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

Mojgan Fard, Krassie Petrova, Nikola Kasabov, Grace Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
132 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-6654-9552-3
ISBN (Print)978-1-6654-9553-0
DOIs
Publication statusPublished online - 1 Mar 2022
EventIEEE Asia-Pacific Conference on Computer Science and Data Engineering - Brisbane, Australia
Duration: 8 Dec 202110 Dec 2021
https://doi.org/10.1109/CSDE53843.2021

Conference

ConferenceIEEE Asia-Pacific Conference on Computer Science and Data Engineering
Abbreviated titleCSDE
Country/TerritoryAustralia
CityBrisbane
Period8/12/2110/12/21
Internet address

Keywords

  • transfer of learning
  • spiking neural networks
  • education
  • learning computer programming
  • NeuCube
  • cognitive load
  • EEG

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