Algorithms for Batch Scheduling to Maximize the Learning Profit with Learning Effect and Two Competing Agents

Abstract

Due to the prevalence of e-learning and information technology, a wide choice of various learning styles is offered. So we might have multiple learning paths for a teaching material. However, learners differ from one another in their information literacy and cognitive load. These will influence the learning achievements greatly. Learners lacking information literacy are probably not able to determine their leaning paths easily. For example, obligatory courses, precedence relationship, time limit, and leaning effect should be taken into account. In light of these observations, we propose a genetic algorithm for determining leaning paths with many topics and a branch-and-bound algorithm for providing optimal learning paths of few learning topics.