Performance evaluation of adaptive content selection in AEHS

Adaptive content selection is recognized as a challenging research issue in adaptive educational hypermedia systems (AEHS). Several efforts have been reported in literature aiming to support the Adaptation Model (AM) design by providing AEHS designers with either guidance for the direct definition of adaptation rules, or semi-automated mechanisms which generate the AM via the implicit definition of such rules. The goal of the semi-automated, decision-based approaches is to generate a continuous decision function that estimates the desired AEHS response, aiming to overcome the insufficiency and/or inconsistency problems of the defined adaptation rule sets. Although such approaches bare the potential to provide efficient AMs, they still miss a commonly accepted framework for evaluating their performance. In this paper, we propose an evaluation framework suitable for validating the performance decision-based approaches in adaptive learning objects selection in AEHS and demonstrate the use of this framework in the case of our proposed decision-based approach for estimating the desired AEHS response. © Springer-Verlag Berlin Heidelberg 2011

Evaluating the performance of adaptive learning objects selection and sequencing in adaptive educational hypermedia systems

Adaptive learning objects selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning objects in AEHS, the definition of adaptation behavior, referred to as Adaptation Model, is required. Several efforts have been reported in literature aiming to support the Adaptation Model design by providing AEHS designers either guidance for the direct definition of adaptation rules, or semi-automatic mechanisms for making the design process less demanding via the implicit definition of such rules. The main drawback of the direct definition of adaptation rules is that there can be cases during the run-time execution of AEHS where no adaptation decision can be made, due to inconsistency, and/or insufficiency of the defined adaptation rule sets. The goal of the semi-automatic approaches is to generate a continuous decision function that estimates the desired AEHS response, overcoming the above mentioned problem. To achieve this, they use data from the implicit definition of sample adaptation rules and try to fit the response function on these data. Although such approaches bare the potential to provide efficient Adaptation Models, they still miss a commonly accepted framework for measuring their performance. In this paper, we present our performance evaluation methodology for validating the use of decision-based approaches for adaptive learning objects selection and sequencing in AEHS. © 2009 IEEE

Decision models in the design of adaptive educational hypermedia systems

Several efforts have been reported in literature aiming to support the Adaptation Model (AM) design in Adaptive Educational Hypermedia Systems (AEHS) with either guidance for the direct definition of adaptation rules or semi-automated mechanisms that generate the AM through the implicit definition of such rules. The main drawback of the direct definition of adaptation rules is that there can be cases during the run-time execution of AEHS where no adaptation decision can be made, due to insufficiency and/or inconsistency of the pre-defined adaptation rule sets. The goal of the semi-automated, decisionbased approaches is to generate a continuous decision function that estimates the desired AEHS response, aiming to overcome the above mentioned problem. However, such approaches still miss a commonly accepted framework for evaluating their performance. In this chapter, we review the design approaches for the definition of the AM in AEHS and discuss a set of performance evaluation metrics proposed by the literature for validating the use of decision-based approaches. © 2012, IGI Global