Abstract
A lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them. This paper focuses on the integration between computer science and social science (namely, education) for strengthening the visibility, recognition, and understanding the problems of simulation and modelling in social (educational) decision processes. The objective of the paper covers topics and streams on social-behavioural modelling and computational intelligence applications in education. To obtain the benefits of real, factual data for modeling student learning styles, this paper investigates exemplar-based approaches and possibilities to combine them with case-based reasoning methods for automatically predicting student learning styles in virtual learning environments. A comparative analysis of approaches combining exemplar-based modelling and case-based reasoning leads to the choice of the Bayesian Case model for diagnosing a student’s learning style based on the data about the student’s behavioral activities performed in an e-learning environment.
Highlights
This paper aims to present thorough, multidisciplinary research for making contributions, starting from concepts, models, and ending with recommendations and decision making capable to contribute to the effective educational policy formation agenda.In modern educational theories, effective learning paths should take into account learners’ needs and characteristics [1]
As Bayesian probability theory is used for modelling aleatoric uncertainty, BCM combines a Bayesian approach with the case-based reasoning method, which models epistemic uncertainty arising due to limited knowledge or data
In students’ leaning style model, which is a model of high-dimensional discrete data, we model student behavioral activity data in a virtual learning environment as coming from a mixture distribution, with mixture components corresponding to learning style clusters
Summary
This paper aims to present thorough, multidisciplinary research for making contributions, starting from concepts, models, and ending with recommendations and decision making capable to contribute to the effective educational policy formation agenda.In modern educational theories, effective learning paths should take into account learners’ needs and characteristics [1]. According to [5], a wide range of data about the behaviour of students in virtual learning environments should be used to generate good quality, real-time predictions about suitable materials and activities for each student individually. This should lead to success in acquiring knowledge and skills. In this case, a number of educational data mining and modelling methods and techniques could be used to identify and (if necessary) refine students’ learning styles
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