Abstract:

The paper aims to analyse advantages and disadvantages of applying different expert-centred and user-centred evaluation methods to evaluate personalised learning units / scenarios. Learning units / scenarios are referred here as methodological sequences of learning components (learning objects, learning activities, and learning environment). High-quality learning units should consist of the learning components optimised to particular students according to their personal needs, e.g. learning styles. Several original methods are analysed in the paper, e.g. expert-centred methods MCEQLS (Multiple Criteria Evaluation of Quality of Learning Software) Fuzzy and MCEQLS AHP (Analytic Hierarchy Process) as well as user-centred method to evaluate suitability, acceptance and use of learning units. A special attention is paid to suitability of learning units to particular students’ needs. A number of MCDM (multiple criteria decision making) principles are applied in these methods to create comprehensive quality models (criteria trees) for evaluating quality of learning units. Several optimisation methods are explored and applied in these methods to optimise learning units in conformity with particular students’ needs. The research results have shown that all analysed methods are quite objective, exact and simple to use for selecting qualitative personalised learning units’ alternatives for particular learners. The differences are based on using different evaluation methods for different purposes and in different pedagogical situations. The proposed user-centred method to evaluate suitability, acceptance and use of learning units is components’ based, on the one hand, and ETAS-M (Educational Technology Acceptance & Satisfaction Model) - based, on the other. It’s more convenient in comparison with purely components-based models used by MCEQLS Fuzzy and MCEQLS AHP because it is based only on suitability, acceptance and use evaluation made by the users, and fully reflects their needs and points of view. Additionally, this kind of models does not require specific high-level technological expertise from experts-evaluators like in MCEQLS Fuzzy and MCEQLS AHP. On the other hand, this model is better than pure ETAS-M-based model because it’s more flexible since it takes into consideration all different components of learning units separately as well as corresponding average probabilistic suitability indexes. These methods and their proper application in different pedagogical situations are helpful to enhance learning quality and effectiveness.

Keywords:

MCDM, evaluation, expert-centred, user-centred, suitability, acceptance, use.