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Academic Behavior Analysis in Virtual Courses Using a Data Mining Approach

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1051)

Abstract

Virtual education is one of the educational trends of the 21st century; however knowing the perception of students is a new challenge. This article presents a proposal to define the essential components for the construction of a model for the analysis of the records given by the students enrolled in courses in a virtual learning platform (VLE). The article after a review of the use of data analytics in VLE presents a strategy to characterize the data generated by the student according to the frequency and the slice of the day and week that access the material. With these metrics, clustering analysis is performed and visualized through a map of self-organized Neural Networks. The results presented correspond to five courses of a postgraduate career, where was found that students have greater participation in the forums in the daytime than in the nighttime. Also, they participate more during the week than weekends. These results open the possibility to identify possible early behaviors, which let to implement tools to prevent future desertions or possible low academic performance.

Keywords

Learning management systems Educational data mining SOM Networks Virtual education 

References

  1. 1.
    Alias, U.F., Ahmad, N.B., Hasan, S.: Mining of E-learning behavior using SOM clustering. In: 6th ICT International Student Project Conference: Elevating Community Through ICT, ICT-ISPC 2017, pp. 1–4 (2017).  https://doi.org/10.1109/ICT-ISPC.2017.8075350
  2. 2.
    Bara, M.W., Ahmad, N.B., Modu, M.M., Ali, H.A.: Self-organizing map clustering method for the analysis of e-learning activities. In: 2018 Majan International Conference (MIC), pp. 1–5, March 2018.  https://doi.org/10.1109/MINTC.2018.8363155
  3. 3.
    Baruque, C.B., Amaral, M.A., Barcellos, A., da Silva Freitas, J.a.C., Longo, C.J.: Analysing users’ access logs in Moodle to improve e learning. In: Proceedings of the 2007 Euro American Conference on Telematics and Information Systems, EATIS 2007, pp. 72:1–72:4. ACM, New York (2007).  https://doi.org/10.1145/1352694.1352767
  4. 4.
    Charitopoulos, A., Rangoussi, M., Koulouriotis, D.: Educational data mining and data analysis for optimal learning content management: applied in Moodle for undergraduate engineering studies. In: 2017 IEEE Global Engineering Education Conference (EDUCON), pp. 990–998, April 2017.  https://doi.org/10.1109/EDUCON.2017.7942969
  5. 5.
    Conde, M., Garca-Pealvo, F., Fidalgo-Blanco,, Sein-Echaluce, M.: Study of the flexibility of a learning analytics tool to evaluate teamwork competence acquisition in different contexts. In: CEUR workshop Proceedings, vol. 1925, pp. 63–77 (2017). ceur-ws.org/Vol-1925/paper07.pdf. cited By 0
  6. 6.
    Dhingra, S., Chaudhry, K.: A study of the impact of data warehousing and data mining implementation on marketing effort. Int. J. Adv. Stud. Comput. Sci. Eng. 7(1), 13–20 (2018)Google Scholar
  7. 7.
    Elaal, S.: E-learning using data mining. Chin. Egypt. Res. J. Helwan Univ. (2013) Google Scholar
  8. 8.
    Gamie, E.A., El-Seoud, M.S.A., Salama, M.A., Hussein, W.: Pedagogical and elearning logs analyses to enhance students’ performance. In: Proceedings of the 7th International Conference on Software and Information Engineering, ICSIE 2018, pp. 116–120. ACM, New York (2018).  https://doi.org/10.1145/3220267.3220289. Cited by 0
  9. 9.
    Grover, V., Chiang, R.H., Liang, T.P., Zhang, D.: Creating strategic business value from big data analytics: a research framework. J. Manag. Inf. Syst. 35(2), 388–423 (2018)CrossRefGoogle Scholar
  10. 10.
    Hernández-García, Á., Acquila-Natale, E., Iglesías-Pradas, S., Chaparro-Peláez, J.: Design of an extraction, transform and load process for calculation of teamwork indicators in Moodle. In: LASI-SPAIN (2018). ceur-ws.org/Vol-2188/Paper7.pdf
  11. 11.
    Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990).  https://doi.org/10.1109/5.58325CrossRefGoogle Scholar
  12. 12.
