Although we're currently nowhere near this idea, how can businesses, educational institutions, and governments alike not consider the importance of giving individuals control over their digital archives? Or their learning analytics data?17

more than just a student's schoolwork; they should also include personal photos, videos, transcripts, X-rays, dental records, police records, and a million other digital life-bits.

privacy concerns

A simple search function allows teachers to search for specific text strings found in either the xAPI statements or in the text responses typed in by students.

creates a highly personalized experience for the children while simultaneously alleviating privacy concerns.

xAPI and Next Generation Learning Get the right data about the learning experience and its impact on performance. We’re among the early adopters and leaders in the Experience API (xAPI) and its application in performance & analytics. As winners of the xAPI Hyperdrive, eLearning Guild Demofest and Brandon Hall Awards with our xAPI-based solutions, we’re inspiring others with fresh thinking. As hosts of the xAPI Learning Cohort we’re supporting hundreds of pioneers and experimenters in learning and working with the xAPI.

xAPI is a json based data structure that's for expressing the actions taken by a user. It's popular for tracking activity across websites because of it having a standard base schema with flexibility for providing contextual information based on use-case.

Currently, Canvas and Sakai are the only LMSs reviewed which has somesupport for xAPI (emphasis on some). Blackboard, D2L, Sakai and Canvas all have support for IMS Caliper, a more edu specific format.

If you used our Physical web beacon today to login, you were generating xAPI statements.

H5P elements naturally emit xAPI statements

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

Often our solutions must co-exist with existing systems. That’s why we also invest time and money in emerging standards, like xAPI or Open Badges, to help connect our platforms together into a single ecosystem for personal, social and data-driven learning.

Learning Analytics

Information from this will be used to develop learning analytics software features, which will have these functions: Description of learning engagement and progress, Diagnosis of learning engagement and progress, Prediction of learning progress, and Prescription (recommendations) for improvement of learning progress.

full Caliper Analytics compliance

A suite of complex online tools was designed to assist with analyzing student behavior in the LMS and with the development of interactive learning objects at the activity level.

LRSs will typically only have minor data analysis built in as it's specific to the type of information you are trying to track.

Adoption should not be ‘one-or-the-other’, instead it is a ‘horses-for-courses’ decision;

Add xAPI -> https://h5p.org/documentation/x-api Tiki should become a Learning Record Store or a FLOSS solution picked for WikiSuite.

By giving student data to the students themselves, and encouraging active reflection on the relationship between behavior and outcomes, colleges and universities can encourage students to take active responsibility for their education in a way that not only affects their chances of academic success, but also cultivates the kind of mindset that will increase their chances of success in life and career after graduation.

If students do not complete the courses they need to graduate, they can’t progress.

Ethics and privacy in learning analytics

The flexibility and social nature of how technology infuses other aspects of our lives is not captured by the model of Personalized Instruction, which focuses on the isolated individual’s personal path to a fixed end-point. To truly harness the power of modern technology, we need a new vision for educational technology (Enyedy, 2014: 16).

There is no firm evidence that adaptive learning systems are leading to better course grades or course completion.

A look at the Hype Cycle (see here for Wikipedia’s entry on the topic and for criticism of the hype of Hype Cycles) of the IT research and advisory firm, Gartner, indicates that both big data and adaptive learning have now slid into the ‘trough of disillusionment’, which means that the market has started to mature, becoming more realistic about how useful the technologies can be for organizations.

Overall, across the ALMAP trials, adding adaptivity to developmental and gateway courses had no effect on course completion rates after controlling for students’ initial achievement levels under any of the three possible use cases.

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

The Handbook of Learning Analytics is designed to meet the needs of a new and growing field. It aims to balance rigor, quality, open access and breadth of appeal and was devised to be an introduction to the current state of research. The Handbook is a snapshot of the field in 2017 and features a range of prominent authors from the learning analytics and educational data mining research communities. The chapters have been peer reviewed by committed members of these fields and are being published with the endorsement of both the Society for Learning Analytics Research and the International Society for Educational Data Mining. We hope you will find the Handbook of Learning Analytics a useful and informative resource.

My own response has been to focus on personal analytics, but this has been a hard sell.

“Knewton is a Ferrari, but we’re in a Kia market. Ferraris require more maintenance. It’s more complicated to use Knewton.”

“The notion that adaptive technology is the reason why one school should choose one company’s content over OER (open educational resources) or other options” has become a staple of many publisher’s marketing claims, says Trace Urdan, an education market analyst.

Pearson is “investing heavily in product development and is developing its own in-house adaptive learning capability,”

automatically generated data on the use of digital platforms and services as a means of mapping patterns of digital competencies and skills

CourseSmart Adds Analytics on Student E-Text Usage

Learning Analytics researchers have long held that the contexts of learning are critical in making meaningful analyses. With the large data footprint that Blackboard has to analyze, our team is able to look at some of these contexts in depth.

privacy controls over student data,

Analytics for Learn allows the analysis of teachers’ performance as well, something that will prove controversial in many institutions but will be increasingly of interest to senior managers who wish to monitor the quality of teaching in ways that were never possible in traditional classroom settings.

Bottom-up mining of patterns may reveal phenomena that nobody was predicting based on formal theory, and to which we are therefore blinded. It will be an exciting moment when an unexpected pattern in the data, discovered by an algorithm as an apparent anomaly, leads to a theoretical breakthrough.

paternalism in learning analytics (stakeholders believing they know what students need)

Be the skunk at the party. Because it’s intelligent skunks, not cheerleaders, that this field needs right now.

learning analytics are processes or procedures used to analyze student data to algorithmically predict outcomes, intervene in the learning process, and uncover patterns of learning behavior

Shaping the Educational Technology Innovation Ecosystem

The privacy dashboard discloses to students the learning data being captured about them and how it is being used (such as for research and/or early warning tools).