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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/22/10784 |
| _version_ | 1797511297919614976 |
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| author | Fangyao Xu Shaojie Qu |
| author_facet | Fangyao Xu Shaojie Qu |
| author_sort | Fangyao Xu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:44:21Z |
| format | Article |
| id | doaj.art-98be5238db40470491bb5e3e3801adef |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:21Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-98be5238db40470491bb5e3e3801adef2023-11-22T22:18:49ZengMDPI AGApplied Sciences2076-34172021-11-0111221078410.3390/app112210784Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural NetworkFangyao Xu0Shaojie Qu1Beijing Institute of Technology, Beijing 100081, ChinaBeijing Institute of Technology, Beijing 100081, ChinaPerformance 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.https://www.mdpi.com/2076-3417/11/22/10784self-attention mechanismgraph neural networkdata miningbehaviour sequence patternbehaviour network |
| spellingShingle | Fangyao Xu Shaojie Qu Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network Applied Sciences self-attention mechanism graph neural network data mining behaviour sequence pattern behaviour network |
| title | 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_short | 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 |
| topic | self-attention mechanism graph neural network data mining behaviour sequence pattern behaviour network |
| url | https://www.mdpi.com/2076-3417/11/22/10784 |
| work_keys_str_mv | AT fangyaoxu dataminingofstudentsconsumptionbehaviourpatternbasedonselfattentiongraphneuralnetwork AT shaojiequ dataminingofstudentsconsumptionbehaviourpatternbasedonselfattentiongraphneuralnetwork |