Interactive Learning Environment Optimization Using a Multi-Objective Genetic Algorithm

Abstract

Interactive learning environments have been regarded as critical parts of any modern digital learning environment due to their capability of providing personalized, adapted, and engaging educational opportunities for learners. Yet, existing learning platforms fail to optimize several educational goals concurrently due to inherent tradeoffs between engagement, learning achievement, cognitive load, and platform efficiency. In this study, an optimization strategy for interactive learning environments based on the Multi-Objective Genetic Algorithm (MOGA) is presented in order to enhance learning outcomes and adaptiveness of the learning system. Specifically, the optimization strategy consists of tracking learner interaction, recommendations and Pareto-evolution-based optimization to provide balanced configurations for various types of learners. The multi-objective approach involves the use of three parameters related to the learner behavior, achievement in the content, involvement in the content, and efficiency in the platform. The optimization is done through the use of a chromosome encoding technique, fitness function, crossover, mutation and principles of Pareto dominance. Comparisons were made with the old algorithm and their existing static learning systems, rule based adaptive learning systems and single objective genetic algorithm for experimenting. The results demonstrate substantial improvements in a range of performance measures. The first was the increase of interaction level from 68.4% to 92.7% and the second was the average test score from 71.3% to 93.1%. This will be a sign of educational choices and of the healthiness of the learner. The response time also went down from 430ms to 278ms. So, it can be concluded that the multi-objective genetic algorithms are optimal in optimizing the process of intelligent interactive education. This is because this is the approach that provides solutions to personalized adaptive learning, efficient use of resources, and balanced instructional decisions. Deep learning, reinforcement learning, and explainable artificial intelligence might be utilized in intelligent educational optimization systems in future research.