Research Article
MoodleMiner: Data Mining Analysis Tool for Moodle Learning Management System
Abstract
The purpose of this study is to
develop a tool by which non-experts can carry out basic data mining analyses on
logs they obtained via the Moodle learning management system. The study also
includes findings obtained by applying the developed tool on a data set from a
real course. The developed tool automatically extracts features regarding
student interactions with the learning system by using their click-stream data,
and analyzes these data by using the data mining libraries available in the R
programming language. The tool has enabled users who do not have any expertise
in data mining or programming to automatically carry out data mining analyses.
The information generated by the tool will help researchers and educators alike
in grouping students by their interaction levels, determining at-risk students,
monitoring students' interaction levels, and identifying important features
that impact students’ academic performances. The data processed by the tool can
also be exported to be used in various other analyses.
Keywords
Educational data mining learning analytics Moodle R learning management system log analysis tool
References
- Aguilar, D. A. G., Therón, R., & Peñalvo, F. G. (2008). Understanding educational relationships in Moodle with ViMoodle. Paper presented at the Advanced Learning Technologies, 2008. ICALT'08. Eighth IEEE International Conference on. Amershi, S., & Conati, C. (2007). Unsupervised and supervised machine learning in user modeling for intelligent learning environments. Paper presented at the Proceedings of the 12th international conference on Intelligent user interfaces, Honolulu, Hawaii, USA. Baker, R., & Siemens, G. (2014). Educational Data Mining and Learning Analytics. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (2 ed., pp. 253-272). Cambridge: Cambridge University Press. Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. J. Educ. DataMining, 1(1), 3-17. Baker, R. S. J. d. (2007). Modeling and understanding students' off-task behavior in intelligent tutoring systems. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, San Jose, California, USA. Baker, R. S. J. d. (2010). Data Mining. In International Encyclopedia of Education (Third Edition) (pp. 112-118). Oxford: Elsevier. Bakharia, A., & Dawson, S. (2011). SNAPP: a bird's-eye view of temporal participant interaction. Paper presented at the Proceedings of the 1st international conference on learning analytics and knowledge. Beal, C. R., Qu, L., & Lee, H. (2006). Classifying learner engagement through integration of multiple data sources. Paper presented at the Proceedings of the 21st national conference on Artificial intelligence - Volume 1, Boston, Massachusetts. Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. Int. J. Technol. Enhanc. Learn., 4(5/6), 318-331. doi:10.1504/ijtel.2012.051815 Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., . . . Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. e-learning and education : eleed(10). Drăgulescu, B., Bucos, M., & Vasiu, R. (2015). CVLA: integrating multiple analytics techniques in a custom moodle report. Paper presented at the International Conference on Information and Software Technologies. García, E., Romero, C., Ventura, S., & De Castro, C. (2009). An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction, 19(1-2), 99-132. Hämäläinen, W., & Vinni, M. (2010). Classifiers for Educational Data Mining. In Handbook of Educational Data Mining (pp. 57-74): CRC Press. IntelliBoard. (2015). IntelliBoard.net. Retrieved from http://www.intelliboard.net/ Kotsiantis, S. (2009). Educational data mining: A case study for predicting dropout-prone students. Int. J. Knowl. Eng. Soft Data Paradigm., 1(2), 101-111. doi:10.1504/ijkesdp.2009.022718 Liu, D. Y.-T., Froissard, J.-C., Richards, D., & Atif, A. (2015). An enhanced learning analytics plugin for Moodle: student engagement and personalised intervention. Mazza, R., Bettoni, M., Faré, M., & Mazzola, L. (2012). Moclog–monitoring online courses with log data. Mazza, R., & Milani, C. (2004). Gismo: a graphical interactive student monitoring tool for course management systems. Paper presented at the International Conference on Technology Enhanced Learning, Milan. Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4, Part 1), 1432-1462. doi:10.1016/j.eswa.2013.08.042 Perez, R. P., Romero, C., & Ventura, S. (2010). A Java desktop tool for mining Moodle data. Paper presented at the Educational Data Mining 2011. R Core Team. (2017). R: A language and environment for statistical computing: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ Rabbany, R., Takaffoli, M., & Zaïane, O. R. (2011). Analyzing participation of students in online courses using social network analysis techniques. Paper presented at the Proceedings of educational data mining. Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006). Towards networked learning analytics—A concept and a tool. Paper presented at the Proceedings of the fifth international conference on networked learning. Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6), 601-618. doi:10.1109/TSMCC.2010.2053532 Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27. doi:10.1002/widm.1075 Singh, J. (2015). New Block: Analytics graphs. Retrieved from http://www.moodleworld.com/new-block-analytics-graphs/ Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. doi:doi:10.1111/1467-9868.00293 Zafra, A., Romero, C., & Ventura, S. (2013). DRAL: a tool for discovering relevant e-activities for learners. Knowledge and information systems, 36(1), 211-250. Zorrilla, M., & García-Saiz, D. (2013). A service oriented architecture to provide data mining services for non-expert data miners. Decision Support Systems, 55(1), 399-411.
