Abstract
Sophisticated educational technologies are evolving rapidly, and online courses are becoming more easily available, generating interest in innovating lightweight data-driven adaptive approaches that foster responsive teaching and improving the overall learning experience. However, in most existing adaptive educational systems, the black-box modeling of learner and instructional models based on the views of a few designers or experts tended to drive the adaptation of learning content. However, different sources of uncertainty could affect these views, including how accurately the proposed adaptive educational methods actually assess student responses and the corresponding uncertainties associated with how students receive and comprehend the resulting instruction. E-learning environments contain high levels of linguistic uncertainties, whereby students can interpret and act on the same terms, words, or methods (e.g., course difficulty, length of study time, or preferred learning style) in various ways according to varying levels of motivation, pre-knowledge, cognition, and future plans. Thus, one adaptive instructional model does not fit the needs of all students. Basing the instruction model on determining learners' interactions within the learning environment in interpretable and easily read white-box models is crucial for adapting the model to students' needs and understanding how learning is realized. This paper presents a new zSlices-based type-2 fuzzy-logic-based system that can learn students' preferred knowledge delivery needs based on their characteristics and current levels of knowledge to generate an adaptive learning environment. We have evaluated the proposed system's efficiency through various large-scale, real-world experiments involving 1871 students from King Abdulaziz University. These experiments demonstrate the proposed zSlices type-2 fuzzy-logic-based system's capability for handling linguistic uncertainties to produce better performance, particularly in terms of enhanced student performance and improved success rates compared with interval type-2 fuzzy logic, type-1 fuzzy systems, adaptive, instructor-led systems, and non-adaptive systems.