ON PERSONALISED LEARNING SCENARIOS EVALUATION MODEL

The main aim of the paper is to present a model (i.e. a system of quality criteria) for evaluation of quality of personalised learning scenarios / units. Learning scenarios / units are referred here as methodological sequences of learning components (learning objects, activities, methods, technologies and tools). High-quality learning scenarios 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 scenarios evaluation model presented in the paper is based on:
(1) well-known principles of Multiple Criteria Decision Analysis (MCDA) for identifying the quality criteria: value relevance, understandability, measurability, non-redundancy, judgmental independence, balancing completeness and conciseness, operationality, simplicity versus complexity, and
(2) Unified Theory on Acceptance and Use of Technology (UTAUT) model consisting of performance expectancy, effort expectancy, social influence, facilitating conditions, behavioural intention, and use behaviour constructs. Both MCDM principles and UTAUT constructs are presented in the paper in more detail. In the paper, presentation of the model for evaluating the quality of personalised learning scenarios is followed by discussion on applying different optimisation methods to evaluate the quality of personalised learning scenarios. The methodology of creating personalised learning scenarios evaluation model presented in the paper is absolutely new in scientific literature.

This methodology is:
(1) applicable in the real life situations when teachers have to help their students to create and apply learning scenarios / units that are most suitable for their needs and thus to improve education quality and efficiency,
(2) could significantly improve the quality of the expert evaluation of learning scenarios / units by noticeably reducing the expert evaluation subjectivity level.