The evaluation of Learning Management Systems using an artificial intelligence fuzzy logic algorithm

2010 ◽  
Vol 41 (2) ◽  
pp. 248-254 ◽  
Author(s):  
Nadire Cavus
Author(s):  
Matija Pipan ◽  
Tanja Arh ◽  
Borka Jerman Blažic

The chapter deals with a complex decision-making problem, the selection and evaluation of Learning Management Systems (LMS) in which several objectives - referring to the definite group of users - like social, technical, environmental, and economic impacts, must be simultaneously taken into account. We introduce Evaluation Cycle Management (ECM), a support methodology aimed at the evaluation of options that occur in the decision-making processes. ECM is based on Multi-attribute decision making (Criteria Evaluation) and Usability Testing (Usability Evaluation). The Multi-attribute decision making in the first phase of ECM presents an approach to the development of a qualitative hierarchical decision model that is based on DEX, an expert system shell for multi-attribute decision support. The second phase of ECM is aimed at Usability Testing on end users. ECM illustrates its usefulness by showing its main features and its application to the above problem. It is based on the theoretical and practical expertise related to the quality and usability assurance of LMS.


Author(s):  
Azham Hussain Et.al

This study looked into extant literature to elicit a mapping relationship for the affective component of Bloom’s learning taxonomy and a theoretical affective model for the design and evaluation of the affective experiences of users of and learners on learning management system (LMS) platforms. The study found that no prior affective model exists specifically for the design and evaluation of the affective experiences of LMS users. The study then mapped the affective component of the Bloom’s taxonomy of learning with the conceptual affective model and captured corresponding suitable quality facets/dimensions that influence and contribute to the model. This model is particularly appropriate for the design and evaluation of the affectivity of LMS platforms.


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