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Social Relationship Mining Based on Student Data

Published: 24 March 2021 Publication History

Abstract

In recent years, the campus social network mining has been widely concerned by scholars because it is closely related to the physical and mental health development of college students and the university management work. Compared with the typical social network mining tasks, the challenges of campus social network mining mainly lie in the difficulty of data acquisition and unlabeled data. Based on the phenomenon of homogeneity between friends, this paper proposed a social relationship mining method. Firstly, this paper carried out data dimension reduction operation for each student's daily consumption data, and then innovatively carried out the convolution operation for the consumption behavior data after dimension reduction, and finally obtained the characteristics of students' consumption behavior. The Multiple dimensional scaling algorithm is adopted for data dimension reduction, and the convolutional network is an improved Lenet-5 network. Then, the personalized ranking model is developed base on the phenomenon that the distance between friends is less than the distance between non-friends. To solve the problem of unlabeled data, we mined the friends' relationship from the student's library access control system swiping card records, and then taken the mined friends' relationship as the real friends' relationship to train the model. From the experimental results, the social network mining method proposed in this paper performs well.

References

[1]
Garcia E, Romero C, Ventura S, et al. 2011. A Collaborative Educational Association Rule Mining Tool. Internet and Higher Education, 14(2): 77--88.
[2]
Gikas J, Grant MM. 2013. Mobile Computing Devices in Higher Education: Student Perspectives on Learning with Cellphones, Smartphones & Social Media. Internet and Higher Education, 19: 18--26.
[3]
Segrin C, Flora J. 2000. Poor Social Skills Are a Vulnerability Factor in the Development of Psychosocial Problems. Human Communication Research, 26(3): 489--514.
[4]
Dima AM, Vasilache S. 2015. Social Network Analysis for Tacit Knowledge Management in Universities. Journal of the Knowledge Economy, 6(4): 856--864.
[5]
Chu SKW, Du HS. 2013. Social Networking Tools for Academic Libraries. Journal of Librarianship and Information Science, 45(1): 64--75.
[6]
Yao H, Nie M, Su H, et al. 2017. Predicting Academic Performance Via Semi-supervised Learning with Constructed Campus Social Network. Database Systems for Advanced Applications, 10178: 597--609.
[7]
Puccetti R. 2019. Social Mining From WiFi Campus Data. Sebd 2019-Proceedings of the Italian Symposium on Advanced Database Systems
[8]
Xu JY, Liu T, Yang LT, et al. 2019. Finding College Student Social Networks By Mining the Records of Student Id Transactions. Symmetry-basel, 11(3): 17
[9]
Dutti A, Ismaili MA, Herawani T. 2017. A Systematic Review on Educational Data Mining. Ieee Access, 5: 15991--16005.
[10]
Tang CJ, Lau RWH, Li Q, et al. 2000. Personalized Courseware Construction Based on Web Data Mining. Proceedings of the First International Conference on Web Information Systems Engineering, Vol Ii: Ieee Computer Soc, 204--211.
[11]
Bellaachia A, Vommina E, Berrada B. 2006. Minel: a Framework for Mining E-learning Logs. Proceedings of the Fifth Iasted International Conference on Web-based Education: Acta Press, 0--259.
[12]
Kock M, Paramythis A. 2011. Activity Sequence Modelling and Dynamic Clustering for Personalized E-learning. User Modeling and User-adapted Interaction, 21(1): 51--97.
[13]
Romero C, Ventura S. 2007. Educational Data Mining: a Survey From 1995 to 2005. Expert Systems with Applications, 33(1): 135--146.
[14]
Mohamad SK, Tasir Z. 2013. Educational Data Mining: a Review. 9th International Conference on Cognitive Science: Elsevier Science Bv, 320--324.
[15]
Moodley R, Chiclana F, Carter J, et al. 2020. Using Data Mining in Educational Administration: a Case Study on Improving School Attendance. Applied Sciences-basel, 10(9): 20.
[16]
Naike C, Lei W, Rong F, et al. 2019. Workflow Model Mining Based on Educational Management Data Logs. 2019 Chinese Control and Decision Conference, 5450--5455.
[17]
Dringus LP, Ellis T. 2005. Using Data Mining as a Strategy for Assessing Asynchronous Discussion Forums. Computers & Education, 45(1): 141--160.
[18]
Romero C, Lopez MI, Luna JM, et al. 2013. Predicting Students' Final Performance From Participation in On-line Discussion Forums. Computers & Education, 68: 458--472.
[19]
Hung HC, Liu IF, Liang CT, et al. 2020. Applying Educational Data Mining to Explore Students' Learning Patterns in the Flipped Learning Approach for Coding Education. Symmetry-basel, 12(2): 14.
