REVERSED ROULETTE WHEEL SELECTION ALGORITHMS (RWSA) AND REINFORCEMENT LEARNING (RL) FOR PERSONALIZING AND IMPROVING E-LEARNING SYSTEM: THE CASE STUDY AND ITS IMPLEMENTATION

Abstract

Various mechanisms to improve learning process with the objective of maximizing learning and dynamically selecting the best teaching operation to achieve learning goals have been done in the field of personalized learning. However, instructional strategists have failed to address the necessary corrective measures to remediate immediately learning difficulties. It is necessary that an alternative, more realistic, simpler and a real time multi-based performance for personalized learning sequence be developed and implemented. Three major contributions can be asserted by the study: it personalized the learning sequence using reversed roulette wheel selection algorithm and linear ranking; the fitness value is based on real time, multi-based performance system; and it implements the reinforcement and mastery learning to motivate students and to improve their learning output. Result shows that the personalized learning sequence (PLS) were dynamic and heuristic and considers the curriculum difficulty level and the curriculum continuity of successive curriculum while producing individualized and personalized learning sequence. Data collected during 18 weeks experimental sessions, from 34%, a 54% increased has been achieved, making the overall passing rate to 88%. Digital transcripts based on students’ perceptions and experiences in using the prototype positively correlates with theme analysis having a score of +.321 with positive attitude such as: very happy, friends, motivate, improve, understanding, knowledge and good were extracted from document analysis.