The Design of Exploratory Application and Preprocessing of Event Log Data in LMS Moodle-Based Online Learning Activities for Process Mining

Demaspira Aulia, Indra Waspada

DOI: https://doi.org/10.23917/khif.v5i2.8023

Abstract

Process Mining is one of the sub-studies of Data Mining that focuses on the events of a system. An area that benefits from process mining is education, especially online learning. This study used Moodle as a platform to provide online event activity log data in online learning. Moodle-based process mining requires several stages that are not easily understood directly by teachers. As a solution, some efforts are needed to integrate Moodle with process mining. This study built an application that could contribute to the Preprocessing and Exploratory Data Analysis (EDA) stages of Moodle event log data – as an important part of the process mining stage. Preprocessing was implemented by using the simple heuristic filtering method, while EDA was employed through visualization using flow control and dotted charts. Eventually, the application built in this study successfully performed preprocessing in Moodle event log data and could display the results visually, as a tool of control flow analysis and dotted chart analysis.

Keywords

exploratory data analysis; Moodle; process mining

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References

J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques. 2012.

C. Romero and S. Ventura, “Educational Data Mining : A Review of the State of the Art,” IEEE Trans. Syst. Man. Cybern., vol. 40, no. 6, pp. 601–618, 2010.

W. M. P. Van Der Aalst, Process Mining Data Science in Action, 2nd ed. Heidelberg: Springer, 2016.

K. Grigorova, E. Malysheva, and S. Bobrovskiy, “Information Technology and Nanotechnology,” 2017.

A. Bogarín, R. Cerezo, and C. Romero, “A survey on educational process mining,” WIREs Data Min. Knowl. Discov., vol. 8, no. February, pp. 1–17, 2018.

D. R. Ferreira, A Primer on Process Mining. 2017.

A. M. Momani, “Comparison between two Learning Management Systems : Moodle and Blackboard,” Inf. Syst. Behav. Soc. Methods eJorurnal, vol. 2, no. 54, pp. 1–10, 2010.

K. Slaninova, J. Martinovic, P. Drazdilova, and V. Snasel, “From Moodle Log File to the Students Network,” 2014, pp. 641–650.

A. Bogarín, C. Romero, R. Cerezo, and M. Sánchez-santillán, “Clustering for improving Educational Process Mining,” 2014, pp. 11–15.

A. Bogarín, C. Romero, and R. Cerezo, “Discovering students ’ navigation paths in Moodle,” in 8th International Conference on Educational Data Mining, 2015, pp. 556–557.

L. Juhanak, J. Zounek, and L. Rohlíkov, “Using process mining to analyze students ’ quiz-taking behavior patterns in a learning management system,” Comput. Human Behav., vol. 92, pp. 496–506, 2019.

K. Willems, “Python Exploratory Data Analysis Tutorial (article) - DataCamp,” 2017. [Online]. Available: https://www.datacamp.com/community/tutorials/exploratory-data-analysis-python. [Accessed: 10-Dec-2018].

M. Fani Sani, S. J. van Zelts, and W. M. P. Van Der Aalst, “Repairing Outlier Behaviour in Event Logs,” in International Conference on Business Information Systems, 2018, vol. 320, pp. 115–131.

N. Tax, N. Sidorova, and W. M. P. Van Der Aalst, “Discovering more precise process models from event logs by filtering out chaotic activities,” J. Intell. Inf. Syst., vol. 52, no. 1, pp. 107–139, 2019.

R. P. J. C. Bose, R. S. Mans, and W. M. P. Van Der Aalst, “Wanna Improve Process Mining Results ? It ’ s High Time We Consider Data Quality Issues Seriously,” in IEEE Symposium on Computational Intelligence and Data Mining, 2013, pp. 127–134.

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