[20]
Chui KT, Fung DCL, Lytras MD, et al. 2020. Predicting At-risk University Students in a Virtual Learning Environment Via a Machine Learning Algorithm. Computers in Human Behavior, 107: 7.
[21]
Sarra A, Fontanella L, Di zio S. 2019. Identifying Students at Risk of Academic Failure Within the Educational Data Mining Framework. Social Indicators Research, 146(1): 41--60.
[22]
He W. 2013. Examining Students' Online Interaction in a Live Video Streaming Environment Using Data Mining and Text Mining. Computers in Human Behavior, 29(1): 90--102.
[23]
Diaz-torrente X, Quintiliano-scarpelli D. 2020. Dietary Patterns of Breakfast Consumption Among Chilean University Students. Nutrients, 12(2): 13.
[24]
Anna M, Pertti V. 2017. Reader Characteristics, Behavior, and Success in Fiction Book Search. Journal of the Association for Information Science and Technology, 68(9): 2154--2165.
[25]
Xu X, Wang JZ, Peng H, et al. 2019. Prediction of Academic Performance Associated with Internet Usage Behaviors Using Machine Learning Algorithms. Computers in Human Behavior, 98: 166--173.
[26]
Romero C, Ventura S. 2010. Educational Data Mining: a Review of the State of the Art. Ieee Transactions on Systems Man and Cybernetics Part C-applications and Reviews, 40(6): 601--618.
[27]
Hassan H, Anuar S, Ahmad NB. 2019. Students' Performance Prediction Model Using Meta-classifier Approach. Engineering Applications of Neural Networksx: Springer International Publishing Ag, 221--231.
[28]
Ding Guoyong. 2019. Study on Modeling of Academic Performance Data of College Students -- An Analysis based on education Data of A University. Nanjing Normal University
[29]
Zhou Lingyuan. 2015. Library Situational Awareness Service Model and Applied Research. Nanchang University
[30]
Jiang T, Chi Y, Gao H. 2017. A Clickstream Data Analysis of Chinese Academic Library Opac Users' Information Behavior. Library & Information Science Research, 39(3): 213--223.
[31]
Asunka S, Chae HS, Hughes B, et al. 2009. Understanding Academic Information Seeking Habits Through Analysis of Web Server Log Files: the Case of the Teachers College Library Website. Journal of Academic Librarianship, 35(1): 33--45.
[32]
Yao HX, Lian DF, Cao Y, et al. 2019. Predicting Academic Performance for College Students: a Campus Behavior Perspective. Acm Transactions on Intelligent Systems and Technology, 10(3): 21.
[33]
Liu T, Yang LT, Liu SY, et al. 2017. Inferring and Analysis of Social Networks Using Rfid Check-in Data in China. Plos One, 12(6): 18.
[34]
H. pham C.-Shahabi-and-Y.-Liu. 2013. EBM: An entropy-based model to infer social strength from spatiotemporal data. in Proc. ACM SIGMOD Int. Conf. Manag. Data, 265--276.
[35]
Zhang Y, Pang J. 2015. Distance and Friendship: a Distance-based Model for Link Prediction in Social Networks. Asia-pacific Web Conference, 55--67.
[36]
Wang HJ, Li ZH, Lee WC. 2014. Pgt: Measuring Mobility Relationship Using Personal, Global and Temporal Factors. 2014 Ieee International Conference on Data Mining, 570--579.
[37]
Backes M, Humbert M, Pang J, et al. 2017. Walk2friends: Inferring Social Links From Mobility Profiles. Ccs '17: Proceedings of the 2017 Acm Sigsac Conference on Computer and Communications Security, 1943--1957.
[38]
Li J, Zeng FZ, Xiao Z, et al. 2020. Drive2friends: Inferring Social Relationships From Individual Vehicle Mobility Data. Ieee Internet of Things Journal, 7(6): 5116--5127.
[39]
Xu Jingya. 2019. Research on Homogenous Elimination Method in College Students' Social Network Mining. Central China Normal University.
[40]
Lecun Y, Bottou L. 1998. Gradient-based Learning Applied to Document Recognition. Proceedings of the Ieee, 86(11): 2278--2324

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EBIMCS '20: Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science
December 2020
718 pages
ISBN:9781450389099
DOI:10.1145/3453187
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Guilin: Guilin University of Technology, Guilin, China
  • International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2021

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Author Tags

  1. Big data on education
  2. Campus Social Network
  3. Convolutional network
  4. Data dimension reduction
  5. Homogeneity

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  • Research
  • Refereed limited

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EBIMCS 2020

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EBIMCS '20 Paper Acceptance Rate 112 of 566 submissions, 20%;
Overall Acceptance Rate 143 of 708 submissions, 20%

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