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

Big Data and Cognitive Computing

Shih-Ching Yeh¹  Usman Alhaji Abdurrahman²  Liang Wei²  Yunying Wong³ 

[1] Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan;School of Information Science and Technology, Fudan University, Shanghai 200433, China;School of Psychology, Fudan University, Shanghai 200433, China;

关键词: cognitive load;    learning transfer;    multimodal fusion;    physiological measures;    virtual reality;    driving simulator;   

DOI  :  10.3390/bdcc5040054

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown