Moodle Predicta: A Data Mining Tool for Student Follow Up

Igor Moreira Félix, Ana Paula Ambrósio, Priscila Silva Neves, Joyce Siqueira, Jacques Duilio Brancher


Educational data mining (EDM) aims to find useful patterns in large volumes of data from teaching/learning environments, increasing academic results. However, EDM requires previous and deep knowledge of data mining methods and techniques, involving several computing paradigms, preprocessing and results’ interpretation. In this paper, Moodle Predicta, an educational data mining desktop tool is presented. This software is developed in Java and enables non-expert data mining users to enjoy benefits from EDM, within the Moodle system. Divided in two modules, Moodle Predicta allows: (i) visualization of Moodle courses data; and (ii) predict students’ performance.


  1. ABED - Associac¸a˜o Brasileira de Educac¸a˜o a Distaˆncia (2015). Censo EAD.BR: Relatório Analítico da Aprendizagem a Distaˆncia no Brasil 2014. Accessado em 27/07/2016.
  2. Agudo-Peregrina, Í. F., Hernández-García, Í., and Iglesias-Pradas, S. (2012). Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications. In 2012 International Symposium on Computers in Education (SIIE), pages 1-6.
  3. Allen, I. E. and Seaman, J. (2014). Grade change. Tracking Online Education in the United States. Babson Survey Research Group and Quahog Research Group, LLC.
  4. Avlijas?, G., H.-M. A. R. (2016). A guide for association rule mining in moodle course management system. Sinteza 2016 - International Scientific Conference on ICT and E-Business Related Research, pages 56-61.
  5. Baker, R. S. and Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1):3- 17.
  6. Bapu, G. K., Deshmukh, M. P. R., Ashok, M. B., Shamrao, S. P., and Tanaji, S. G. (2015). Clustering moodle data as a tool for profiling students. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4.
  7. Bogarín, A., Romero, C., Cerezo, R., and S ánchezSantillán, M. (2014). Clustering for improving educational process mining. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, LAK 7814, pages 11-15, New York, NY, USA. ACM.
  8. Chayanukro, S., Mahmuddin, M., and Husni, H. (2014). A generalized e-learning usage behaviour model by data mining technique. Journal of Information and Communication Technology, 13(1):37-53. cited By 0.
  9. Danubianu, M. (2015). A data preprocessing framework for students' outcome prediction by data mining techniques. In System Theory, Control and Computing (ICSTCC), 2015 19th International Conference on, pages 836-841.
  10. e Ricardo Araujo e Douglas Detoni, C. C. (2015). Modelagem e predic¸a˜o de reprovac¸a˜o de acadeˆmicos de cursos de educac¸a˜o a distaˆncia a partir da contagem de interac¸o˜es. Revista Brasileira de Informática na Educac¸a˜o, 23(03):1.
  11. EDUCAUSE Center for Analysis and Research (2014). The Current Ecosystem of Learning Management Systems in Higher Education: Student, Faculty, and IT Perspectives. Accessado em 27/07/2016.
  12. García-Saiz, D. and Zorrilla, M. (2012). A promising classification method for predicting distance students' performance. In Educational Data Mining 2012.
  13. Guércio, H., P., M., V., S., P., K., and E., B. (2014). Análise do desempenho estudantil na educac¸a˜o a distaˆncia aplicando técnicas de minerac¸a˜o de dados. Workshop de Minerac¸a˜o de Dados em Ambientes Virtuais do Ensino e Aprendizagem, 3.
  14. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explor. Newsl., 11(1):10-18.
  15. Jindal, R. and Borah, M. D. (2013). A survey on educational data mining and research trends. International Journal of Database Management Systems, 5(3):53.
  16. Kotsiantis, S., Patriarcheas, K., and Xenos, M. (2010). A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education. Knowledge-Based Systems, 23(6):529 - 535.
  17. Luan, J. (2002). Data mining and its applications in higher education. New Directions for Institutional Research, 2002(113):17-36.
  18. Marquez-Vera, C., Morales, C. R., and Soto, S. V. (2013). Predicting school failure and dropout by using data mining techniques. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 8(1):7-14.
  19. (2016a). Moodle Philosophy. Accessed: 2016-10-05.
  20. (2016b). Moodle Statistics. Accessed: 2016-10-05.
  21. Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2):1-7.
  22. Moradi, H., Moradi, S. A., and Kashani, L. (2014). Students' Performance Prediction Using Multi-Channel Decision Fusion, pages 151-174. Springer International Publishing, Cham.
  23. Neto, F. A. A. and Castro, A. (2015). Elicited and mined rules for dropout prevention in online courses. In 2015 IEEE Frontiers in Education Conference (FIE), pages 1-7.
  24. Olama, M. M., Thakur, G., McNair, A. W., and Sukumar, S. R. (2014). Predicting student success using analytics in course learning management systems. Proc. SPIE, 9122:91220M-91220M-9.
  25. Pen˜a-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.
  26. Pierrakeas, C., Xeno, M., Panagiotakopoulos, C., and Vergidis, D. (2004). A comparative study of dropout rates and causes for two different distance education courses. The International Review of Research in Open and Distributed Learning, 5(2).
  27. Romero, C., López, M.-I., Luna, J.-M., and Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68:458 - 472.
  28. Romero, C. and Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1):135 - 146.
  29. Romero, C. and Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6):601-618.
  30. Romero, C., Ventura, S., Espejo, P. G., and Hervás, C. (2008a). Data mining algorithms to classify students. In Educational Data Mining 2008.
  31. Romero, C., Ventura, S., and García, E. (2008b). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1):368 - 384.
  32. Sharma, M. and Mavani, M. (2011). Accuracy comparison of predictive algorithms of data mining: Application in education sector. Communications in Computer and Information Science, 125 CCIS:189-194. cited By 0.
  33. Sisovic, S., Matetic, M., and Bakaric, M. B. (2016). Clustering of imbalanced moodle data for early alert of student failure. In 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pages 165-170.
  34. Sorour, S. E., Goda, K., and Mine, T. (2015). Estimation of student performance by considering consecutive lessons. In 2015 IIAI 4th International Congress on Advanced Applied Informatics, pages 121-126.
  35. Thakar, P. (2015). Performance analysis and prediction in educational data mining: A research travelogue. CoRR, abs/1509.05176.
  36. Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., and Zhong, N. (2013). Detection and Presentation of Failure of Learning from Quiz Responses in Course Management Systems, pages 64-73. Springer International Publishing, Cham.
  37. Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27:44 - 53.
  38. Zorrilla, M. and Garcia-Saiz, D. (2014). Meta-learning: Can it be suitable to automatise the kdd process for the educational domain? Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8537 LNAI:285-292. cited By 0.

Paper Citation

in Harvard Style

Moreira Félix I., Ambrósio A., Silva Neves P., Siqueira J. and Duilio Brancher J. (2017). Moodle Predicta: A Data Mining Tool for Student Follow Up . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-239-4, pages 339-346. DOI: 10.5220/0006318403390346

in Bibtex Style

author={Igor Moreira Félix and Ana Paula Ambrósio and Priscila Silva Neves and Joyce Siqueira and Jacques Duilio Brancher},
title={Moodle Predicta: A Data Mining Tool for Student Follow Up},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,},

in EndNote Style

JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Moodle Predicta: A Data Mining Tool for Student Follow Up
SN - 978-989-758-239-4
AU - Moreira Félix I.
AU - Ambrósio A.
AU - Silva Neves P.
AU - Siqueira J.
AU - Duilio Brancher J.
PY - 2017
SP - 339
EP - 346
DO - 10.5220/0006318403390346