Fard, Mojgan HafeziPetrova, KrassieKasabov, NikolaWang, Grace2025-04-072025-04-072025-03-17M Mahmud, M Doborjeh, N Kasabov, Z Doborjeh (Eds.) Peer-Reviewed Abstracts of the 31st International Conference on Neural Information Processing (ICONIP 2024), 2-6 Dec 2024, Auckland, New Zealandhttp://hdl.handle.net/10292/18994Learning computer programming is a demanding cognitive task that requires a number of competencies.  Students with limited problem-solving skills or prior programming knowledge find it challenging to understand the abstract concepts involved. While much research has investigated how cognitive load affects learning, few studies have investigated the cognitive load in memory associated with programming tasks using electroencephalograms. This research uses the EEG data, modelled in the NeuCube spiking neural network architecture, to analyse the memory efficiency in two groups of students, with and without prior programming knowledge, when learning a new language. Based on the quantitative analysis of brain neuronal connectivity captured in the NeuCube models, it is concluded that prior programming knowledge results in less cognitive load, meaning more efficient memory use.  Findings and results of empirical data analysis in this study can inform educators to develop strategies and design personalised learning interventions based on the student’s prior knowledge.Copyright (c) 2025 The Authors(s). Creative Commons License. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.https://creativecommons.org/licenses/by-nc-sa/4.0/46 Information and Computing Sciences39 Education4608 Human-Centred Computing4611 Machine LearningNeurosciencesMental HealthBasic Behavioral and Social ScienceBehavioral and Social ScienceConference ContributionOpenAccess10.24135/iconip7