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.