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The aim of this paper is to present the methodology (i.e., the model and method) for evaluating the acceptance, use and suitability of personalized learning units/scenarios for particular students. Learning units/scenarios are referred to here as the methodological sequences of learning components (learning objects, learning activities and the learning environment). High-quality learning units should consist of learning components optimized for particular students in accordance with their personal needs, e.g., learning styles. In the paper, optimized learning units mean learning units composed of the elements having the highest probabilistic suitability indexes for particular students in accordance with the Felder-Silverman learning styles model. The personalized learning unit evaluation methodology, presented in the paper, is based on (1) the well-known principles of the Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) the Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on a well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model and (3) probabilistic suitability indexes for identifying learning component suitability to the needs of particular students, all in accordance with their learning styles. The methodology for evaluating the acceptance, use and suitability of personalized learning units for particular students, which is presented in the paper, is absolutely new in scientific literature. In the paper, there are also examples of implementing the methodology using different weights of evaluation criteria. This methodology is applicable in real life situations, where teachers have to help students create and apply learning units that are most suitable for their needs and thus to improve education quality and efficiency.

In the paper, learning scenarios (units) quality evaluation and optimisation problems are analysed. Learning scenarios optimisation is referred here as its personalisation according to learners needs. In the paper, comparative analysis of two popular optimisation methods based on Fuzzy numbers theory and Analytic Hierarchy Process is performed, aiming to measure what method is the most suitable to evaluate the quality of personalised learning scenarios. Learning scenarios quality is referred here as its suitability to learners needs. Research results show that Fuzzy numbers theorybased methods are more suitable to evaluate the quality of personalised learning scenarios.

The paper aims to present a novel probabilistic method to creating personalised learning packages. The method is based on learning components’ suitability to students needs according to their learning styles. In the paper, the authors use Felder-Silverman Learning Styles Model and an example of Inquiry Based Learning (IBL) method. Expert evaluation method based on trapezoidal fuzzy numbers is applied in the research to obtain numerical values of suitability of learning styles and learning activities. Personalised learning packages should consist of learning components (learning objects, learning activities and learning environments) that are optimal (i.e. the most suitable) to particular students according to their learning styles. “Optimal” means “having the highest suitability index”. Original probabilistic method is applied to establish not only students’ learning styles but also probabilistic suitability of learning activities to students’ learning styles. An example of personalised learning package using IBL activities is presented in more detail.