Process Model for Differentiated Instruction using Learning Analytics
Keywords:Learning Analytics, Web Analytics, Differentiated Instruction, Learning Design
AbstractHigher 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.
Research Papers (general)
LicenseCopyright of all work published here subsists in the authors. While SACJ retains right of first publication, subsequent re-publication is expressly permitted provided the original SACJ publication is acknowledged and cited, according to the terms detailed below. If plagiarism is detected during review, a paper may be summarily rejected and will not be accepted unless even minor infringements are corrected. Should plagiarism be detected after a paper is published, the Editor reserves the right to withdraw a paper from publication. We expect authors to be honest in representing work as their own, and to respect the time and effort our reviewers put in without an undue burden of policing plagiarism, and hence take violations seriously. SACJ applies the Creative Commons Attribution NonCommercial 4.0 License (CC BY-NC 4.0) to all papers published in this journal. Authors who publish with SACJ agree to the following:
- Authors retain copyright and grant SACJ right of first publication. The work is additionally licensed under a Creative Commons Attribution Non-Commercial License that requires others who share the work to acknowledge the work’s authorship and initial publication in SACJ. Should anyone else wish to make commercial use of the work, SACJ cedes the right to the author to negotiate terms and does not expect to be paid any royalties.
- Authors may enter into additional arrangements for non-exclusive distribution of the SACJ-published version of the work (e.g., post it to a repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are required to refrain from posting their work online prior to completion of reviews so as not to compromise double-blind reviewing or confuse plagiarism checks.