Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network

Performance prediction is of significant importance. Previous mining of behaviour data was limited to machine learning models. Corresponding research has not made good use of the information of spatial location changes over time, in addition to discriminative students’ behavioural patterns and tende...

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Autores principales:	Fangyao Xu, Shaojie Qu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	self-attention mechanism graph neural network data mining behaviour sequence pattern behaviour network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/98be5238db40470491bb5e3e3801adef
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spelling	oai:doaj.org-article:98be5238db40470491bb5e3e3801adef2021-11-25T16:38:00ZData Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network10.3390/app1122107842076-3417https://doaj.org/article/98be5238db40470491bb5e3e3801adef2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10784https://doaj.org/toc/2076-3417Performance prediction is of significant importance. Previous mining of behaviour data was limited to machine learning models. Corresponding research has not made good use of the information of spatial location changes over time, in addition to discriminative students’ behavioural patterns and tendentious behaviour. Thus, we establish students’ behaviour networks, combine temporal and spatial information to mine behavioural patterns of academic performance discrimination, and predict student’s performance. Firstly, we put forward some principles to build graphs with a topological structure based on consumption data; secondly, we propose an improved self-attention mechanism model; thirdly, we perform classification tasks related to academic performance, and determine discriminative learning and life behaviour sequence patterns. Results showed that the accuracy of the two-category classification reached 84.86% and that of the three-category classification reached 79.43%. In addition, students with good academic performance were observed to study in the classroom or library after dinner and lunch. Apart from returning to the dormitory in the evening, they tended to stay focused in the library and other learning venues during the day. Lastly, different nodes have different contributions to the prediction, thereby providing an approach for feature selection. Our research findings provide a method to grasp students’ campus traces.Fangyao XuShaojie QuMDPI AGarticleself-attention mechanismgraph neural networkdata miningbehaviour sequence patternbehaviour networkTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10784, p 10784 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	self-attention mechanism graph neural network data mining behaviour sequence pattern behaviour network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	self-attention mechanism graph neural network data mining behaviour sequence pattern behaviour network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Fangyao Xu Shaojie Qu Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network
description	Performance prediction is of significant importance. Previous mining of behaviour data was limited to machine learning models. Corresponding research has not made good use of the information of spatial location changes over time, in addition to discriminative students’ behavioural patterns and tendentious behaviour. Thus, we establish students’ behaviour networks, combine temporal and spatial information to mine behavioural patterns of academic performance discrimination, and predict student’s performance. Firstly, we put forward some principles to build graphs with a topological structure based on consumption data; secondly, we propose an improved self-attention mechanism model; thirdly, we perform classification tasks related to academic performance, and determine discriminative learning and life behaviour sequence patterns. Results showed that the accuracy of the two-category classification reached 84.86% and that of the three-category classification reached 79.43%. In addition, students with good academic performance were observed to study in the classroom or library after dinner and lunch. Apart from returning to the dormitory in the evening, they tended to stay focused in the library and other learning venues during the day. Lastly, different nodes have different contributions to the prediction, thereby providing an approach for feature selection. Our research findings provide a method to grasp students’ campus traces.
format	article
author	Fangyao Xu Shaojie Qu
author_facet	Fangyao Xu Shaojie Qu
author_sort	Fangyao Xu
title	Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network
title_short	Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network
title_full	Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network
title_fullStr	Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network
title_full_unstemmed	Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network
title_sort	data mining of students’ consumption behaviour pattern based on self-attention graph neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/98be5238db40470491bb5e3e3801adef
work_keys_str_mv	AT fangyaoxu dataminingofstudentsconsumptionbehaviourpatternbasedonselfattentiongraphneuralnetwork AT shaojiequ dataminingofstudentsconsumptionbehaviourpatternbasedonselfattentiongraphneuralnetwork
_version_	1718413091244343296

Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network

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