Foundations of dynamic learning analytics: Using university student data to increase retention

Abstract

With digitisation and the rise of e-learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper-based course materials, registration, admissions and support services to the mobile, always-on and always accessible data has driven demand for information and generated new forms of data observable through consumption behaviours. These changes have led to a plethora of data sets that store learning content and track user behaviours. Most recently, new data analytics approaches are creating new ways of understanding trends and behaviours in students that can be used to improve learning design, strengthen student retention, provide early warning signals concerning individual students and help to personalise the learner's experience. This paper proposes a foundational learning analytics model (LAM) for higher education that focuses on the dynamic interaction of stakeholders with their data supported by visual analytics, such as self-organising maps, to generate conversations, shared inquiry and solution-seeking. The model can be applied for other educational institutions interested in using learning analytics processes to support personalised learning and support services. Further work is testing its efficacy in increasing student retention rates. An introduction to learning analyticsAlthough data analytics capabilities have been developing over the last 10-20 years, there has broadly been a disconnect between business intelligence and the use of data for supporting learning-based hypotheses. For example, although data have been gathering in educational databases, the capability and know-how for using it to advance learning and improve the student experience has barely begun and only rarely been investigated (Ferguson, 2012a(Ferguson, , 2012b. With the build-up of data from learning management systems, customer relationship management systems and student-based systems, some universities have begun to investigate how to increase student retention, improve student-centred services and to develop more interactive learning experiences.

Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments

“…For the student audience in the study population, students enrolled part-time performed better in both face-to-face and online courses as compared to those enrolled full-time. The literature from other studies consistently found that full-time students were more likely to succeed (Adelman, 1999;Aragon & Johnson, 2008;Colorado & Eberle, 2010;Demetriou & Schmitz-Sciborski, 2011;de Freitas et al, 2015). There are many possible explanations for this finding, but not one identified in the existing literature.…”

Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective

“…Other early concerns were highlighted as natural extensions of the logical outcomes of the research (de Freitas et al 2014); these approaches were not systematic inquiries into the potentially harmful effects of using student data. The ethics of opt-in/opt-out and consent (Prinsloo and Slade 2015;Hack 2015a), anonymity of data (de Freitas et al 2014), and expressly utilitarian ethics in learning analytics (Willis 2014) later emerged.…”

“…The ethics of opt-in/opt-out and consent (Prinsloo and Slade 2015;Hack 2015a), anonymity of data (de Freitas et al 2014), and expressly utilitarian ethics in learning analytics (Willis 2014) later emerged. Kruse and Pongsajapan (2012) propose a move from learning analytics as applied to students by administrators and professors toward systems where students are equipped and enabled with their own data, though this approach also has potential ethical problems.…”

Use of Learning Analytics Data in Health Care–Related Educational Disciplines: Systematic Review (Preprint)

“…Higher education institutions collect a vast amount of information from students regarding their use of e-learning resources in the form of activity logs and other digital footprints such as time and date, student demographics, course enrolments, survey questionnaires, library usage, and academic grades [8].…”