Abstract
References
- Aguilar, D. A. G., Therón, R., & Peñalvo, F. G. (2008). Understanding educational relationships in Moodle with ViMoodle. Paper presented at the Advanced Learning Technologies, 2008. ICALT'08. Eighth IEEE International Conference on. Amershi, S., & Conati, C. (2007). Unsupervised and supervised machine learning in user modeling for intelligent learning environments. Paper presented at the Proceedings of the 12th international conference on Intelligent user interfaces, Honolulu, Hawaii, USA. Baker, R., & Siemens, G. (2014). Educational Data Mining and Learning Analytics. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (2 ed., pp. 253-272). Cambridge: Cambridge University Press. Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. J. Educ. DataMining, 1(1), 3-17. Baker, R. S. J. d. (2007). Modeling and understanding students' off-task behavior in intelligent tutoring systems. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, San Jose, California, USA. Baker, R. S. J. d. (2010). Data Mining. In International Encyclopedia of Education (Third Edition) (pp. 112-118). Oxford: Elsevier. Bakharia, A., & Dawson, S. (2011). SNAPP: a bird's-eye view of temporal participant interaction. Paper presented at the Proceedings of the 1st international conference on learning analytics and knowledge. Beal, C. R., Qu, L., & Lee, H. (2006). Classifying learner engagement through integration of multiple data sources. Paper presented at the Proceedings of the 21st national conference on Artificial intelligence - Volume 1, Boston, Massachusetts. Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. Int. J. Technol. Enhanc. Learn., 4(5/6), 318-331. doi:10.1504/ijtel.2012.051815 Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., . . . Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. e-learning and education : eleed(10). Drăgulescu, B., Bucos, M., & Vasiu, R. (2015). CVLA: integrating multiple analytics techniques in a custom moodle report. Paper presented at the International Conference on Information and Software Technologies. García, E., Romero, C., Ventura, S., & De Castro, C. (2009). An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction, 19(1-2), 99-132. Hämäläinen, W., & Vinni, M. (2010). Classifiers for Educational Data Mining. In Handbook of Educational Data Mining (pp. 57-74): CRC Press. IntelliBoard. (2015). IntelliBoard.net. Retrieved from http://www.intelliboard.net/ Kotsiantis, S. (2009). Educational data mining: A case study for predicting dropout-prone students. Int. J. Knowl. Eng. Soft Data Paradigm., 1(2), 101-111. doi:10.1504/ijkesdp.2009.022718 Liu, D. Y.-T., Froissard, J.-C., Richards, D., & Atif, A. (2015). An enhanced learning analytics plugin for Moodle: student engagement and personalised intervention. Mazza, R., Bettoni, M., Faré, M., & Mazzola, L. (2012). Moclog–monitoring online courses with log data. Mazza, R., & Milani, C. (2004). Gismo: a graphical interactive student monitoring tool for course management systems. Paper presented at the International Conference on Technology Enhanced Learning, Milan. Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4, Part 1), 1432-1462. doi:10.1016/j.eswa.2013.08.042 Perez, R. P., Romero, C., & Ventura, S. (2010). A Java desktop tool for mining Moodle data. Paper presented at the Educational Data Mining 2011. R Core Team. (2017). R: A language and environment for statistical computing: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ Rabbany, R., Takaffoli, M., & Zaïane, O. R. (2011). Analyzing participation of students in online courses using social network analysis techniques. Paper presented at the Proceedings of educational data mining. Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006). Towards networked learning analytics—A concept and a tool. Paper presented at the Proceedings of the fifth international conference on networked learning. Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6), 601-618. doi:10.1109/TSMCC.2010.2053532 Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27. doi:10.1002/widm.1075 Singh, J. (2015). New Block: Analytics graphs. Retrieved from http://www.moodleworld.com/new-block-analytics-graphs/ Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. doi:doi:10.1111/1467-9868.00293 Zafra, A., Romero, C., & Ventura, S. (2013). DRAL: a tool for discovering relevant e-activities for learners. Knowledge and information systems, 36(1), 211-250. Zorrilla, M., & García-Saiz, D. (2013). A service oriented architecture to provide data mining services for non-expert data miners. Decision Support Systems, 55(1), 399-411.
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