Assessment of instantaneous cognitive load imposed by educational multimedia using electroencephalography signals

Abstract

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.

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

“…While the test–rest approach is valid to prove the stability of results, especially in longitudinal studies, it is not the most suitable test to assess the impact of pre-processing on quantitative estimation when repeated measurements are not provided. In this context, a series of papers have been recently published in which the performances of machine learning approaches to classify the MWL level after different signal pre-processing pipelines were compared [ 36 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. These works are focused only on the automatic classification accuracy, considering several features extracted from all the EEG frequency bands and electrode signals, e.g., ERP, as input to the algorithm, whereas any direct evaluation of the EEG features extracted is provided.…”

Effects and prediction of cognitive load on encoding model of brain response to auditory and linguistic stimuli in educational multimedia

“…(5) Temporal Contiguity: corresponding words and visuals are presented at the same time. Find more details about the stimulus in our previous work 15 . Two linguists in English language teaching devised the scenario for making educational multimedia.…”

“…They were randomly assigned to watch lesson 11 NP, then lesson 6 P (n = 21) or lesson 6 NP and then lesson 11 P (n = 18). The data were collected in previous studies 15 , 41 . Specifically, 29 participants remained in the first condition, and 28 participants remained in the other condition after removing participants due to the following reasons: incomplete recording (n = 2), noisy data (n = 7), to find details see Preprocessing subsection, and too low post-test score (n = 2 in the first condition and n = 1 in the other).…”

“…Degraded visual stimuli are intentionally reduced quality or clarity of visual information, such as blurred or pixelated images or text. Sarailoo measured the CL of educational multimedia using machine-learning techniques 15 . For a systematic review, see 16 .…”

Neurophysiological measures and correlates of cognitive load in attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations

“…EEG has been used to quantify cognitive load through event‐related potentials (ERPs), and an inverse relationship has been found between the amplitude of ERPs and the cognitive load experienced during the completion of a working memory task (Ortiz et al, 2020; Tamanna & Parvez, 2021). EEG frequency bands can also classify high versus low cognitive load, with a reported accuracy of about 84.5% (Sarailoo et al, 2022). In addition to providing electrophysiological information, MEG yields a better spatial resolution of source localisation compared with EEG (Baillet, 2017).…”