Roulette Wheel Selection Algorithm and Reinforcement Learning

ISBN-13: 9783659829567 / Angielski / Miękka / 2016 / 184 str.

Melvin Ballera

Opis

Various mechanisms to improve the learning process with the main objective of maximizing learning and dynamically selecting the best teaching operation to achieve learning goals have been done in the field of personalized learning. Despite recommending a personalized learning sequence, e-learning instructional strategists have failed to perform or address the necessary corrective measures to remediate immediately learning misconceptions or difficulties. As e-learning materials continue to evolve, it is necessary that an alternative, dynamic, and real time multi-performance be developed and implemented in e-learning systems. Two major contributions in the field of e-learning have been asserted by this study: it personalizes the learning sequence using reversed roulette wheel selection algorithm blended with linear ranking based on real time, dynamic multi-based performance matrix; and implements the reinforcement and mastery learning to motivate students and improve their learning output. Based on experiments, personalized learning sequence (PLS) were dynamic and heuristic but simultaneously considers the curriculum difficulty level.

O autorze

Dr. Melvin A. Ballera is a graduate of PhD in Computer Science at Strathmore University - Kenya and Master of Science in Philippines. He holds positions including research director, academic coordinator and director for linkages. He has published numerous papers in the area of artificial intelligence, e-learning, and instructional technology.