Title: PERSONALIZING AND IMPROVING E-LEARNING SYSTEM USING ROULETTE WHEEL SELECTION ALGORITHM, REINFORCEMENT LEARNING AND CASE-BASED REASONING APPROACH

Year of Publication: Jul - 2013
Page Numbers: 184-193
Authors: Melvin Ballera, Ismail Ateya Lukandu, Abdalla Radwan
Conference Name: The Fourth International Conference on e-Learning (ICEL2013)
- Czech Republic

Abstract:


Despite the ever-increasing practice of using e-learning in educational institutions, most of the applications perform poorly in motivating students to learn for many reasons and varied factors. Online educational materials make learning a task-driven process but can be improved by providing a personalized topic sequencing that match the learner’s ability, background and prior knowledge. By improving, optimal teaching operation in e-learning system can be achieved by bringing the learner closest to the ultimate learning goal. This can be achieved by employing evolutionary technique; roulette wheel algorithm (RWA), reinforcement learning (RL) and case-based reasoning (CBR) approach. This paper makes three critical contributions in e-learning development and implementations: (1) it presents a personalized topic sequence using the roulette wheel selection algorithms; (2) provides reinforcement learning through practice and mastery learning and; (3) to illustrate case-based reasoning approach in retrieving and storing cases for further use and profiling students.