Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network Approach
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...
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MDPI AG
2025-04-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/15/5/519 |
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| author | Cindy Di Han Shane N. Phillipson Vincent C S Lee |
| author_facet | Cindy Di Han Shane N. Phillipson Vincent C S Lee |
| author_sort | Cindy Di Han |
| collection | DOAJ |
| description | 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 (<i>n</i> = 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. |
| format | Article |
| id | doaj-art-2ffb8e55408d44c18b0b3746a46edbd9 |
| institution | Kabale University |
| issn | 2227-7102 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj-art-2ffb8e55408d44c18b0b3746a46edbd92025-08-20T03:47:49ZengMDPI AGEducation Sciences2227-71022025-04-0115551910.3390/educsci15050519Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network ApproachCindy Di Han0Shane N. Phillipson1Vincent C S Lee2Faculty of Education, Monash University, Wellington Road, Clayton, VIC 3800, AustraliaDepartment of Education, Swinburne University of Technology, John Street, Hawthorn, VIC 3122, AustraliaDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC 3800, AustraliaThe 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 (<i>n</i> = 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.https://www.mdpi.com/2227-7102/15/5/519academic achievementactiotope model of giftednessartificial neural networkscomplexitymodellingprediction |
| spellingShingle | Cindy Di Han Shane N. Phillipson Vincent C S Lee Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network Approach Education Sciences academic achievement actiotope model of giftedness artificial neural networks complexity modelling prediction |
| title | Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network Approach |
| title_full | Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network Approach |
| title_fullStr | Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network Approach |
| title_full_unstemmed | Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network Approach |
| title_short | Modelling the Interactions Between Resources and Academic Achievement: An Artificial Neural Network Approach |
| title_sort | modelling the interactions between resources and academic achievement an artificial neural network approach |
| topic | academic achievement actiotope model of giftedness artificial neural networks complexity modelling prediction |
| url | https://www.mdpi.com/2227-7102/15/5/519 |
| work_keys_str_mv | AT cindydihan modellingtheinteractionsbetweenresourcesandacademicachievementanartificialneuralnetworkapproach AT shanenphillipson modellingtheinteractionsbetweenresourcesandacademicachievementanartificialneuralnetworkapproach AT vincentcslee modellingtheinteractionsbetweenresourcesandacademicachievementanartificialneuralnetworkapproach |