Studying Transfer of Learning using a Brain-Inspired Spiking Neural Network in the Context of Learning a New Programming Language

Paper

Fard, Mojgan Hafezi, Petrova, Krassie, Kasabov, Nikola and Wang, Grace Y.. 2021. "Studying Transfer of Learning using a Brain-Inspired Spiking Neural Network in the Context of Learning a New Programming Language." IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021). Brisbane, Australia 08 - 10 Dec 2021 Brisbane, Australia. https://doi.org/10.1109/CSDE53843.2021.9718472
Paper/Presentation Title

Studying Transfer of Learning using a Brain-Inspired Spiking Neural Network in the Context of Learning a New Programming Language

Presentation TypePaper
AuthorsFard, Mojgan Hafezi (Author), Petrova, Krassie (Author), Kasabov, Nikola (Author) and Wang, Grace Y. (Author)
Journal or Proceedings TitleProceedings of the IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021)
Number of Pages6
Year2021
Place of PublicationBrisbane, Australia
ISBN9781665495523
Digital Object Identifier (DOI)https://doi.org/10.1109/CSDE53843.2021.9718472
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9718472
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9718231/proceeding
Conference/EventIEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021)
Event Details
IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE 2021)
Event Date
08 to end of 10 Dec 2021
Event Location
Brisbane, Australia
Abstract

Transfer of learning (TL) has been an important research area for scholars, educators, and cognitive psychologists for over a century. However, it is not yet understood why applying existing knowledge and skills in a new context does not always follow expectations, and how to facilitate the activation of prior knowledge to enable TL. This research uses cognitive load theory (CLT) and a neuroscience approach in order to investigate the relationship between cognitive load and prior knowledge in the context of learning a new programming language. According to CLT, reducing cognitive load improves memory performance and may lead to better retention and transfer performance. A number of different frequency-based features of EEG data may be used for measuring cognitive load. This study focuses on analysing spatio-temporal brain data (STBD) gathered experimentally using an EEG device. An SNN based computational architecture, NeuCube, was used to create a brain-like computation model and visualise the neural connectivity and spike activity patterns formed when an individual is learning a new programming language. The results indicate that cognitive load and the associated Theta and Alpha band frequencies can be used as a measure of the TL process and, more specifically, that the neuronal connectivity and spike activity patterns visualised in the NeuCube model can be interpreted with reference to the brain activities associated with the TL process.

Keywordscognitive load; EEG; learning computer programming; NeuCube; SNN; spiking neural networks; Transfer of learning
ANZSRC Field of Research 2020520499. Cognitive and computational psychology not elsewhere classified
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Byline AffiliationsAuckland University of Technology, New Zealand
Institution of OriginUniversity of Southern Queensland
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