The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification
Since the booming of “big data” or “data analytic” topics, it has drawn attention toward several research areas such as: student behavior classification, video surveillance, automatic navigation and etc. This paper present k-mean clustering technique to monitor and assess the student performance and behavior as well as give improvement toward e-learning system in the future. Data set of student performance along with teacher attributes are collected then analyzed, it was filtered into 6 attributes of teacher that may potentially affect the student performance. Afterwards, k-mean clustering applied into the filtered data set to generate particular cluster number. The result reveal that Teacher1 statistically hold the highest density (0.27) and teachers with good speech/lectures tend to have strong correlation with another factor such as: commitment of teacher on preparing lecture material and time management utilization. If this synergy between teacher and student running flawlessly, it will be great achievement for e-learning system to the society.
B. Chen, Y.-H. Chang, F. Ouyang and W. Zhou, “Fostering student engagement in online discussion through social learning analytics,” The Internet and Higher Education 37 (2018) 21–30, vol. 37, no. 2018, pp. 21-30, 2018.
Y. Yang, H. Wu and J. Cao, “SmartLearn: Predicting Learning Performance and Discovering Smart Learning Strategies in Flipped Classroom,” in International Conference on Orange Technologies, Melbourne, 2016.
A. Mansur, N. Yusof and A. Basori, “Comprehensive analysis of Student’s Academic Failure Classification through Role-Sphere Influence and Flow betwenness centrality,” Procedia Computer Science, vol. 116, pp. 509-515, 2017. https://doi.org/10.1016/j.procs.2017.10.031.
J. Ji, C. Zhou, Z. Wang and H. Yang, “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.
B. Zhou and C. Wu, “Semantic Model for Social Networking Federation,” AISS (Advances in Information Sciences and Service Sciences), vol. 3, no. 11, pp. 213-223, 2011.
A. Basori, A. Tenriawaru and A. Mansur, “Intelligent Avatar on E-learning Using Facial Expression and Haptic,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 9, no. 1, pp. 115-124, 2011. http://dx.doi.org/10.12928/telkomnika.v9i1.676.
F. Ortiza and R. Fraile, “Social Network Featuring Entertainment, Culture and Technology in Spanish Universities: The Infocampus Project,” Open Information Systems Journal, vol. 3, no. 1, pp. 48-53, 2009.
S. J. Kepp and H. Schorr, “Analyzing Collaborative Learning Activities in Wikis Using Social Network Analysis,” in CHI '09 Extended Abstracts on Human Factors in Computing Systems, Boston, MA, USA, 2009.
H.-L. Twu, “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 (JETDE), vol. 3, no. 1, 2010. 10.18785/jetde.0301.05.
P. De Meo, F. Messina, D. Rosaci and G. M. Sarné, “Combining trust and skills evaluation to form e-Learning classes in online social networks,” Information Sciences, vol. 405, pp. 107-122, September 2017. https://doi.org/10.1016/j.ins.2017.04.002.
R. Hubsche, “Assigning Students to Groups Using General and Context-Specific Criteria,” IEEE Transactions on Learning Technologies, vol. 3, no. 3, pp. 178-189, 2010. https://doi.org/10.1109/TLT.2010.17.
A. Rad and M. Benyoucef, “Similarity and ties in social networks: a study of the youtube social network,” Journal of Information Systems Applied Research, vol. 7, no. 4, 2014.
H. Roreger and T. C. Schmidt, “Socialize online learning: Why we should integrate learning content management with Online Social Networks,” in 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, Lugano, Switzerland, 2012. https://doi.org/10.1109/PerComW.2012.6197601.
M. Erdt, A. Fernández and C. Rensing, “Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey,” IEEE Transactions on Learning Technologies, vol. 8, no. 4, pp. 326 - 344, 2015. https://doi.org/10.1109/TLT.2015.2438867.
P. A. Grabowicz, L. M. Aiello, V. M. Eguiluz and A. Jaimes, “Distinguishing topical and social groups based on common identity and bond theory,” in Proceedings of the sixth ACM international conference on Web search and data mining, Rome, Italy, 2013.
C. M. Kokkinos, A. Kargiotidis and A. Markos, “The relationship between learning and study strategies and big five personality traits among junior university student teachers,” Learning and Individual Differences, vol. 43, pp. 39-47, 2015. https://doi.org/10.1016/j.lindif.2015.08.031.
M. Komarraju, S. J. Karau, R. R. Schmeck and A. Avdic, “The Big Five personality traits, learning styles, and academic achievement,” Personality and Individual Differences, vol. 51, no. 4, pp. 472-477, 2011. https://doi.org/10.1016/j.paid.2011.04.019.
M. Heller and G. Marchant, “Facilitating Self-Regulated Learning Skills and Achievement With a Strategic Content Learning Approach,” Community College Journal of Research and Practice, vol. 39, no. 9, pp. 808-818, 2015. https://doi.org/10.1080/10668926.2014.908752.
Y. Park, J. Yu and I. Jo, “Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute,” The Internet and Higher Education, vol. 29, pp. 1-11, 2016.
L. Halverson, C. Graham, K. Spring, J. Drysdale and C. Henrie, “Athematic analysis of the most highly cited scholarship in the ﬁrst decade of blended learning research,” The Internet and Higher Education, vol. 20, no. 1, 2014..
W. Shiau, Y. Dwivedi and H. Yang, “Co-citation and cluster analyses of extant literature on social networks,” International Journal of Information Management, vol. 37, no. 1, 2017.
S. Chai and M. Kim, “A socio-technical approach to knowledge contribution behavior: An empirical investigation of social networking sites users,” International Journal of Information Management, vol. 32, no. 1, 2012.
S. K. Chu, C. M. Capio, J. C. van Aalst and E. W. Cheng, “Evaluating the use of a social media tool for collaborative group writing of secondary school students in Hong Kong,” Computers & Education, vol. 110, pp. 170-180, 2017.
E. Fokoue and N. Gunduz, “UCI Machine Learning Repository : Turkiye Student Evaluation Data Set,” 2013.
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