    Kolekar, S.V., Pai, R.M., Manohara Pai, M.M.: Adaptive user interface for Moodle based E-learning system using learning styles. Procedia Comput. Sci. 135, 606–615 (2018).  https://doi.org/10.1016/j.procs.2018.08.226. The 3rd International Conference on Computer Science and Computational Intelligence (ICCSCI 2018): Empowering Smart Technology in Digital Era for a Better LifeCrossRefGoogle Scholar
  13. 13.
    Konstantinidis, A., Grafton, C.: Using Excel Macros to Analyse Moodle Logs. UK Research.Moodle.Net, pp. 4–6 (2013). http://research.moodle.net/pluginfile.php/333/mod_data/content/1233/Using Excel Macros to Analyse Moodle Logs.pdf
  14. 14.
    Moreira Félix, I., Ambrósio, A.P., Silva Neves, P., Siqueira, J., Duilio Brancher, J.: Moodle predicta: a data mining tool for student follow up. In: Proceedings of the 9th International Conference on Computer Supported Education 1 (CSEDU), pp. 339–346 (2017).  https://doi.org/10.5220/0006318403390346
  15. 15.
    Poon, L.K.M., Kong, S.-C., Wong, M.Y.W., Yau, T.S.H.: Mining sequential patterns of students’ access on learning management system. In: Tan, Y., Takagi, H., Shi, Y. (eds.) DMBD 2017. LNCS, vol. 10387, pp. 191–198. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61845-6_20CrossRefGoogle Scholar
  16. 16.
    Poon, L.K.M., Kong, S.-C., Yau, T.S.H., Wong, M., Ling, M.H.: Learning analytics for monitoring students participation online: visualizing navigational patterns on learning management system. In: Cheung, S.K.S., Kwok, L., Ma, W.W.K., Lee, L.-K., Yang, H. (eds.) ICBL 2017. LNCS, vol. 10309, pp. 166–176. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59360-9_15CrossRefGoogle Scholar
  17. 17.
    Qiao, C., Hu, X.: Discovering student behavior patterns from event logs: Preliminary results on a novel probabilistic latent variable model. In: 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), pp. 207–211, July 2018.  https://doi.org/10.1109/ICALT.2018.00056
  18. 18.
    Raga, R.C., Raga, J.D.: A comparison of college faculty and student class activity in an online learning environment using course log data. In: 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 1–6, August 2017.  https://doi.org/10.1109/UIC-ATC.2017.8397475
  19. 19.
    Ros, S., Lázaro, J.C., Robles-Gómez, A., Caminero, A.C., Tobarra, L., Pastor, R.: Analyzing content structure and Moodle milestone to classify student learning behavior in a basic desktop tools course. In: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2017, pp. 42:1–42:6. ACM, New York (2017).  https://doi.org/10.1145/3144826.3145392
  20. 20.
    Porras, J.T., Alcántara-Manzanares, J., García, S.R.: Virtual platforms use: a useful monitoring tool. EDMETIC 7(1), 242–255 (2018).  https://doi.org/10.21071/edmetic.v6i2.8696CrossRefGoogle Scholar
  21. 21.
    Sheard, J., Ceddia, J., Hurst, J., Tuovinen, J.: Inferring student learning behaviour from website interactions: a usage analysis. Educ. Inf. Technol. 8(3), 245–266 (2003).  https://doi.org/10.1023/A:1026360026073CrossRefGoogle Scholar
  22. 22.
    Shim, J.P., French, A.M., Guo, C., Jablonski, J.: Big data and analytics: issues, solutions, and ROI. CAIS 37, 39 (2015)CrossRefGoogle Scholar
  23. 23.
    Smith, S.M., et al.: How might the development of data mining and log analysis systems for the Moodle virtual learning environment improve computer science students’ course engagement and encourage course designers’ future engagement with data analysis methods for the evaluation of course resources? Ph.D. thesis, University of Lincoln (2017). http://eprints.lincoln.ac.uk/30882/
  24. 24.
    Vega, A.B.: Mejora en el descubrimiento de modelos de minería de procesos en educación mediante agrupación de datos de interacción con la plataforma Moodle. Ph.D. thesis, Universidad de Córdoba (2018)Google Scholar
  25. 25.
    Verma, A., Rathore, S., Vishwakarma, S., Goswani, S.: Multilevel analysis of studentś feedback using Moodle logs in virtual cloud environment. Int. J. Comput. Sci. Inf. Technol. 9, 15–28 (2017).  https://doi.org/10.5281/zenodo.2558650CrossRefGoogle Scholar
  26. 26.
    Wu, X., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008).  https://doi.org/10.1007/s10115-007-0114-2CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad Nacional Abierta y a DistanciaBogotaColombia
  2. 2.Universidad de Bogota Jorge Tadeo LozanoBogotaColombia
  3. 3.OCOX AIBogotaColombia

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