Complementary Architecture of E-Learning Path Personalization and Optimization

Using computing algorithms to generate personalized learning resources to provide the needs and improve the capabilities, preferences, and academic performance of diverse learners is creating preferred learning environments. As more learning resources, strategies and techniques are frequently added to these e-learning systems, input data to personalize the learning path has been growing exponentially making swift responses to learner’s requests difficult. This study proposed a complementary learning path personalization architecture using ant colony (ACO) with nearest neighbour technique and genetic algorithm (GA) to extend the functions of the Spark framework purposely to develop a robust evolutionary computing algorithm. Experimental results indicate that complementing ACO, GA and Spark frameworks improved the generation of personalized learning resources and best-fitted-optimized learning paths. Spark-ACO took less computational time than standalone ACO. Combining ACO and GA improved the likelihood of an ant colony being trapped at a local optimum, and Spark-ACO-GA significantly enhanced the accuracy of the solutions.