Main Article Content


Learners have different needs and abilities; teachers have the ambition to intervene before it is too late. How may e-learning systems support this? Learning Analytics may be the answer but there is not a general-purpose model to adopt. Many learning analytics tools examine data related to the activities of learners in on-line systems. Research efforts in learning analytics tried to examine data coming from LMS tracks in order to define predictive model of students’ performances and failure risks and to intervene to improve the learning outcomes. The analytical methods are widely used but no theoretical references are clear. In this paper, we tried to define a prediction model for learning analytics. In particular, we adopted a Moodle-based LMS in a blended course and collected all data of more than 400 undergraduate students in terms of resource accesses and exam performances. The model we defined was able to identify the learners at risk during their learning processes only by analysing their navigation paths among the contents.


Learning Analytics Navigation Path LMS Moodle Learning Analytics Model

Article Details

How to Cite
Miranda, S., & Vegliante, R. (2019). Learning Analytics to support learners and teachers: the navigation among contents as a model to adopt. Journal of E-Learning and Knowledge Society, 15(3), 101-116.


  1. Agudo-Peregrina, A. F., Iglesias-Pradas, S., Conde-Gonzalez, M.A., Hernandez-Garcıa, A. (2014) “Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning,” Comput. Hum. Behav., vol. 31, pp. 542–550, Feb. 2014.
  2. Beer, C., Clark, K. and Jones, D. (2010) “Indicators of engagement,” in Australian Soc. Comput. Learn. Tertiary Educ. Annu. Conf., 2010, pp. 75–86.
  3. Breiter, A., & Light, D. (2006). Data for School Improvement: Factors for Designing Effective Information Systems to Support Decision-Making in Schools. Educational Technology & Society, 9(3), 206-217.
  4. Brown, J. S. , Collins, A. and Duguid, P. (1989), “Situated cognition and the culture of learning,” Educ. Res., vol. 18, no. 1, pp. 32–42, 1989.
  5. Conijn, R., Snijders, C., Kleingeld, A. and Matzat, U. (2017) “Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS” IEEE Transactions on learning technologies, vol. 10, no. 1, January-March 2017.
  6. Gasevic, D., Dawson, S., Rogers, T. (2016) “Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success,” Internet High. Educ., vol. 28, pp. 68–84, Jan. 2016.
  7. Hoic-Bozic, N., Mornar, V., Boticki, I. (2009) “A blended learning approach to course design and implementation,” IEEE Trans. Educ., vol. 52, no. 1, pp. 19–30, Feb. 2009.
  8. José A. Ruipérez-Valiente, Pedro J. Muñoz-Merino, Derick Leony, Carlos Delgado Kloos (2015), ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform, Computers in Human Behavior v.47, 139-148 (
  9. Levenshtein VI (1966), Binary codes capable of correcting deletions, insertions, and reversals, in Soviet Physics Doklady, vol. 10, 1966, pp. 707–10.
  10. Macfadyen, L. and Dawson, S. (2010) “Mining LMS data to develop an ‘early warning system’ for educators: A proof of concept” Comput. Educ., vol. 54, no. 2, pp. 588–599, Feb. 2010.
  11. Matcha, W., Ahmad Uzir, N., Gasevic, D. and Pardo, A. (2019), "A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective," in IEEE Transactions on Learning Technologies. doi: 10.1109/TLT.2019.2916802
  12. Miranda, S., Vegliante, R., De Angelis, M. (2019), “I processi di valutazione nell’e-learning”. In: Training actions and evaluation processes. Atti del Convegno Internazionale SIRD Pensa Multimedia Pag.687-699 ISBN:978-88-6760-634-4, Training actions and evaluation processes Salerno 25-26 Oct. 2018
