# Data Mining of Students' Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network
## 1 Introduction  
Methods to improve education quality by mining off-line education and on-line learning platform data [1] has led to the development of educational data mining (EDM) [2]. Among the several problems in the field of EDM, predicting students' scholastic performance is a key issue [3, 4], and various statistical methods [5, 6] and tools [7] have been developed to perform this task. However, these methods could not reflect the learning conditions of students over a specific period, and do not facilitate the discovery of new knowledge patterns from the data set for the development of new and accurate models. Advancements in machine learning has led to the emergence of powerful data visualization methods and a variety of models, such as clustering, classification, and prediction, including algorithms that can dynamically process data streams [8], and other algorithms such as the support vector machine have been used to detect students who may fail in courses as an early warning [9]. Research on machine learning using behavioural data for performance prediction has attracted a significant amount of attention, such as grade prediction using online behaviour [10, 11, 12], gaming behaviour [13], consumption behaviour [14] and travel behaviour [15]. To conduct on-line behaviour mining more deeply, artificial neural networks have been used for log data mining and desirable results have been achieved [16]. However, with regard to knowledge tracking, such as the prediction of student's test questions, artificial neural networks have not yielded satisfactory result, and recurrent neural networks (RNNs) have had to be introduced [17]. Further, when dealing with long sequences, RNNs found that gradient explosion and gradient disappearancewere prone to happen [18]. Therefore, to overcome such shortcomings, long short term memory networks (LSTM) were introduced [19]. LSTM can well solve the prediction problem with temporal data, while it has defects in processing spatial data. Further, the same behaviour will lead to ambiguity in the behavioural purpose if the difference in spatial location is ignored. For example, the behavioural meanings of fetching boiled water in the teaching building, in the dormitory, and in the bathroom are obviously different. We can, respectively, speculate that the purposes of corresponding behaviours are to study, to play games in the dormitory, and to take a bath. The lack of spatial location information will affect the prediction accuracy of models and the analysis of discriminative behavioural patterns. If the information in the spatial position was considered to mine behavioural characteristics contained in consumption data, the graph topological structure formed by behaviour cannot be well mined, and the above methods lost the ability to have good performance for such data with certain special structures. Naturally mining the data with graph structure for performance prediction requires the introduction of new tools. At present, graph neural networks (GNNs) have undergone rapid developments. Further, such networks have been extensively used [20; 21; 22] and are suitable for dealing with graph structure data. We observed that the behaviour of students can be demonstrated in the form of a graph and particular behavioural pattern and tendency can be observed, which inspired us to use graph-related tools to mine hidden information in behavioural patterns using students' spatial location changes over time. In this study, we aimed to utilize the information regarding spatial position changes over time reflected by consumption data collected from a school to extract behavioural pattern features and construct graph structures for mining discriminative behavioural characteristics and behavioural trends related to academic performance, as well as for performance prediction. Firstly, we proposed two guidelines for constructing the graph structures by extracting features from the consumption behaviour data.