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A current problem with the research of adaptive systems is the inconsistency of evaluation applied to the adaptive systems. However, evaluating an adaptive system is a difficult task due to the complexity of such systems. Evaluators need to ensure correct evaluation methods and measurement metrics are used. This paper reviews a variety of evaluation techniques applied in adaptive and user-adaptive systems. More specifically, it focuses on the user-centred evaluation of adaptive systems such as personalised recommender systems and adaptive information retrieval systems. The review tackles the question of 'How have user-centred evaluations of adaptive and user-adaptive systems been conducted and how can these evaluation practices be improved?' Based on the analysed results of the: (a) evaluation approaches, (b) user-centred evaluation techniques, and (c) evaluation metrics, we propose an evaluation framework for end-user experience in evaluating adaptive systems (EFEx).

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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.