Intelligent medical case based e-learning system

Educational theory has purported the notion that student-centric modes of learning are more effective in enhancing student engagement and by extension, learning outcomes. However, the translation of this theoretical pedagogy of learning into an applied model for medical training has been fraught with difficulty due to the structural complexity of creating a classroom environment that enables students to exercise full autonomy. In this paper, we propose an intelligent computational e-learning platform for case-based learning (CBL) in Medicine that enriches and enhances the learning experiences of medical students by exposing them to simulated real-world clinical contexts. We argue that computational systems in Medicine should not merely provide a passive outlay of information, but instead promote active engagement through an immersive learning experience. This is achieved through a digital platform that renders a virtual patient simulation, which allows students to assess, diagnose, treat and test patients as they would in the real-world

An intelligent information access system assisting a case based learning methodology evaluated in higher education with medical students

In recent years there has been a shift in educational methodologies toward a student-centered approach, one which increasingly emphasizes the integration of computer tools and intelligent systems adopting different roles. In this paper we describe in deta2.775 JCR (2012) Q1, 7/219 Education & educational researchUE

On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Decision Trees

Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables