Abstract
The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.
Highlights
The aim of effective instructional design is to help learners construct or automate knowledge schemas by means of specific strategies at certain learning circumstances [1,2,3]
The appropriate values of m and r resulted in the entropy indices, which were significantly different between the positive group (CL or cognitive load matching (CLM)) and the negative group (BL or cognitive load mismatching (CLMM))
We made a compromise between the accuracy of the classifier and the dimension of selected features, i.e., decreasing the dimension of selected features at the cost of a small decrease of mean accuracy
Summary
The aim of effective instructional design is to help learners construct or automate knowledge schemas by means of specific strategies at certain learning circumstances [1,2,3]. The key for effective learning is the matching of cognitive load and working memory [4]. Otherwise, when cognitive load exceeds working memory capacity, cognitive overload leads to bad learning outcomes [4]. It is meaningful to monitor cognitive load in the learning process. An experienced teacher can judge cognitive load through an observation of the learner’s behavior and learning outcome [3]. Teachers are often absent in e-learning, urging people to explore automatic cognitive load detection methods by using physiological measures
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