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E-Learning environment implies self-motivation and perseverance in study and completion of learning tasks. However, the more autonomy students have in managing their e-Learning, the harder they cope with distractions and remaining focused and engaged. This research study aims to assess the level of student engagement in four e-Learning platforms (CoLaB Tutor, AC-ware Tutor, CM Tutor and Moodle) in higher education. A model for Tracking Student Learning and Knowledge (TSLAK) is developed and based on two sets of variables: variables tracking student’s learning activities (VTL) and variables tracking student’s knowledge (VTK). This study aims to provide answers on how a model for tracking student online learning and knowledge can be formalized for the four e-Learning platforms and how can student learning and knowledge acquisition processes be described and measured by VTL and VTK. The results obtained by VTL and VTK indicate a significant decline in students’ engagement. Out of 218 the most engaged students, 77 (35%) of them used the CoLaB Tutor, 41 (19%) used the AC-ware Tutor, 52 (24%) used the CM Tutor, and 48 (22%) used the Moodle. The research showed that out of the total number of students only 88 (13%) of them were the most engaged and the most successful or more precisely, 63 (71%) graduates and 25 (29%) undergraduates. Such student engagement and success measured by VTL and VTK indicate the necessity of increasing students’ motivation in blended learning environments, strengthening their preparation and introduction to e-Learning platforms, and observing their feedback during a research study.


Distributed Learning Environments Evaluation of CAL Systems Intelligent Tutoring Systems

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How to Cite
Grubišić, A., Žitko, B., Stankov, S., Šarić-Grgić, I., Gašpar, A., Tomaš, S., Brajković, E., Volarić, T., Vasić, D., & Dodaj, A. (2020). A common model for tracking student learning and knowledge acquisition in different e-Learning platforms. Journal of E-Learning and Knowledge Society, 16(3), 10-23.


