A recommender framework for the evaluation of end user experience in adaptive technology enhanced learning
by Catherine Mulwa; Seamus Lawless; Ian O'Keeffe; Mary Sharp; Vincent Wade
International Journal of Technology Enhanced Learning (IJTEL), Vol. 4, No. 1/2, 2012

Abstract: Adaptive Technology Enhanced Learning (TEL) has attracted significant interest with the promise of supporting individual learning tailored to the unique circumstances, preferences and prior knowledge of a learner. However, the evaluation of the overall performance of such systems is a major challenge, as the adaptive TEL system reacts differently for each individual user and context of use. Evaluation of such systems is significant but very complex area of research in itself since depending on the aspect of personalisation that needs to be evaluated. Several evaluation techniques need to be combined and executed differently. This paper proposed a novel recommender framework built upon an evaluation educational data set using a hybrid recommend approach to identify appropriate procedures. Recommendations are to software developers and users of adaptive TEL systems. A review and analyses of evaluation studies on adaptive TEL systems was conducted. Based on the analysed results, an educational evaluation data set was created.

Online publication date: Wed, 31-Dec-2014

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