Key Findings About Students

• Students grade may impact on both affect and academic resilience, while the way learning analytic alerts are developed may not promote self-regulated learning. This suggests we need to look beyond these characteristics of individual messages to identify drivers of engaging students in self-regulated learningand academics with teaching responsibilities need to be engaged in the development of learning analytics.
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• Students and academics with teaching responsibilities need to be engaged in the development of learning analytics.
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• Students are concerned about the potential inequities that may arise from learning analytics (e.g., some people receiving more support than others) yet also recognise that they would benefit from the additional support provided through learning analytics.
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Students would like to have some choice regarding the implementation of their of dashboard analytics.
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Key Findings About Staff

• Academics perceived that, while currently unrealised, there could be several benefits to learning analytics to help their students.

• Academics held reservations about how learning analytics could increase their workloads, impact student learning, result in ‘helicopter’ teaching, and ultimately be misused.
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Key Findings About University

• Universities will need to develop a governance framework and guiding principles for the implementation of learning analytics.

• A matrix has been developed to assist universities to examine ethical issues associated with current or planned learning analytics activities or strategies from the perspective of key stakeholders.

• Universities need to be clear in their messages to students and staff as to what learning analytics are, and importantly, how they will be used.