While many strategies for protecting personal privacy have relied on regulatory frameworks, consent and anonymizing data, such approaches are not always effective. Frameworks and Terms and Conditions often lag user behaviour and advances in technology and software; consent can be provisional and fragile; and the anonymization of data may impede personalized learning. This paper reports on a dialogical multi-case study methodology of four Massive Open Online Course (MOOC) providers from different geopolitical and regulatory contexts. It explores how the providers (1) define 'personal data' and whether they acknowledge a category of 'special' or 'sensitive' data; (2) address the issue and scope of student consent (and define that scope); and (3) use student data in order to inform pedagogy and/or adapt the learning experience to personalise the context or to increase student retention and success rates.

This study found that large amounts of personal data continue to be collected for purposes seemingly unrelated to the delivery and support of courses. The capacity for users to withdraw or withhold consent for the collection of certain categories of data such as sensitive personal data remains severely constrained. This paper proposes that user consent at the time of registration should be reconsidered, and that there is a particular need for consent when sensitive personal data are used to personalize learning, or for purposes outside the original intention of obtaining consent.@en

Despite evidence that learning analytics has become institutionalised within higher education since its emergence in 2011, there remain questions regarding its impact on informing curricula, pedagogy and ultimately, on student success. A variety of factors can impact on the successful implementation of learning analytics. Despite its huge potential, learning analytics may become stuck in the middle of, inter alia, the need to balance operational needs and resource allocation, and different perceptions of learning, agency and loci of control in learning, teaching and macro-societal factors. In this conceptual paper, we propose an institutional cartography of learning analytics and explore the impact of a number of micro, meso and macro institutional factors that may impact and shape the institutionalisation of learning analytics. We utilise a socio-critical model for understanding student success as a conceptual basis for developing this cartography. @en