Abstract
In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.
Highlights
There is an ongoing debate in mathematics education research on how to optimally support learners’ during arithmetic learning (e.g., Askew, 2015; Calder, 2015)
To evaluate the EEG-based learning environment, subjects used it to learn arithmetic addition in the octal number system (e.g., 3 + 5 = 10) and the learning success was compared to a control group, who learned the same task using a learning environment that adapts based on the number of correct responses
Since the error-rate was used for adapting the difficulty level of the presented learning material, the number of correctly solved trials was similar across subjects
Summary
There is an ongoing debate in mathematics education research on how to optimally support learners’ during arithmetic learning (e.g., Askew, 2015; Calder, 2015). It is crucial for successful learning to keep the cognitive workload in the individual optimal range for each learner (Sweller et al, 1998; Gerjets et al, 2009) This can be achieved by adapting the difficulty of the learning content to the individual competencies of the learner. Computer-supported learning (Kirschner and Gerjets, 2006) seems suited for implementing adaptivity, because it is easy to implement algorithms that change the difficulty of the presented material based on the learner’s behavioral response. This allows for an easy personalization of the learning environment to the user’s individual needs, which is assumed to be necessary for efficient learning. Adaptive computer-supported learning environments rely on the user’s interaction behavior for adaptation, e.g., error-adaptive systems, which change the task
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