchers to address the sociotechnical processes underlying how students are constructed into data, and ways data about students are considered and understood. We draw on these concepts to identify and describe three areas for future research in LA. With the description of each area, we provide a brief analysis of current practices in American higher education, highlighting how documentation studies enables deeper analytical digging

The Temptation of Data-enabled Surveillance: Are Universities the Next Cautionary Tale?

There is increasing concern about “surveillance capitalism,” whereby for-profit companies generate value from data, while individuals are unable to resist (Zuboff 2019). Non-profits using data-enabled surveillance receive less attention. Higher education institutions (HEIs) have embraced data analytics, but the wide latitude that private, profit-oriented enterprises have to collect data is inappropriate. HEIs have a fiduciary relationship to students, not a narrowly transactional one (see Jones et al, forthcoming). They are responsible for facets of student life beyond education. In addition to classrooms, learning management systems, and libraries, HEIs manage dormitories, gyms, dining halls, health facilities, career advising, police departments, and student employment. HEIs collect and use student data in all of these domains, ostensibly to understand learner behaviors and contexts, improve learning outcomes, and increase institutional efficiency through “learning analytics” (LA). ID card swipes and Wi-Fi log-ins can track student location, class attendance, use of campus facilities, eating habits, and friend groups. Course management systems capture how students interact with readings, video lectures, and discussion boards. Application materials provide demographic information. These data are used to identify students needing support, predict enrollment demands, and target recruiting efforts. These are laudable aims. However, current LA practices may be inconsistent with HEIs’ fiduciary responsibilities. HEIs often justify LA as advancing student interests, but some projects advance primarily organizational welfare and institutional interests. Moreover, LA advances a narrow conception of student interests while discounting privacy and autonomy. Students are generally unaware of the information collected, do not provide meaningful consent, and express discomfort and resigned acceptance about HEI data practices, especially for non-academic data (see Jones et al. forthcoming). The breadth and depth of student information available, combined with their fiduciary responsibility, create a duty that HEIs exercise substantial restraint and rigorous evaluation in data collection and use

The Syllabus as a Student Privacy Document in an Age of Learning Analytics

Purpose The purpose of this paper is to reveal how instructors discuss student data and information privacy in their syllabi. Design/methodology/approach The authors collected a mixture of publicly accessible and privately disclosed syllabi from 8,302 library and information science (LIS) courses to extract privacy language. Using privacy concepts from the literature and emergent themes, the authors analyzed the corpus. Findings Most syllabi did not mention privacy (98 percent). Privacy tended to be mentioned in the context of digital tools, course communication, policies and assignments. Research limitations/implications The transferability of the findings is limited because they address only one field and professional discipline, LIS, and address syllabi for only online and hybrid courses. Practical implications The findings suggest a need for professional development for instructors related to student data privacy. The discussion provides recommendations for creating educational experiences that support syllabi development and constructive norming opportunities. Social implications Instructors may be making assumptions about the degree of privacy literacy among their students or not value student privacy. Each raises significant concerns if privacy is instrumental to intellectual freedom and processes critical to the educational experience. Originality/value In an age of educational data mining and analytics, this is one of the first studies to consider if and how instructors are addressing student data privacy in their courses, and the study initiates an important conversation for reflecting on privacy values and practices

The Development of an Undergraduate Data Curriculum: A Model for Maximizing Curricular Partnerships and Opportunities

The article provides the motivations and foundations for creating an interdisciplinary program between a Library and Information Science department and a Human-Centered Computing department. The program focuses on data studies and data science concepts, issues, and skill sets. In the paper, we analyze trends in Library and Information Science curricula, the emergence of data-related Library and Information Science curricula, and interdisciplinary data-related curricula. Then, we describe the development of the undergraduate data curriculum and provide the institutional context; discuss collaboration and resource optimization; provide justifications and workforce alignment; and detail the minor, major, and graduate opportunities. Finally, we argue that the proposed program holds the potential to model interdisciplinary, holistic data-centered curriculum development by complimenting Library and Information Science traditions (e.g., information organization, access, and ethics) with scholarly work in data science, specifically data visualization and analytics. There is a significant opportunity for Library and Information Science to add value to data science and analytics curricula, and vice versa