Effects of Neuro-Cognitive Load on Learning Transfer Using a Virtual Reality-Based Driving System

Usman Alhaji Abdurrahman,Shih-Ching Yeh,Liang Wei,Yunying Wong

doi:10.3390/bdcc5040054

Usman Alhaji Abdurrahman, Shih-Ching Yeh + Show 2 more

Open Access

https://doi.org/10.3390/bdcc5040054

Copy DOI

Abstract

Understanding the ways different people perceive and apply acquired knowledge, especially when driving, is an important area of study. This study introduced a novel virtual reality (VR)-based driving system to determine the effects of neuro-cognitive load on learning transfer. In the experiment, easy and difficult routes were introduced to the participants, and the VR system is capable of recording eye-gaze, pupil dilation, heart rate, as well as driving performance data. So, the main purpose here is to apply multimodal data fusion, several machine learning algorithms, and strategic analytic methods to measure neurocognitive load for user classification. A total of ninety-eight (98) university students participated in the experiment, in which forty-nine (49) were male participants and forty-nine (49) were female participants. The results showed that data fusion methods achieved higher accuracy compared to other classification methods. These findings highlight the importance of physiological monitoring to measure mental workload during the process of learning transfer.

Highlights

Learning is the change in behavior resulting from individual experience
The results show that the increase in cognitive load was associated with the conditions of the more cognitively demanding routes that led to the rise in response level
The addition of several turns, intersections, and landmarks on the difficult routes elicited an increase in psychophysiological activation, such as an increase in pupil dilation, heart rate, and eye gaze

Summary

Introduction

Learning is the change in behavior resulting from individual experience. According to [3], learning is always hampered whenever the learning task requires too much cognitive workload This working memory is considered volatile or short-term and limited, whereas it is infinite if it is long-term. If the subject cannot recover it from memory, the working memory becomes overloaded, leading to memory failures [4] Students find it difficult to recall previous pieces of information, especially complex ones, without prior knowledge [5]. This is because the working memory is insufficient; it can only deal with limited information at a time [6]: only about seven meaningful units of information can be stored in it at any given time [7]

Methods

Results

Conclusion