Abstract

Cognitive load management will be critical in enhancing learning results, especially in e-learning. A heavy cognitive load may overload the learners, resulting in decreased engagement and performance. The context-aware systems offer a prospective solution as they are able to dynamically modify the learning experience using real-time learner input. The purpose of this paper is to design and assess a context-aware cognitive load reduction model that would automatically regulate the difficulty of tasks and feedback associated with cognitive states of learners. With the help of machine learning algorithms, the model can evaluate the cognitive load of the learners based on the interaction data and other contextual factors, including physiological data and environmental context. The system automatically increases and decreases the complexity of the tasks and feedback in real-time, keeping the learners in an ideal range of cognitive load, neither stunned nor bored. Findings of the model review implied that the cognitive load has been reduced by 20% and the task performance, especially on those tasks where high cognitive involvement is required, has increased by 15%. A learner said that his/her engagement, fatigue, and performance on complex tasks were improved using the model. This context-sensitive cognitive load minimization model has a significant benefit over the learning experience as it adjusts on the fly to the cognitive state of the learner. It improves learning performance as it makes tasks sufficiently challenging and provides the correct amount of feedback to the learners. Such a flexible strategy leads to an increase in the level of engagement of learners and overall effectiveness of e-learning settings.