Student Performance Based on Activity Log on Social Network and e-Learning

- Agusriandi, Imas Sukaesih Sitanggang, Sony Hartono Wijaya

Abstract


Learning activities in social networks and e-learning platforms bring massive activity log in the database, making it challenging to measure students’ performance. Data mining technique and social network analysis provide some benefits in the field of education in discovering knowledge from hidden information of student’s activities on e-learning and social network environment. This study aims to identify dominant students on social network group based on centrality values and to analyze log data from the activities on e-learning using process mining technique. Centrality value was measured by analyzing data quality or data pre-processing, creating the network, measuring the network, and highlighting degrees and layouts. The process mining technique included data pre-processing, discovering process, and conformance checking. This study found that dominant students were identified from a high hub score and authority. This study also found a free-rider student. The presence of dominant students and free-riders made the collaboration of social network group are weak. This study also found that student performance on e-learning has been discovered where the student’s activity, namely, the course module viewed and course viewed, were more frequent than other activities. On the other hand, an optimum fitness value was obtained, i.e., 0.94 on all the processes of e-learning. This study provides insights that can be used to improve student collaboration and to enhance online learning activities.

Keywords


activity log; e-learning; performance; social network

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References


PISA. (2015) Programme for International Student Assessment, “Pisa Results in Focus,” PISA. [Online]. Available: https://www.oecd.org/pisa/pisa-2015-results-in-focus.pdf.

A. Mueen, B. Zafar, dan U. Manzoor, “Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques,” Int. J. Mod. Educ. Comput. Sci., vol. 8, no. 11, pp. 36–42, Nov. 2016.

M. Ciolacu, A. F. Tehrani, R. Beer, dan H. Popp, “Education 4.0—Fostering student’s performance with machine learning methods,” in 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME), 2017, pp. 438–443.

Y. Kim, “The Framework of Cloud e-Learning System for Strengthening ICT Competence of Teachers in Nicaragua,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, pp. 62–67, Feb. 2018.

F. H. Wang, “An exploration of online behaviour engagement and achievement in flipped classroom supported by learning management system,” Comput. Educ., vol. 114, no.1 pp. 79–91, 2017.

A. Krouska, C. Troussas, dan M. Virvou, “SN ‐ Learning: An exploratory study beyond e ‐ learning and evaluation of its applications using EV ‐ SNL framework,” J Comput Assist Learn, vol. 35, no.1 pp. 168–177, Oct. 2018.

A. Singh, “Mining of Social Media data of University students,” Educ. Inf. Technol., vol. 22, no. 4, pp. 1515–1526, 2017.

J.-H. Lam dan W. W. K. Ma, “When and how does learning satisfy? Working collaboratively online with a clear purpose,” Int. J. Innov. Learn., vol. 23, no. 4, pp. 400–415, 2018.

A. E. E. Sobaih, M. A. Moustafa, P. Ghandforoush, dan M. Khan, “To use or not to use? Social media in higher education in developing countries,” Comput. Human Behav., vol. 58, pp. 296–305, 2016.

A. Bogarin, R. Cerezo, dan C. Romero, “Discovering learning processes using Inductive Miner: A case study with Learning Management Systems (LMSs),” Psicothema, vol. 30, no. 3, pp. 322–329, 2018.

N. F. Kolan, N. Jailani, M. Abu Bakar, dan R. Latih, “Trust Model Based on Islamic Business Ethics and Social Network Analysis,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, no. 6, pp. 2323, 2018.

P. M. T. Crespo, “Social networks exploration for educational data mining,” Instituto Superior Técnico, Lisboa (PT), 2013.

F. Elghibari, R. Elouahbi, dan F. El Khoukhi, “Data Mining for Detecting E-learning Courses Anomalies: An Application of Decision Tree Algorithm,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, no. 3, pp. 980, 2018.

I. Yurek, D. Birant, dan K. U. Birant, “Interactive process miner: a new approach for process mining,” Turk J Elec Eng Comp Sci, vol. 26, pp. 1314–1328, 2018.

J. G. Rigby, “Principals’ conceptions of instructional leadership and their informal social networks: An exploration of the mechanisms of the mesolevel,” Am. J. Educ., vol. 122, no. 3, pp. 433–464, 2016.

S. Ahajjam, M. El Haddad, dan H. Badir, “A new scalable leader-community detection approach for community detection in social networks,” Soc. Networks, vol. 54, hal. 41–49, 2018.

E. Rojas, J. Munoz-Gama, M. Sepúlveda, dan D. Capurro, “Process mining in healthcare: A literature review.,” J. Biomed. Inform., vol. 61, pp. 224–36, 2016.

A. Bogarín, R. Cerezo, dan C. Romero, “Discovering learning processes using inductive miner: A case study with learning management systems (LMSs),” Psicothema, vol. 30, no. 3, pp. 322–329, 2018.

J. Munoz-gama, Conformance Checking and Diagnosis in Process Mining, Comparing Observed and Modeled Processes, 1 ed. Chile: Springer, 2016.

K. L. Vogt, “Measuring Student Engagement Using Learning Management Systems,” University of Toronto, Toronto (CA), 2016.

A. Brodsky, G. Shao, M. Krishnamoorthy, A. Narayanan, D. Menascé, dan R. Ak, “Analysis and optimization based on reusable knowledge base of process performance models,” Int. J. Adv. Manuf. Technol., vol. 88, no. 1–4, pp. 337–357, 2016.

J. Hagerty, “2017 Planning Guide for Data and Analytics,” Gartner, 2016. [Online]. Available: https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/2017_planning_guide_for_data_analytics.pdf.

R. Jugulum, “Importance of Data Quality for Analytics,” in Quality in the 21st Century, Springer, 2016, pp. 23–31.

D. C. Corrales, A. Ledezma, dan J. C. Corrales, “From Theory to Practice: A Data Quality Framework for Classification Tasks,” Symmetry (Basel)., vol. 10, pp. 1–29, 2018.

S.-H. Cheong dan Y.-W. Si, “Accelerating the Kamada-Kawai algorithm for boundary detection in a mobile ad hoc network,” ACM Trans. Sens. Networks, vol. 13, no. 1, pp. 3, 2017.

R. Conforti, M. Dumas, L. García-Bañuelos, dan M. La Rosa, “BPMN miner: automated discovery of BPMN process models with hierarchical structure,” Inf. Syst., vol. 56, pp. 284–303, 2016.

W. M. P. Van der Aalst, Process mining: data science in action, 2 ed. London: Springer, 2016.

B. Van Dongen, J. Carmona, dan T. Chatain, “A Unified Approach for Measuring Precision and Generalization Based on Anti-alignments,” in 14th International Conference on Business Process Man- agement (BPM’16), 2016, pp. 39–56.

A. Burattin, F. M. Maggi, dan A. Sperduti, “Conformance checking based on multi-perspective declarative process models,” Expert Syst. Appl., vol. 65, pp. 194–211, 2016.

J. Siles-González dan C. Solano-Ruiz, “Self-assessment, reflection on practice and critical thinking in nursing students,” Nurse Educ. Today, vol. 45, pp. 132–137, 2016.




DOI: http://dx.doi.org/10.18517/ijaseit.10.6.8753

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