1 Vilnius University / Vilnius Gediminas Technical University (LITHUANIA)
2 Vilnius University (LITHUANIA)
About this paper:
Appears in: EDULEARN16 Proceedings
Publication year: 2016
Pages: 89-98
ISBN: 978-84-608-8860-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2016.1015
Conference name: 8th International Conference on Education and New Learning Technologies
Dates: 4-6 July, 2016
Location: Barcelona, Spain
The aim of the paper is two-fold: first, to perform literature review on scientific methods, techniques, and possible results on application of personalised learning approach in education, and second – to present original research methodology and some results on application of personalised learning approach based on intelligent methods and technologies in Lithuania. In the paper, personalised learning approach is ensured by taking into account students’ learning styles according to different learning styles models. Interrelations of students’ learning styles and their cognitive traits (i.e. working memory capacity, inductive reasoning ability, and associative learning skills) is also analysed in the paper. This preferences analysis is necessary to further creating individual learning paths (scenarios) that should be optimal for particular learners. These learning paths should consist of suitable learning components (learning objects, learning methods, learning activities, learning tools, mobile apps etc.) optimal to particular students according to their personal needs (i.e. learning styles and cognitive traits). Scientific methodology to creating optimal learning paths for particular learners presented in the paper is based on the expert evaluation method and application of intelligent technologies. Intelligent technologies applied in the paper are multiple criteria decision making based expert evaluation, ontologies, recommender systems, intelligent software agents, and personal learning environments to construct learning paths (scenarios) consisting of the learning components that are the most suitable for particular learners. Inquiry-based learning activities are also analysed in the paper in terms of suitability to students’ learning styles. The main success factors of this approach are pedagogically sound vocabularies of learning components used to create personalised learning paths (scenarios), and experts’ collective intelligence. Lithuanian Intelligent Future School project aimed at implementing both learning personalisation and educational intelligence is presented in more detail.
Personalised learning, learning styles, intelligent technologies, recommender systems, Technology-Enhanced Learning.