Search

The paper aims to present a methodology (i.e. model and method) to evaluate suitability, acceptance and use of personalised learning scenarios. High-quality learning scenarios should consist of the learning components (i.e. learning objects, learning activities, and learning environment) optimised to particular students according to their personal needs, e.g. learning styles. In the paper, optimised learning scenarios mean learning scenarios composed of the components having the highest probabilistic suitability indexes to particular students according to Felder–Silverman learning styles model. Personalised learning scenarios evaluation methodology presented in the paper is based on (1) probabilistic suitability indexes to identify learning components suitability to particular students needs according to their learning styles, and (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model.

The aim of the paper is to present a methodology (i.e. model and method) to evaluate suitability, acceptance and use of 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. In the paper, optimised learning scenarios mean learning scenarios composed of the components having the highest probabilistic suitability indexes to particular students according to Felder-Silverman learning styles model. Personalised learning units evaluation methodology presented in the paper is based on (1) well-known principles of Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model, and (3) probabilistic suitability indexes to identify learning components’ suitability to particular students’ needs according to their learning styles. The methodology to evaluate personalised learning units presented in the paper is absolutely new in scientific literature. This methodology is applicable in real life situations where teachers have to help students to create and apply learning units that are most suitable for their needs and thus to improve education quality and efficiency.

The paper aims to present an original probabilistic method to creating personalised learning activities and units. The method is based on evaluating learning components suitability to students needs according to their learning styles. In the paper, Felder–Silverman Learning Styles Model and Inquiry Based Learning method suitable to learn Math are used. Expert evaluation method based on Trapezoidal Fuzzy Numbers theory is applied in the research to obtain numerical values of suitability of IBL activities to students learning styles. Personalised learning units should consist of learning components (i.e. learning activities) that are optimal (i.e. the most suitable) to particular students according to their learning styles. Optimal means having the highest suitability index. In the paper, this original probabilistic method is applied to establish probabilistic suitability of IBL activities to particular students.

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.