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[14] Constantinos M. Kokkinos, Apostolos Kargiotidis, Angelos Markos,The relationship between learning and study strategies and big five personality traits among junior university student teachers,Learning and Individual Differences 43 (2015) 39–47
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[16] Heller, M.L., & Marchant, G.J. (2015). Facilitating self-regulated learning skills and achievement with a strategic content learning approach. Community College Journal of Research and Practice. Advanced online publicationhttp://dx.doi.org/10.1080/10668926.2014.908752.
[17] Yeonjeong Park, JiHyunYu, Il-HyunJo,Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute,Internet and Higher Education 29 (2016) 1–11
[18] Halverson, L. R., Graham, C. R., Spring, K. J., Drysdale, J. S., & Henrie, C. R. (2014). Athematic analysis of the most highly cited scholarship in the first decade of blended learning research. The Internet and Higher Education, 20,20–34.
[19] Wen-Lung Shiau,Yogesh K. Dwivedi,Han Suan Yang,"Co-citation and cluster analyses of extant literatureon social networks",International Journal of Information Management 37 (2017) 390–399
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[21] Samuel K.W. Chu, Catherine M. Capio, Jan C.W. van Aalst,Eddie W.L. Cheng,Evaluating the use of a social media tool for collaborative group writing of secondary school students in Hong Kong,Computers & Education 110 (2017) 170-180
[2] Ji, J., Zhou, C., Wang, Z. & Yan, H. 2011. Maximizing The Community Coverage Of Influence Through A Social Network. AISS: Advances In Information Sciences And Service Sciences,Vol. 3, No. 9, Pp. 339 ~ 346, 2011.http://dx.doi.org/10.4156/aiss.vol3.issue9.44
[3] Zhou, B. & Wu, C. 2011. Semantic Model For Social Networking Federation. Aiss: Advances In Information Sciences And Service Sciences, Vol. 3, No. 11, Pp. 213 ~ 223, 2011.
[4] Erlin B., Yusof, N. & Rahman, A. A.2009. Analyzing Online Asynchronous Discussion Using Content And Social Network Analysis In: Ieee-Ninth International Conference On Intelligent Systems Design And Applications, 2009.http://dx.doi.org/10.1109/isda.2009.40
[5] Ortiza, F. & Fraile, R. 2009. Social Network Featuring Entertainment, Culture And Technology In Spanish Universities: The Infocampus Project The Open Information Systems Journal, 3, 48-53. http://dx.doi.org/10.2174/1874133900903010048
[6] Kepp, S.-J. & Schorr, H. Year. Analyzing Collaborative Learning Activities In Wikis Using Social Network Analysis. In: Chi, Spotlight On Work In Progress~Session 2, April 4-9, Boston, Ma, Usa. Acm, 2009.
[7] Twu, H.-L. 2010. A Predictive Study Of Wiki Interaction:Can Attitude Toward Wiki Predict Wiki Interaction In High-Context Cultures Groups? Journal of Educational Technology Development and Exchange, 3(1), 57-68.
[8] Pasquale De Meo, Fabrizio Messina, Domenico Rosaci, Giuseppe M.L. Sarné,"Combining trust and skills evaluation to form e-Learning classes in online social networks, Information Science,Vol 405,2017,107-122
[9] R.Hübscher,Assigning students to groups using general and context specific criteria, Learn.Technol. IEEE Trans.3(3) (2010), 178–189.
[10] A.A.Rad,M.Benyoucef, Similarity and ties in social networks: a study of the youtube social network, J.Inf.Syst.Appl.Res.(2014)14–65.
[11] H. Roreger , T.C. Schmidt , Socialize online learning: Why we should integrate learning contentmanagement with online social networks, in: Pervasive Computing and Communications Workshops,2012 IEEE International Conference, 2012, pp.685–690.
[12] M. Erdt , A. Fernandez , C. Rensing , Evaluating recommender systems for technology enhanced learning: a quantitative survey, Learn. Technol. IEEE Trans.8(4)(2015)326–
[13] P. Grabowicz, L. Aiello , V. Eguiluz , A. Jaimes , Distinguishing topical and social groups based on common identity and bond theory, in: Proc. of the ACM Int.Conf.WSDM2013,ACM,2013,pp.627–636.
[14] Constantinos M. Kokkinos, Apostolos Kargiotidis, Angelos Markos,The relationship between learning and study strategies and big five personality traits among junior university student teachers,Learning and Individual Differences 43 (2015) 39–47
[15] Komarraju, M., Karau, S.J., Schmeck, R.R., & Avdic, A. (2011). The big five personality traits, learning styles, and academic achievement. Personality and Individual Differences, 51,472-477
[16] Heller, M.L., & Marchant, G.J. (2015). Facilitating self-regulated learning skills and achievement with a strategic content learning approach. Community College Journal of Research and Practice. Advanced online publicationhttp://dx.doi.org/10.1080/10668926.2014.908752.
[17] Yeonjeong Park, JiHyunYu, Il-HyunJo,Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute,Internet and Higher Education 29 (2016) 1–11
[18] Halverson, L. R., Graham, C. R., Spring, K. J., Drysdale, J. S., & Henrie, C. R. (2014). Athematic analysis of the most highly cited scholarship in the first decade of blended learning research. The Internet and Higher Education, 20,20–34.
[19] Wen-Lung Shiau,Yogesh K. Dwivedi,Han Suan Yang,"Co-citation and cluster analyses of extant literatureon social networks",International Journal of Information Management 37 (2017) 390–399
[20] Chai, S., & Kim, M. (2012). A socio-technical approach to knowledge contribution behavior: An empirical investigation of social networking sites users, International Journal of Information Management, 32, 118–126
[21] Samuel K.W. Chu, Catherine M. Capio, Jan C.W. van Aalst,Eddie W.L. Cheng,Evaluating the use of a social media tool for collaborative group writing of secondary school students in Hong Kong,Computers & Education 110 (2017) 170-180
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Affiliations
Andi Besse Firdausiah Mansur
Department of Computer Science, Faculty of Computing and Information Technology Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia
Norazah Yusof
Department of Computer Science, Faculty of Computing and Information Technology Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia
Comparative classification of student's academic failure through Social Network Mining and Hierarchical Clustering
Abstract
Student academic failure are caused by several factors such as: family relationship, study time, absence, parent education, travel time and etc. This study observe several factors which are related to student academic failure by calculating the centrality degree between students to find the correlation between failure factors for each students. Furthermore, each student will be measured by measuring the geodesic distance for each factors for hierarchical clustering. The flow betwenness measure and hierarchical clustering show the promising result, where students who has similar factors value are tends to be grouped together in the same cluster. The student with high value of flow betwenness is considered as broker of network and play vital character inside network. The result of study is believed can bring important and useful information toward the student performance analysis for future better education.