Abstract
Educational Data Mining has gained a variety of attention. It describes students’ cognitive needs through data mining, and provides individualized knowledge support for cognitive differences. Although the application of data mining algorithms is relatively mature, the data pre-processing based on data collection still suffers from high costs. The paper focus on a research question: how to effectively collect behavior data in virtual learning environments? “Effectively” in the sense of ensuring that value-intensive behavior data on decision-making can be accurately collected which reflects the students’ cognitive. Therefore, the paper presents a method to achieve the object. The method comprises six steps, including extraction, transformation, determination, design, trigger and store. Based on the fact that all behavior data generated by the interaction is objective, identifying the collection points on the trigger event matches the granularity level of behavior data. Considering the related platforms and intelligent applications, the method can be used, providing behavior data support for the research of knowledge services.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aldowah, H., Al-Samarraie, H., Fauzy, W.M.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics Inform. 37, 13–49 (2019). https://doi.org/10.1016/j.tele.2019.01.007
Mahanama, B., Mendis, W., Jayasooriya, A., Malaka, V., Thayasivam, U., Thayasivam, U.: Educational data mining: a review on data collection process. Presented at the 1 September (2018). https://doi.org/10.1109/ICTER.2018.8615532
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40, 601–618 (2010). https://doi.org/10.1109/TSMCC.2010.2053532
Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Exp. Syst. Appl. 33, 135–146 (2007). https://doi.org/10.1016/j.eswa.2006.04.005
Feldman-Maggor, Y., Barhoom, S., Blonder, R., Tuvi-Arad, I.: Behind the scenes of educational data mining. Educ. Inf. Technol. 26(2), 1455–1470 (2020). https://doi.org/10.1007/s10639-020-10309-x
Romero, C., Romero, J.R., Ventura, S.: A survey on pre-processing educational data. In: Peña-Ayala, A. (ed.) Educational Data Mining. SCI, vol. 524, pp. 29–64. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02738-8_2
Kolekar, S.V., Pai, R.M., Manohara Pai, M.M.: XML based pre-processing and analysis of log data in adaptive e-learning system: an algorithmic approach. In: Vincenti, G., Bucciero, A., Vaz de Carvalho, C. (eds.) eLEOT 2015. LNICSSITE, vol. 160, pp. 135–143. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28883-3_17
Munk, M., Drlík, M., Benko, L., Reichel, J.: Quantitative and qualitative evaluation of sequence patterns found by application of different educational data preprocessing techniques. IEEE Access 5, 8989–9004 (2017). https://doi.org/10.1109/ACCESS.2017.2706302
Romero, C., Ventura, S.: Educational data mining and learning analytics: an updated survey. WIREs Data Min. Knowl. Discov. 10, e1355 (2020). https://doi.org/10.1002/widm.1355
Ihantola, P., et al.: Educational data mining and learning analytics in programming: literature review and case studies. Presented at the 4 July (2015). https://doi.org/10.1145/2858796.2858798
Angeli, C., Howard, S.K., Ma, J., Yang, J., Kirschner, P.A.: Data mining in educational technology classroom research: can it make a contribution? Comput. Educ. 113, 226–242 (2017). https://doi.org/10.1016/j.compedu.2017.05.021
Munoz-Organero, M., Munoz-Merino, P.J., Kloos, C.D.: Student behavior and interaction patterns with an LMS as motivation predictors in e-learning settings. IEEE Trans. Educ. 53, 463–470 (2010). https://doi.org/10.1109/TE.2009.2027433
Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M.P., Núñez, J.C.: Students’ LMS interaction patterns and their relationship with achievement: a case study in higher education. Comput. Educ. 96, 42–54 (2016). https://doi.org/10.1016/j.compedu.2016.02.006
McCuaig, J., Baldwin, J.: Identifying successful learners from interaction behaviour. International Educational Data Mining Society (2012)
Domagk, S., Schwartz, R.N., Plass, J.L.: Interactivity in multimedia learning: an integrated model. Comput. Hum. Behav. 26, 1024–1033 (2010). https://doi.org/10.1016/j.chb.2010.03.003
Dromey, R.G.: From requirements to design: formalizing the key steps. In: Cerone, A., Lindsay, P. (eds.) Proceedings of the 1st International Conference on Software Engineering and Formal Methods, pp. 2–11 (2003). https://doi.org/10.1109/SEFM.2003.1236202
Subagyo, W.P., Nugroho, S.M.S., Sumpeno, S.: Simulation multi behavior NPCs in fire evacuation using emotional behavior tree. In: 2016 International Seminar on Application for Technology of Information and Communication (ISemantic), pp. 184–190 (2016). https://doi.org/10.1109/ISEMANTIC.2016.7873835
Papamitsiou, Z., Economides, A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educ. Technol. Soc. 17, 49–64 (2014)
Biard, T., Le Mauff, A., Bigand, M., Bourey, J.-P.: Separation of decision modeling from business process modeling using new “decision model and notation” (DMN) for automating operational decision-making. In: Camarinha-Matos, L.M., Bénaben, F., Picard, W. (eds.) PRO-VE 2015. IAICT, vol. 463, pp. 489–496. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24141-8_45
Acknowledgments
The presented research works have been supported by “the National Natural Science Foundation of China” (61972029).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Wu, T., Gou, J., Mu, W., Wang, Z. (2021). Behavior Data Collection in Collaborative Virtual Learning Environments. In: Camarinha-Matos, L.M., Boucher, X., Afsarmanesh, H. (eds) Smart and Sustainable Collaborative Networks 4.0. PRO-VE 2021. IFIP Advances in Information and Communication Technology, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-030-85969-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-85969-5_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85968-8
Online ISBN: 978-3-030-85969-5
eBook Packages: Computer ScienceComputer Science (R0)
