Sarailoo Reza, Latifzadeh Kayhan, Amiri S Hamid, Bosaghzadeh Alireza, Ebrahimpour Reza
Artificial Intelligence Group, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.
Front Neurosci. 2022 Aug 1;16:744737. doi: 10.3389/fnins.2022.744737. eCollection 2022.
The use of multimedia learning is increasing in modern education. On the other hand, it is crucial to design multimedia contents that impose an optimal amount of cognitive load, which leads to efficient learning. Objective assessment of instantaneous cognitive load plays a critical role in educational design quality evaluation. Electroencephalography (EEG) has been considered a potential candidate for cognitive load assessment among neurophysiological methods. In this study, we experiment to collect EEG signals during a multimedia learning task and then build a model for instantaneous cognitive load measurement. In the experiment, we designed four educational multimedia in two categories to impose different levels of cognitive load by intentionally applying/violating Mayer's multimedia design principles. Thirty university students with homogenous English language proficiency participated in our experiment. We divided them randomly into two groups, and each watched a version of the multimedia followed by a recall test task and filling out a NASA-TLX questionnaire. EEG signals are collected during these tasks. To construct the load assessment model, at first, power spectral density (PSD) based features are extracted from EEG signals. Using the minimum redundancy - maximum relevance (MRMR) feature selection approach, the best features are selected. In this way, the selected features consist of only about 12% of the total number of features. In the next step, we propose a scoring model using a support vector machine (SVM) for instantaneous cognitive load assessment in 3s segments of multimedia. Our experiments indicate that the selected feature set can classify the instantaneous cognitive load with an accuracy of 84.5 ± 2.1%. The findings of this study indicate that EEG signals can be used as an appropriate tool for measuring the cognitive load introduced by educational videos. This can be help instructional designers to develop more effective content.
多媒体学习在现代教育中的应用日益广泛。另一方面,设计出能施加最佳认知负荷量的多媒体内容至关重要,这会带来高效的学习效果。对瞬时认知负荷进行客观评估在教学设计质量评估中起着关键作用。脑电图(EEG)在神经生理学方法中被认为是认知负荷评估的潜在候选方法。在本研究中,我们进行实验以在多媒体学习任务期间收集EEG信号,然后构建一个用于瞬时认知负荷测量的模型。在实验中,我们将两类四个教育多媒体进行设计,通过有意应用/违背梅耶的多媒体设计原则来施加不同水平的认知负荷。三十名英语语言能力相当的大学生参与了我们的实验。我们将他们随机分为两组,每组观看一个多媒体版本,随后进行回忆测试任务并填写一份NASA - TLX问卷。在这些任务期间收集EEG信号。为构建负荷评估模型,首先,从EEG信号中提取基于功率谱密度(PSD)的特征。使用最小冗余 - 最大相关性(MRMR)特征选择方法,选择最佳特征。通过这种方式,所选特征仅占总特征数的约12%。在下一步中,我们提出一种使用支持向量机(SVM)的评分模型,用于在多媒体的3秒片段中进行瞬时认知负荷评估。我们的实验表明,所选特征集能够以84.5±2.1%的准确率对瞬时认知负荷进行分类。本研究结果表明,EEG信号可作为测量教育视频所引入认知负荷的合适工具。这有助于教学设计人员开发更有效的内容。