What Can Analytics Contribute to Accessibility in e-Learning Systems and to Disabled Students’ Learning?

Type: Evidence | Proposition: B: Teaching | Polarity: | Sector: | Country:

This paper explores the potential of analytics for improving accessibility of e-learning systems and for supporting disabled learners in their studies.

The work is set in the context of learning and academic analytics' focus on issues of retention. The definitions of disability and accessibility in e-learning are outlined and the implications of these for how disabled students needs may be modeled in learning analytics systems briefly discussed.

A comparative analysis of completion rates between disabled and non-disabled students in a large data set of 5 years of Open University modules is presented. The wide variation in comparative retention rates is noted and characterized. A key assertion of this paper are that learning analytics provide a set of tools for identifying and understanding such discrepancies and that analytics can be used to focus interventions that will improve the retention of disabled students in particular. A comparison of this quantitative approach with that of qualitative end of module surveys is made. How an approach called Critical Learning Paths, currently being researched, may be used to identify accessibility deficits in module components that are significantly impacting on the learning of disabled students is described.

An outline agenda for onward research currently being planned is given.

It is hoped that this paper will stimulate a wider interest in the potential benefits of learning analytics for higher educational institutions as they try to assure the accessibility of their e-learning and for the provision of support for disabled students.

Citation: Martyn Cooper, Rebecca Ferguson and Annika Wolff (2016). "What Can Analytics Contribute to Accessibility in e-Learning Systems and to Disabled Students' Learning?". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.