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

History in a Jar: The Taste and the Trauma of Gefilte Fish

In 2004, a character in Tova Mirvis’s novel The Outside World presciently remarked, “Gefilte fish can be the next sushi… Because people are hungry for something authentic… They miss the past. Even if they never had it, they still miss it.” Twelve years later, Liz Alpern and Jeffrey Yoskowitz released their cookbook, The Gefilte Manifesto: New Recipes for Old World Jewish Foods, to both popular and critical acclaim. The trajectory of Jewish food in America has changed dramatically in the last two decades, calling into question the ever-fraught relationship between “kosher” and “Jewish” food. While gefilte fish has its origins in medieval Germany, this recipe for stuffed fish evolved over centuries until it became the more recognizable fish quenelle popular among Eastern European and American Jews at the turn of the twentieth century. A laborious task to be sure, midcentury American manufacturing lightened this undertaking by mass-producing these gelatinous fish balls in glass jars – ultimately becoming a grocery store staple that stirs both nostalgia and nausea in American Jewish memory. An insider’s dish, gefilte fish – once a means of thriftily stretching a meal – has been elevated to an almost sacred, elegant addition to a Jewish menu, an appetizer that elicits both delight and disgust. By looking at gefilte fish as a historically significant part of Jewish cuisine, as well as its modern innovations in the United States, we can see changing Jewish narratives regarding acculturation, innovation, and the place of nostalgia on the Jewish American plate

The Experience of Gifted Girls Transitioning from Elementary School to Sixth and Seventh Grade: A Grounded Theory

This research explored the experiences of gifted girls transitioning from elementary school to sixth and seventh grade. The current literature indicates that gifted girls often struggle emotionally during this transition. Seven research participants were selected and interviewed over a four-month period. Grounded theory methodology was used to analyze data, generate subsequent interview questions, and build theory. This study indicated that these gifted girls transition was facilitated by their strong identities, which enabled them to balance their social and academic lives. Their strong identities allowed them to choose strategies that helped them build connections with both gifted and nongifted peers. These relationships contributed significantly to their sense of self, and in turn supported their transition experiences

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

A matter of trust: : Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students

A clinical records survey

A clinical records surve

Spin effects in the magneto-drag between double quantum wells

We report on the selectivity to spin in a drag measurement. This selectivity to spin causes deep minima in the magneto-drag at odd fillingfactors for matched electron densities at magnetic fields and temperatures at which the bare spin energy is only one tenth of the temperature. For mismatched densities the selectivity causes a novel 1/B-periodic oscillation, such that negative minima in the drag are observed whenever the majority spins at the Fermi energies of the two-dimensional electron gasses (2DEGs) are anti-parallel, and positive maxima whenever the majority spins at the Fermi energies are parallel.Comment: 4 pages, 3 figure