Process Model for Differentiated Instruction using Learning Analytics

Higher education institutions seem to have a haphazard approach to harnessing the ubiquitous data that learners generate on online educational platforms, despite promising opportunities offered by this data. Several learning analytics process models have been proposed to optimise the learning environment based on this learner data. The model proposed in this paper addresses deficiencies in existing learning analytics models that frequently emphasises only the technical aspects of data collection, analysis and intervention, yet remain silent on ethical issues inherent in collecting and analysing student data and pedagogy-based approaches to the interventions. The proposed model describes how differentiated instruction can be provided based on a dynamic learner profile built through an ethical learning analytics process. Differentiated instruction optimises online learning through recommending learning objects tailored towards the learner attributes stored in a learner profile. The proposed model provides a systematic and comprehensive abstraction of a differentiated learning design process informed by learning analytics. The model emerged by synthesising steps of a tried-and-tested web analytics process with educational theory, an ethical learning analytics code of practice, principles of adaptive education systems and a layered abstraction of online learning design.

Abstract licence: CC BY-NC

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