Modelling the Interactions Between Resources and Academic Achievement

An Artificial Neural Network Approach

Bibliographic Data

ID22045210
AuthorsCindy Di Han (0000-0003-4704-4521, Monash University, corresponding author), Shane N Phillipson (0000-0002-1694-3914, Swinburne University of Technology), Vincent C S Lee (0000-0001-5976-4601, Monash University)
Year2025
Volume15
Issue5
Pages519
Publication date2025-04-22
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci15050519
OpenAlexW4409667509
LanguageEN
References cited46

The actiotope model of giftedness takes a systems approach to understand the development of exceptionality and, more broadly, the academic achievement of students. Focusing primarily on the interactions between environmental capitals and outcomes such as academic achievement, research has relied on methods such as structural equation modelling (SEM) to understand these interactions. However, such methods do not reflect the nonlinear interactions inherent within systems. Based on datasets obtained from students from one Australian school (n = 778), both SEM and artificial neural networks (ANNs) were created for school-assessed achievement scores (mathematics, english and science) and standardised test scores (mathematics, vocabulary, and reading). Using the optimal ANN for school-assessed achievement scores for mathematics, its potential to predict future scores based on hypothetical improvements to five of the 11 capitals was confirmed. With high quality data, the use of ANNs will allow researchers to better understand these interactions and support practitioners to implement evidence-based interventions

Academic achievement · Artificial neural network · Mathematics education · Computer Science · Online Learning and Analytics · Psychology · Artificial Intelligence

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