  13. Moore, M. G. (1989) “Editorial: Three types of interaction,” Amer. J. Distance Educ., vol. 3, no. 3, pp. 1–6, Jan. 1989
  14. Morris, L. V., Finnegan, C. and Wu, S.-S. (2005) “Tracking student behavior, persistence, and achievement in online courses,” Internet High. Educ., vol. 8, no. 3, pp. 221–231, 2005.
  15. Mothukuri U. K. et al. (2017), "Improvisation of learning experience using learning analytics in eLearning," 2017 5th National Conference on E-Learning & E-Learning Technologies (ELELTECH), Hyderabad, 2017, pp. 1-6. doi: 10.1109/ELELTECH.2017.8074995
  16. Munoz-Merino, P. J., Ruiperez-Valiente, J. A., Alario-Hoyos, C. , Perez-Sanagustn, M. and Delgado Kloos, C. (2015) “Precise effectiveness strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs,” Comput. Human Behav., vol. 47, pp. 108–118, 2015.
  17. Nandi, D., Hamilton, M., Harland, J. and Warburton, G. (2011) “How active are students in online discussion forums?” in Proc. 13th Australasian Comput. Educ. Conf., 2011, vol. 114, pp. 125–134.
  18. Pi~na, A. A. (2012) “An overview of learning management systems,” in Virtual Learning Environments: Concepts, Methodologies, Tools and Applications, 1st ed. Louisville, KY, USA: Sullivan Univ. Syst., 2012, pp. 33–51.
  19. Pistilli, M. D., Willis III, J. E., & Campbell, J. P. (2014). Analytics through an Institutional Lens: Definition, Theory, Design, and Impact. In Learning Analytics (pp. 79-102). Springer New York.
  20. Rafaeli, S. and Ravid, G. (1997) “Online, web-based learning environment for an information systems course: Access logs, linearity and performance,” in Proc. Inf. Syst. Educ. Conf., 1997, vol. 97, pp. 92–99.
  21. Sergis, S., Sampson, D. (2017) “Teaching and Learning Analytics to Support Teacher Inquiry: A systematic literature review” In Learning Analytics: Fundaments, Applications and Trends, P. Ayala Ed., Springer International Publishing, 2017, 25-63.
  22. Shaffer, D. W. et al. (2009) “Epistemic network analysis: A prototype for 21st-century assessment of learning,” Int. J. Learn. Media, vol. 1, no. 2, pp. 33–53, 2009.
  23. Shankar, S. K., Prieto, L. P., Rodríguez-Triana, M. J. and Ruiz-Calleja, A. (2018) "A Review of Multimodal Learning Analytics Architectures," 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT), Mumbai, 2018, pp. 212-214. doi: 10.1109/ICALT.2018.00057
  24. Shum, S. B. and Ferguson, R. (2012), “Social learning analytics,” Educ. Technol. Soc., vol. 15, no. 3, pp. 3–26, 2012.
  25. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10
  26. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.
  27. Siemens, G., Baker, R. S. (2012) “Learning analytics and educational data mining: Towards communication and collaboration,” in Proc. 2nd Int. Conf. Learn. Analytics Knowl., 2012, pp. 252–254.
  28. T. Yu T. and Jo, I.-H. (2014) “Educational technology approach toward learning analytics: Relationship between student online behavior and learning performance in higher education,” in Proc. 4th Int. Conf. Learn. Anal. Knowl., 2014, pp. 269–270.
  29. Tempelaar, D. T., Rienties, B., Giesbers, B. (2015) “In search for the most informative data for feedback generation: Learning analytics in a data-rich context,” Comput. Human Behavior, vol. 47, pp. 157–167, Jun. 2015.
  30. Virvou, M., Alepis, Sotirios, Christos Sidiropoulos (2015) “A learning analytics tool for supporting teacher decision”, Information, Intelligence, Systems and Applications (IISA), 2015.
  31. Zacharis, N. Z. (2015) “A multivariate approach to predicting student outcomes in web-enabled blended learning courses,” Internet High. Educ., vol. 27, pp. 44–53, Oct. 2015.