Educational Data Mining (EDM) and Learning Analytics (LA) are among the top buzzwords of the EdTech scene right now.

Mount St. Mary’s use of predictive analytics to encourage at-risk students to drop out to elevate the retention rate reveals how analytics can be abused without student knowledge and consent

The learning analytics and education data mining discussed in this handbook hold great promise. At the same time, they raise important concerns about security, privacy, and the broader consequences of big data-driven education. This chapter describes the regulatory framework governing student data, its neglect of learning analytics and educational data mining, and proactive approaches to privacy. It is less about conveying specific rules and more about relevant concerns and solutions. Traditional student privacy law focuses on ensuring that parents or schools approve disclosure of student information. They are designed, however, to apply to paper “education records,” not “student data.” As a result, they no longer provide meaningful oversight. The primary federal student privacy statute does not even impose direct consequences for noncompliance or cover “learner” data collected directly from students. Newer privacy protections are uncoordinated, often prohibiting specific practices to disastrous effect or trying to limit “commercial” use. These also neglect the nuanced ethical issues that exist even when big data serves educational purposes. I propose a proactive approach that goes beyond mere compliance and includes explicitly considering broader consequences and ethics, putting explicit review protocols in place, providing meaningful transparency, and ensuring algorithmic accountability. Export Citation: Plain Text (APA

Over the course of many years, every school has refined and perfected the connections LMSs have into a wide variety of other campus systems including authentication systems, identity management systems, student information systems, assessment-related learning tools, library systems, digital textbook systems, and other content repositories. APIs and standards have decreased the complexity of supporting these connections, and over time it has become easier and more common to connect LMSs to – in some cases – several dozen or more other systems. This level of integration gives LMSs much more utility than they have out of the box – and also more “stickiness” that causes them to become harder to move away from. For LMS alternatives, achieving this same level of connectedness, particularly considering how brittle these connections can sometimes become over time, is a very difficult thing to achieve.

privacy controls over student data,

This has much in common with a customer relationship management system and facilitates the workflow around interventions as well as various visualisations.  It’s unclear how the at risk metric is calculated but a more sophisticated predictive analytics engine might help in this regard.

Outside of the classroom, universities can use connected devices to monitor their students, staff, and resources and equipment at a reduced operating cost, which saves everyone money.

Devices connected to the cloud allow professors to gather data on their students and then determine which ones need the most individual attention and care.

For G Suite users in primary/secondary (K-12) schools, Google does not use any user personal information (or any information associated with a Google Account) to target ads.

Application Modern higher education institutions have unprecedentedly large and detailed collections of data about their students, and are growing increasingly sophisticated in their ability to merge datasets from diverse sources. As a result, institutions have great opportunities to analyze and intervene on student performance and student learning. While there are many potential applications of student data analysis in the institutional context, we focus here on four approaches that cover a broad range of the most common activities: data-based enrollment management, admissions, and financial aid decisions; analytics to inform broad-based program or policy changes related to retention; early-alert systems focused on successful degree completion; and adaptive courseware.

the risk of re-identification increases by virtue of having more data points on students from multiple contexts

Responsible Use

Data sharing over open-source platforms can create ambiguous rules about data ownership and publication authorship, or raise concerns about data misuse by others, thus discouraging liberal sharing of data.

captures values such as transparency and student autonomy

Activities such as time spent on task and discussion board interactions are at the forefront of research.

Research: Student data are used to conduct empirical studies designed primarily to advance knowledge in the field, though with the potential to influence institutional practices and interventions. Application: Student data are used to inform changes in institutional practices, programs, or policies, in order to improve student learning and support. Representation: Student data are used to report on the educational experiences and achievements of students to internal and external audiences, in ways that are more extensive and nuanced than the traditional transcript.

often private companies whose technologies power the systems universities use for predictive analytics and adaptive courseware

the use of data in scholarly research about student learning; the use of data in systems like the admissions process or predictive-analytics programs that colleges use to spot students who should be referred to an academic counselor; and the ways colleges should treat nontraditional transcript data, alternative credentials, and other forms of documentation about students’ activities, such as badges, that recognize them for nonacademic skills.

“We need much more honesty, about what data is being collected and about the inferences that they’re going to make about people. We need to be able to ask the university ‘What do you think you know about me?’”

what do we do with that information?

turn students and faculty into data points

E-texts could record how much time is spent in textbook study. All such data could be accessed by the LMS or various other applications for use in analytics for faculty and students.”

not as a way to monitor and regulate

demanded by education policies — for more data

data being collected about individuals for purposes unknown to these individuals

Data collection on students should be considered a joint venture, with all parties — students, parents, instructors, administrators — on the same page about how the information is being used.

there is some disparity and implicit bias