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

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aut.relation.conference8th IEEE Asia-Pacific Conference on Computer Science and Data Engineeringen_NZ
aut.researcherPetrova, Krassimira
dc.contributor.authorFard, Men_NZ
dc.contributor.authorPetrova, Ken_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.contributor.authorWang, Gen_NZ
dc.date.accessioned2022-06-21T04:28:22Z
dc.date.available2022-06-21T04:28:22Z
dc.date.copyright2022en_NZ
dc.date.issued2022en_NZ
dc.description.abstractTransfer 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.
dc.identifier.citationProceedings of the 8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering. IEEE. 2022
dc.identifier.doi10.1109/CSDE53843.2021.9718472
dc.identifier.urihttps://hdl.handle.net/10292/15252
dc.publisherIEEEen_NZ
dc.relation.urihttps://ieeexplore.ieee.org/document/9718472en_NZ
dc.rightsCopyright © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectTransfer of learning; Cognitive load; Learning computer; Programming; Spiking neural networks; SNN; NeuCube; EEG
dc.titleStudying Transfer of Learning Using a Brain-Inspired Spiking Neural Network in the Context of Learning a New Programming Languageen_NZ
dc.typeConference Contribution
pubs.elements-id444814
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Health & Environmental Science
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Clinical Sciences
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Clinical Sciences/Psychology & Neuroscience Department
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HY Public Health & Psychosocial Studies 2018 PBRF
pubs.organisational-data/AUT/zAcademic Progression
pubs.organisational-data/AUT/zAcademic Progression/Professor
pubs.organisational-data/AUT/zAcademic Progression/Professor/AP - Prof - Health and Environmental Sciences
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