This PhD research investigates the privacy and data protection issues surrounding learning analytics in higher education. In three studies, The project will examine the state-of-the-art of both issues in terms of what is already known as well as investigate the expectations of different human stakeholders in the overall learning analytics ecosystem. Grounded in the results from the state of-the-art and the stakeholder expectations, a toolbox that aims at informing the learning analytics stakeholders on the identified privacy and data protection issues will be developed. The toolbox will communicate a bigger picture of what, how, and why learning data are collected and analysed throughout the learning analytics processes. As data reporting (i.e., the stage of closing loops oflearning analytics processes) is an essential phase in the learning analytics ecosystem, a privacy risk aware approach will be proposed to quantify data privacy and protection aspects in learning analytics and make data reporting recommendations based on users' privacy preferences. The impact of this PhD project will increase transparency and thus improvetrust of learning analytics in higher education.
