Privacy-driven Design of Learning Analytics Applications – Exploring the Design Space of Solutions for Data Sharing and Interoperability

Abstract

ABSTRACT:Studies have shown that issues of privacy, control of data, and trust are essential to implementation of learning analytics systems. If these issues are not addressed appropriately, systems will tend to collapse due to a legitimacy crisis, or they will not be implemented in the first place due to resistance from learners, their parents, or their teachers. This paper asks what it means to give priority to privacy in terms of data exchange and application design and offers a conceptual tool, a Learning Analytics Design Space model, to ease the requirement solicitation and design for new learning analytics solutions. The paper argues the case for privacy-driven design as an essential part of learning analytics systems development. A simple model defining a solution as the intersection of an approach, a barrier, and a concern is extended with a process focusing on design justifications to allow for an incremental development of solutions. This research is exploratory in nature, and further validation is needed to prove the usefulness of the Learning Analytics Design Space model.

Questions as data: illuminating the potential of learning analytics through questioning an emergent field

“…It has been timely that contemporaneous work has been conducted elsewhere (Cope & Kalantzis 2014;Sclater 2014;Papamitsiou and Economides 2014;Hoel et al 2016) providing perspective that validates and extends our own findings. However, findings can be expressed at various levels of abstraction, and it is our aim that this work informs the development of an ongoing research agenda in an open, investigative manner.…”

Community clouds within M-commerce: a privacy by design perspective

“…Addressing privacy can be done via socio-cultural solutions, i.e., considering the user and the system [73]. This would facilitate greater trust between the user and the system.…”

Guest Editorial: Ethics and Privacy in Learning Analytics

“…This does not make it clear in what way these data differ from other data and why they should be accorded special treatment. Hoel and Chen (2016) note the need to unpack privacy as a socio-cultural concept and observe that the boundaries around personal and private data are social agreements that depend on who the owner is and in what social settings the data are created and shared.…”

“…Learning analytics make it possible to combine data sets to generate insights that would never have been possible in the past, often making use of data that the learner was not aware were being collected or analyzed. Mobile data make it possible to collect details about the learner's environment -ambient light, temperature, and air pressure -and set them alongside personal data such as blood pressure, heart beat, and perspiration (Hoel & Chen, 2016).…”