Multiple Criteria Evaluation of Quality and Optimisation of e-Learning System Components

The main research object of the paper is investigation and proposal of the comprehensive Learning Object Repositories (LORs) quality evaluation tool suitable for their multiple criteria decision analysis, evaluation and optimisation. Both LORs ‘internal quality’ and ‘quality in use’ evaluation (decision making) criteria are analysed in the paper. The authors have analysed several well-known LORs quality evaluation tools. In their opinion, the comprehensive multiple criteria LOR quality evaluation tool should include both general software ‘internal quality’ evaluation criteria and ‘quality in use’ evaluation criteria suitable for the particular project or user. In the authors’ opinion, the proposed LOR ‘Architecture’ group criteria are general ‘internal quality’ evaluation criteria, and ‘Metadata’, ‘Storage’, ‘Graphical user interface’ and ‘Other’ are ‘customisable’ ‘quality in use’ evaluation criteria. The authors have also presented their comprehensive Virtual Learning Environments (VLEs) quality evaluation tool combining both ‘internal quality’ (i.e., ‘General Architecture’) and ‘quality in use’ (i.e., ‘Adaptation’) technological evaluation criteria. The authors have proposed to use the quality evaluation rating tool while evaluating LORs and VLEs. The authors have analysed that if we want to optimise LORs and VLEs (or the other learning software packages) for the individual learner needs, i.e., to personalise his/her learning process in the best way according to their prerequisites, preferred learning speed and methods etc., we should use the experts’ additive utility function including the proposed LORs and VLEs expert evaluation criteria ratings together with the experts preferred weights of evaluation criteria. In this case we have the multiple criteria optimisation task using criteria ratings, and their weights. Quality evaluation criteria of the main e-Learning system components, i.e., LORs and VLEs are further investigated as the possible learning software packages optimisation parameters. Scalarization method is explored in the paper to be applied to optimise the learning software packages according to the individualised learners needs. Several open source VLEs evaluation results are also presented in the paper.