David Heath

This paper reports on partial findings of an Australian Office for Learning and Teaching funded project entitled Learning Analytics: Assisting Universities with Student Retention. The potential of learning analytics to address diverse learning needs in a systematic and responsive manner is discussed, focusing on how the use of learning analytics can assist in developing responsive and student-centred curricula, and by extension improve student retention. Potential uses of learning analytics to address diverse learning needs and improve retention are explored, as is current use by academics. The paper first provides a literature review with a focus on the potential impact of learning analytics on student retention, and student success and engagement, before reporting on academic-level qualitative data of the project. These qualitative data for this paper are retrieved from a combination of surveys and focused interviews with academics across the Australian higher education sector. The key findings indicate that learning analytics is currently still a potential source of confusion for many academics, that institutional context is a crucial factor, and that a need exists for proactive communication about learning analytics planning and strategy by institutional leaders.

This paper presents a dialogical tool for the advancement of learning analytics implementation for student retention in Higher Education institutions. The framework was developed as an outcome of a project commissioned and funded by the Australian Government's Office for Learning and Teaching. The project took a mixed-method approach including a survey at the institutional level (n = 24), a survey of individual teaching staff and other academics with an interest in student retention (n = 353), and a series of interviews (n = 23). Following the collection and analysis of these data an initial version of the framework was developed and presented at a National Forum attended by 148 colleagues from 43 different institutions. Participants at the forum were invited to provide commentary on the usefulness and composition of the framework which was subsequently updated to reflect this feedback. Ultimately, it is envisaged that such a framework might offer institutions an accessible and concise tool to structure and systematize discussion about how learning analytics might be implemented for student retention in their own context.

As learning analytics activity has increased, a variety of ethical implications and considerations have emerged, though a significant research gap remains in explicitly investigating the views of key stakeholders, such as academic staff. This paper draws on ethics-related findings from an Australian study featuring two surveys, one of institutional leaders (n = 22) and one of academic staff (n = 353), as well as a set of follow-up interviews (n = 23) with academic level staff. A self-selecting sample of participants was asked about the ethical issues they see as important in learning analytics and about the types of ethical principles or considerations they thought should guide learning analytics use. Data showed participants’ views did tend to align with established ethical principles, though the language used to express this varied widely. Building on, and in response to, both the data and the literature review the paper proposes an ethical decision making framework that encourages institutional leaders and those involved in implementing learning analytics to consistently apply and document ethical decision making processes as part of an overall approach to developing well-aligned and transparent institutional policies and broader ethical literacy.

This paper presents findings from a study of Australian and New Zealand academics (n = 276) who teach tertiary education students. The study aimed to explore participants' early experiences of learning analytics in a higher education milieu where data analytics is gaining increasing prominence. Broadly speaking participants were asked about: (1) their teaching context; (2) their current student retention activities; (3) their involvement in, and aspirations for, learning analytics use; and (4) their relationship with their institution around learning analytics. The sampled teaching staff broadly indicated a high level of interest but limited level of substantive involvement in learning analytics projects and capacity building activities. Overall, the intention is to present a critical set of voices that assist in identifying and understanding key issues and draw connections to the broader work being done in the field.

This paper presents findings from a study of Australian and New Zealand academics (n = 276) that teach tertiary education students. The study aimed to explore participants’ early experiences of learning analytics in a higher education milieu in which data analytics is gaining increasing prominence. Broadly speaking participants were asked about: (1) Their teaching context, (2) Their current student retention activities, (3) Their involvement in, and aspirations for, learning analytics use, (4) Their relationship with their institution around learning analytics. The sampled teaching staff broadly indicated a high level of interest but limited level of substantive involvement in learning analytics projects and capacity building activities. Overall, the intention is to present a critical set of voices that assist in identifying and understanding key issues and draw connections to the broader work being done in the field.