Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach
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 brai...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/7/173 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849409104889511936 |
|---|---|
| author | Mojgan Hafezi Fard Krassie Petrova Nikola Kirilov Kasabov Grace Y. Wang |
| author_facet | Mojgan Hafezi Fard Krassie Petrova Nikola Kirilov Kasabov Grace Y. Wang |
| author_sort | Mojgan Hafezi Fard |
| collection | DOAJ |
| description | 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 (<i>n</i> = 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. |
| format | Article |
| id | doaj-art-6b54e842a2214cd9bc45f9b33bb515b0 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-6b54e842a2214cd9bc45f9b33bb515b02025-08-20T03:35:37ZengMDPI AGBig Data and Cognitive Computing2504-22892025-06-019717310.3390/bdcc9070173Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network ApproachMojgan Hafezi Fard0Krassie Petrova1Nikola Kirilov Kasabov2Grace Y. Wang3School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandSchool of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandSchool of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandSchool of Psychology and Wellbeing, University of Southern Queensland, Ipswich, QLD 4305, AustraliaThe 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 (<i>n</i> = 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.https://www.mdpi.com/2504-2289/9/7/173transfer of learningprior knowledgememory efficiencycognitive loadspiking neural networkprogramming language |
| spellingShingle | Mojgan Hafezi Fard Krassie Petrova Nikola Kirilov Kasabov Grace Y. Wang 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 transfer of learning prior knowledge memory efficiency cognitive load spiking neural network programming language |
| title | Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach |
| title_full | Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach |
| title_fullStr | Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach |
| title_full_unstemmed | Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach |
| title_short | Modeling the Effect of Prior Knowledge on Memory Efficiency for the Study of Transfer of Learning: A Spiking Neural Network Approach |
| title_sort | modeling the effect of prior knowledge on memory efficiency for the study of transfer of learning a spiking neural network approach |
| topic | transfer of learning prior knowledge memory efficiency cognitive load spiking neural network programming language |
| url | https://www.mdpi.com/2504-2289/9/7/173 |
| work_keys_str_mv | AT mojganhafezifard modelingtheeffectofpriorknowledgeonmemoryefficiencyforthestudyoftransferoflearningaspikingneuralnetworkapproach AT krassiepetrova modelingtheeffectofpriorknowledgeonmemoryefficiencyforthestudyoftransferoflearningaspikingneuralnetworkapproach AT nikolakirilovkasabov modelingtheeffectofpriorknowledgeonmemoryefficiencyforthestudyoftransferoflearningaspikingneuralnetworkapproach AT graceywang modelingtheeffectofpriorknowledgeonmemoryefficiencyforthestudyoftransferoflearningaspikingneuralnetworkapproach |