Efficiency algorithm: new AI-based tools for an adaptive learning environment

Abstract

Introduction: The learning process is characterized by its variability rather than linearity, as individuals differ in how they receive, process, and store information. In traditional learning, taking into consideration the individual differences between students can be difficult. As a result, many talented students may fail because their learning speed does not align with the assessment requirements. Objectives: In this study, we propose efficiency algorithm as a new assessment method for adaptive learning (AL), based on artificial intelligence, to evaluate differences in students’ learning speed and help ensure the graduation of competent professionals in their discipline. Methods: Our assessment method was based on how effectively students apply the knowledge they have acquired to complete tasks. Using four important parameters that always answer the question of how the student completes rather than its completion. These parameters were information search, information evaluation, information processing, and information communication, which together constitute the basic components of our efficiency algorithm. Key findings: Our results showed that, by using the Naïve Bayes algorithm, we can determine with high accuracy (93%) in which part of the learning process (information search, information evaluation, information processing, or information communication) the student encounters difficulties. Contribution: Our proposed approach helps in designing personalized learning plans that directly target individual weaknesses.