Open AccessEvaluation of suitability, acceptance and use of personalised learning scenarios
Author(s) -
Julija Kurilova,
Eugenijus Kurilovas,
Saulius Minkevičius
Publication year - 2017
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.b.2017.08
Subject(s) - learning styles , computer science , probabilistic logic , artificial intelligence , machine learning , active learning (machine learning) , educational technology , psychology , mathematics education
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.