# Data Mining of Students' Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network
## 5 Open Issues and Conclusions
### Conclusions  
In this study, we focused on mining on-campus consumption data to identify discriminative behavioural patterns based on spatial location change, analyse behaviour trends, and predict students' academic performance by constructing behaviour networks. Firstly, we preprocessed collected consumption data to extract features that could reflect students' living habits and their learning status. Secondly, we attempted to construct campus behaviour networks from reality and artificially, including introducing p-clique. Thirdly, combining with the pooling method, an improved self-attention GNN was utilized for training and prediction, and good prediction performance on the test set was achieved. For discriminative behavioural patterns, the habit of getting up early and behavioural patterns of continuous learning until meal time (lunch or dinner) in classroom and learning after three meals were discriminatory for distinguishing students with different learning levels, since the defined ratios of these behavioural pattern features are greater than 2 and we held certain beliefs that these characteristics are discriminatory in the sense of average. These new knowledge discoveries of behavioural patterns were consistent with the visualization results, conformed to the actual situation, and had certain reference meaning. For the behavioural trend analysis, judging from the behavioural pattern represented by single node 21, the student was more likely to continue studying after dinner than to return to the dormitory; from the perspective of node interaction and the existence of edges (e.g., node 4 and its neighbour nodes), three meals of students who often eat dinner were also relatively regular.  
However, some limitations need noting regarding the present study. An arguable weakness is that all the graphs we consider are still not large samples. When we consider multi-category classification, the current method may fall into the shortcomings of few training samples for each category. In the meantime, few-shot learning may contribute to solving the above problem. Another weakness is that we discard some information, such as excluding behaviour related to gym and school busses. In addition, application limitation exists in our model and it is hard to apply in other universities.  
As for future research directions, we were considering building a bigger behavioural network involving all students at school by their consumption record and some useful and possible video material to carry out analyses for different purposes, such as detecting absenteeism.  
Conceptualization, S.Q. and F.X.; methodology, F.X.; software, F.X.; validation, S.Q.; formal analysis, F.X. and S.Q.; investigation, S.Q.; resources, S.Q.; data curation, F.X.; writing--original draft preparation, F.X.; writing--review and editing, S.Q.; visualization, F.X.; supervision, S.Q.; project administration, S.Q.; funding acquisition, S.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy, including code.  
Conflicts of Interest: The authors declare that there is no conflict of interest regarding the publication of this paper.