Effects of neuro-cognitive load on learning transfer using a virtual reality-based driving system

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

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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 high-light the importance of physiological monitoring to measure mental workload during the process of learning transfer.

Original languageEnglish
Article number54
JournalBig Data and Cognitive Computing
Issue number4
StatePublished - Dec 2021


  • Cognitive load
  • Driving simulator
  • Learning transfer
  • Multimodal fusion
  • Physiological measures
  • Virtual reality


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