基于一卡通消費(fèi)數(shù)據(jù)的學(xué)生成績(jī)預(yù)測(cè)和朋友關(guān)系網(wǎng)絡(luò)檢測(cè)研究
[Abstract]:With the continuous development of information and digital campus construction, the application of campus "one-card" system has penetrated into the study, life and other aspects of college students. In this context, a large number of student activities "trace" data are recorded in the form of text, which provides us with the possibility to analyze various student activities. If we can make use of the massive data we get, we can find out the characteristics of students' behavior, which will undoubtedly provide an important reference for the daily management and decision-making of the school. Therefore, how to make use of these data for the improvement of campus related aspects of the construction is the key direction of campus card data research. At present, the research on the consumption data of one card is focused on the research of students' consumption behavior by means of statistics or data mining. The main time of college students is to study and live in school, which determines that the most important behaviors of college students are "learning behavior" and "communication behavior". Traditionally, students' behavior is studied by means of observation, questionnaire and so on. However, because of the poor objectivity of data and the small amount of data, it is difficult to solve this problem satisfactorily. Whether we can study the students'"learning behavior" and "communication behavior" through the consumption data of one-card is our concern. As the direct result of "learning behavior" and "communication behavior", "academic achievement" and "friend relationship" are important reflection of them. Therefore, it is an important research direction to study students'"learning behavior" and "communication behavior" by using the data of one-card consumption to study students'"academic achievement" and "friends' relationship". Based on the one-card consumption data, this paper studies students'"learning achievement" and "friend relationship" by using the research method of complex network. The main work of this paper is as follows: (1) the rank correlation between breakfast frequency and professional achievement and the rank correlation between paroxysmal consumption time series and professional achievement are analyzed in detail. The KNN (K Nearest Neighbor) classification algorithm of machine learning is used to predict the grade of students, and a high accuracy rate is obtained. (2) based on the consumption data of one card, a binary network of consumption data is constructed. The method of multiple test is used to verify whether the co-occurrence of students originated from random encounter, and the network of student friend relationship is obtained. Then, the basic characteristics of the network of students' friend relationship are analyzed.
【學(xué)位授予單位】:華中師范大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2016
【分類號(hào)】:TP311.13;TN409
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