Technological advancements within the educational sector and online learning promoted
portable data-based adaptive techniques to influence the developments within transformative
learning and enhancing the learning experience. However, many common adaptive
educational systems tend to focus on adopting learning content that revolves around
pre-black box learner modelling and teaching models that depend on the ideas of a few
experts. Such views might be characterized by various sources of uncertainty about the
learner response evaluation with adaptive educational system, linked to learner reception
of instruction. High linguistic uncertainty levels in e-learning settings result in different
user interpretations and responses to the same techniques, words, or terms according to
their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching
models must be targeted to individual learners’ needs. Thus, developing a teaching model
based on the knowledge of how learners interact with the learning environment in readable
and interpretable white box models is critical in the guidance of the adaptation approach
for learners’ needs as well as understanding the way learning is achieved.
This paper presents a novel interval type-2 fuzzy logic-based system which is capable
of identifying learners’ preferred learning strategies and knowledge delivery needs
that revolves around characteristics of learners and the existing knowledge level in generating
an adaptive learning environment. We have conducted a large scale evaluation
of the proposed system via real-word experiments on 1458 students within a massively
crowded e-learning platform. Such evaluations have shown the proposed interval type-2
fuzzy logic system’s capability of handling the encountered uncertainties which enabled
to achieve superior performance with regard to better completion and success rates as well
as enhanced learning compared to the non-adaptive systems, adaptive system versions led
by the teacher, and type-1-based fuzzy based counterparts.
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