Abstract
Learning students social affinity and modeling their social networks are beneficial for instructors to design proper pedagogical strategies. Students seating distribution contains social data and can be used for analysing their social relationships. In this paper, we propose a method to automatically construct the class social network and predict the position of a student’s seat in class. First, we determine the positions of each student in a classroom by utilizing the center projection principle and linear fitting algorithms. The intimate relationship between students is captured to model their social network based on Euclidean distance. Then, we learn the social affinities from the Social Affinity Map (SAM) which clusters the relative positions of surrounding students. Based on this, students’ seating distribution can be predicted successfully with accuracy reaching 82.1%.
This work is supported by the National Natural Science Foundation of China (No. 61501286, No. 61402274, No. 61672333, No. 61702251), The Key Research and Development Program in Shaanxi Province of China (No. 2018GY-008), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2015JQ6208, No. 2018JM6068, No. 2018JM6030), the Fundamental Research Funds for the Central Universities (No. GK201702015) and the China Scholarship Council.
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Mohamed, A., Kamal, M.: Association of student’s position in a classroom and student’s academic performance using ANOVA. In: Proceedings of the 2015 15th International Conference on e-Learning, pp. 392–395 (2015)
Ji, S., Lu, X., Xu, Q.: Fast face detection method combining skin color feature and adaboost. In: Proceedings of the 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), pp. 1–5 (2014)
Alahi, A., Ramanathan, V., Li, F.F.: Socially-aware large-scale crowd forecasting. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2211–2218 (2014)
Shin-ike, K., Lima, H.: A method for determining classroom seating arrangements by using a genetic algorithm. In: Proceedings of the 2012 12th International Conference on Control, Automation and Systems (ICCAS), pp. 29–33 (2012)
Mc Gowan, A., Hanna, P., Greer, D.: Learning to program-does it matter where you sit in the lecture theatre? In: Proceedings of the 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 624–629 (2017)
Zeng, S., Zhang, J.,: Analyse social influence on student motivation based on social activity network. In: Proceedings of the 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 133–138 (2012)
Ji, Q.G., Chi, R., Lu, Z.M.: Anomaly detection and localisation in the crowd scenes using a block-based social force model. IET Image Process. 12, 133–137 (2012)
Qian, Y., Yuan, H., Gong, M.: Budget-driven big data classification. In: Barbosa, D., Milios, E. (eds.) CANADIAN AI 2015. LNCS (LNAI), vol. 9091, pp. 71–83. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18356-5_7
Cao, N.B., et al.: Destination and route choice models for bidirectional pedestrian flow based on the social force model. IET Intell. Transp. Syst. 11, 537–545 (2017)
Hong, M., Jung, J.J., Camacho, D.: GRSAT: a novel method on group recommendation by social affinity and trustworthiness. Cybern. Syst. 48(3), 140–161 (2017)
Isba, R., Woolf, K., Hanneman, R.: Social network analysis in medical education. Med. Educ. 51, 81–88 (2017)
Chvanova, M.S., Hramov, A.E., Khramova, M.V.: Is it possible to improve the university education with social networks: the opinion of students and teachers. In: Proceedings of the 2016 IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS), pp. 33–38 (2016)
Krouska, A., Troussas, C., Virvou, M.: Social networks as a learning environment: developed applications and comparative analysis. In: Proceedings of the 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–6 (2017)
Al-Oqily, I., Abdallah, E., Abdallah, A.: Mobile intra-campus student social network. In: Proceedings of the 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–4 (2017)
Becheru, A., Popescu, E.: Using social network analysis to investigate students’ collaboration patterns in eMUSE platform. In: Proceedings of the 2017 21st International Conference on System Theory, Control and Computing (ICSTCC), pp. 266–271 (2017)
Halawa, M.S., Shehab, M.E., Hamed, E.M.R.: Predicting student personality based on a data-driven model from student behavior on LMS and social networks. In: Proceedings of the 2015 15th International Conference on Digital Information Processing and Communications (ICDIPC), pp. 294–299 (2015)
Gremmen, M.C., van den Berg, Y., Segers, E., Cillessen, A.H.: Considerations for classroom seating arrangements and the role of teacher characteristics and beliefs. Soc. Psychol. Educ. 19, 1–26 (2016)
Li, X., Zhang, Y., Bai, Y.Q., Chiang, F.K.: An investigation of university students classroom seating choices. J. Learn. Spaces (2017)
Upreti, M., Kumar, V.: Learning the student’s sufferings using social networks. In: Proceedings of the 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 319–322 (2017)
Wei, X.Y., Yang, Z.Q.: Mining in-class social networks for large-scale pedagogical analysis. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 639–648 (2012)
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Pei, Z., Pan, M., Liao, K., Ma, M., Leng, C. (2018). Predicting Student Seating Distribution Based on Social Affinity. In: Basu, A., Berretti, S. (eds) Smart Multimedia. ICSM 2018. Lecture Notes in Computer Science(), vol 11010. Springer, Cham. https://doi.org/10.1007/978-3-030-04375-9_3
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DOI: https://doi.org/10.1007/978-3-030-04375-9_3
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