Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach

Article

Fard, Mojgan Hafezi, Petrova, Krassie, Kasabov, Nikola Kirilov and Wang, Grace Y.. 2025. "Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach." Big Data and Cognitive Computing. 9 (7). https://doi.org/10.3390/bdcc9070173
Article Title

Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach

ERA Journal ID211865
Article CategoryArticle
AuthorsFard, Mojgan Hafezi, Petrova, Krassie, Kasabov, Nikola Kirilov and Wang, Grace Y.
Journal TitleBig Data and Cognitive Computing
Journal Citation9 (7)
Article Number173
Number of Pages27
Year2025
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2504-2289
Digital Object Identifier (DOI)https://doi.org/10.3390/bdcc9070173
Web Address (URL)https://www.mdpi.com/2504-2289/9/7/173
Abstract

The transfer of learning (TL) is the process of applying knowledge and skills learned in one context to a new and different context. Efficient use of memory is essential in achieving successful TL and good learning outcomes. This study uses a cognitive computing approach to identify and explore brain activity patterns related to memory efficiency in the context of learning a new programming language. This study hypothesizes that prior programming knowledge reduces cognitive load, leading to improved memory efficiency. Spatio-temporal brain data (STBD) were collected from a sample of participants (n = 26) using an electroencephalogram (EEG) device and analyzed by applying a spiking neural network (SNN) approach and the SNN-based NeuCube architecture. The findings revealed the neural patterns demonstrating the effect of prior knowledge on memory efficiency. They showed that programming learning outcomes were aligned with specific theta and alpha waveband spike activities concerning prior knowledge and cognitive load, indicating that cognitive load was a feasible metric for measuring memory efficiency. Building on these findings, this study proposes that the methodology developed for examining the relationship between prior knowledge and TL in the context of learning a programming language can be extended to other educational domains.

Keywordstransfer of learning; prior knowledge; memory efficiency; cognitive load; spiking neural network; programming language
Article Publishing Charge (APC) FundingOther
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020520401. Cognition
461104. Neural networks
Byline AffiliationsAuckland University of Technology, New Zealand
School of Psychology and Wellbeing
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