Comparative analysis of fuzzy controllers and machine learning in adaptive e-learning systems

The objective of this study is to perform a comparative analysis of fuzzy controllers and selected machine learning algorithms in adaptive e-learning systems for solving classification problems characterized by imprecisely modeled input data. In scenarios where access to labeled datasets for solving classification problems is not available, and thus the application of machine learning methods is not feasible, expert knowledge encoded in the form of if...then rules can serve as a basis for designing an expert fuzzy controller. The paper studies a classification problem where the expert fuzzy controller approach was applied and evaluates its performance against the most widely used machine learning algorithms. In addition, the study includes a comparison with fuzzy controllers generated automatically from an available dataset. The process of data representation for machine learning purposes is also outlined. The results show that machine learning algorithms achieve the highest classification accuracy when a dataset is available. However, fuzzy controllers demonstrate competitive performance, highlighting their potential utility in situations where datasets are not present.