Learning analytics are essentially the information that we can collect and analyse about how a student interacts with online course material throughout their learning journey. Every student will generate their own unique ‘footprint’ and by unpicking this information we can design interventions and support to help students get the most out of their studies.
To date, educators have largely been operating in the dark with little or no clarity on learners’ behaviour. In a recent paper in Development and Learning in Organisations: An International Journal, I explore the potential of learning analytics to better understand student success in the higher education context.
The National Guidelines for Improving Student Outcomes in Online Learning (Stone, 2017) identify learning analytics as one of ten areas of focus, highlighting how they can support more targeted student interventions. Personalising the student experience is particularly important for online learners who generally face an additional set of challenges to on-campus cohorts, such as managing work and family life alongside their studies.
There are opportunities to collect data at each stage of the learning journey: at enrolment, via the learning management system and then finally at assessment. By using this information to understand the hurdles students face, we can make more targeted adjustments to learning material, such as interactive aspects and teacher-facilitated learning interventions.
At OES we utilise learning analytics as evidence to drive the decision making associated with what changes, if any, we should make to a course unit. Such learning design decisions are strongly associated with student engagement and retention, and assist learning designers in minimising the ‘guess work’ by providing a clear picture of the student journey. When used strategically, this powerful data can mean the difference between student success and failure.