  1. Abdous, M., He, W., & Yen, C.-J. (2012). Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade. Educational Technology & Society, 15(3), 77–88.
  2. Bechhofer, S., van Harmelen, S., Hendler, J., Horrocks, I., McGuiness, D. L., Patel-Schneider, P. F., & et al. (2004). OWL Web Ontology Language Reference.
  3. Bloom, B. S. (1956). Taxonomy of educational objectives. The classification of educational goals, Handbook I Cognitive Domain. Committee of College and University Examiners, Longmans.
  4. Bloom, B. S. (1984). The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher, 13(6), 4–16.
  5. Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.
  6. Dietz-Uhler, B., & Hurn, J. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26.
  7. Falakmasir, M., & Habibi, J. (2010). Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning. 241–248.
  8. Fletcher, J. D. (2003). Evidence for Learning From Technology-Assisted Instruction. In H. F. O’Neil, Jr., & R. Perez (Eds.) Technology Applications in Education: A Learning View (pp. 79–100). Lawrence Erlbaum.
  9. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74(1), 59–109.
  10. Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology, 9(6), 495–522.
  11. Grubišić, A. (2012). Adaptive student’s knowledge acquisition model in e-learning systems, PhD Thesis, University of Zagreb, Croatia.
  12. Grubišić, A., Stankov, S., & Žitko, B. (2013). Stereotype Student Model for an Adaptive e-Learning System. International Journal of Computer and Information Engineering, 7(4), 440–447.
  13. Grubišić, A., Stankov, S., & Žitko, B. (2014, January 1). Adaptive courseware model for intelligent e- learning systems. 2nd International Conference on Computing, E- Learning and Emerging Technologies (ICCEET 2014).
  14. Grubišić, A., Stankov, S., Žitko, B., Tomaš, S., Brajković, E., Volarić, T., Vasić, D., & Šarić, I. (2016). Empirical Evaluation of Intelligent Tutoring Systems with Ontological Domain Knowledge Representation: A Case Study with Online Courses in Higher Education. Proceedings of the 13th International Conference Intelligent Tutoring Systems, ITS 2016, 469–470.
  15. Harper, S. R., & Quaye, S. J. (Eds.). (2008). Student Engagement in Higher Education: Theoretical Perspectives and Practical Approaches for Diverse Populations (1 edition). Routledge.
  16. Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133–145.
  17. Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making (Vol. 186). Springer Berlin Heidelberg.
  18. Kats, Y. (2010). Learning Management System Technologies and Software Solutions for Online Teaching: Tools and Applications. Information Science Reference - Imprint of: IGI Publishing.
  19. Kearsley, G. (Ed.). (1987). Artificial Intelligence and Instruction: Applications and Methods (F edition). Addison-Wesley.
  20. Knublauch, H., Fergerson, R. W., Noy, N. F., & Musen, M. A. (2004). The Protégé OWL Plugin: An Open Development Environment for Semantic Web Applications. The Semantic Web – ISWC 2004, 229–243.
  21. Kotsiantis, S., Tselios, N., Filippidi, A., & Wade, V. (2013). Using Learning Analytics to Identify Successful Learners in a Blended Learning Course. Int. J. Technol. Enhanc. Learn., 5(2), 133–150.℡.2013.059088
  22. Krause, K. L., & Coates, H. (2008). Students’ engagement in first‐year university. Assessment & Evaluation in Higher Education, 33(5), 493–505.
  23. Kuh, G. D. (2009). What Student Affairs Professionals Need to Know about Student Engagement. Journal of College Student Development, 50(6), 683–706.
  24. Lin, C. C., & Chiu, C. H. (2013). Correlation between Course Tracking Variables and Academic Performance in Blended Online Courses. 2013 IEEE 13th International Conference on Advanced Learning Technologies, 184–188.
  25. Liu, S. Y., Gomez, J., & Yen, C.-J. (2009). Community College Online Course Retention and Final Grade: Predictability of Social Presence. Journal of Interactive Online Learning, 8(2), 165–182.
  26. Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the American Society for Information Science and Technology, 60(2), 372–380.
  27. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599.
  28. Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003). Predicting student performance: An application of data mining methods with an educational web-based system. Frontiers in Education, 2003. FIE 2003 33rd Annual, 1, T2A–13.
  29. Moridis, C., & Economides, A. A. (2009). Prediction of student’s mood during an online test using formula-based and neural network-based method. Computers & Education, 53(3), 644–652.
  30. Morris, L. V., Finnegan, C., & Wu, S.-S. (2005). Tracking student behavior, persistence, and achievement in online courses. The Internet and Higher Education, 8(3), 221–231.
  31. Novak, J. D., & Cañas, A. J. (2006). The Origins of the Concept Mapping Tool and the Continuing Evolution of the Tool. Information Visualization, 5(3), 175–184.
  32. Novak, J. D., & Cañas, A. J. (2008). The Theory Underlying Concept Maps and How to Construct and Use Them.
  33. Pardos, Z.A., Baker, R. S., Pedro, M. S., Gowda, S. M., & Gowda, S. M. (2014). Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes. Journal of Learning Analytics, 1(1), 107–128.
  34. Pardos, Zachary A., & Heffernan, N. T. (2010). Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing. In P. De Bra, A. Kobsa, & D. Chin (Eds.), User Modeling, Adaptation, and Personalization (pp. 255–266). Springer Berlin Heidelberg.
  35. Psotka, J., Massey, L. D., & Mutter, S. A. (Eds.). (1988). Intelligent Tutoring Systems: Lessons Learned. Psychology Press.
  36. Romero, C., Ventura, S., Espejo, P. G., & Hervás, C. (2012). Data Mining Algorithms to Classify Students. EDM 2008.
  37. Romero-Zaldivar, V.-A., Pardo, A., Burgos, D., & Delgado Kloos, C. (2012). Monitoring student progress using virtual appliances: A case study. Computers & Education, 58(4), 1058–1067.
  38. Rosic, M., Glavinic, V., & Stankov, S. (2005). Intelligent Tutoring Systems for the New Learning Infrastructure. In Intelligent Learning Infrastructure for Knowledge Intensive Organizations: A Semantic Web Perspective Lytras, M. D. ; Naeve, A. (ed.) (pp. 225–250). Hershey: Idea Group Inc./InfoSci, Information Science Publishing.
  39. Shih, B., Koedinger, K. R., & Scheines, R. (2010). A Response-Time Model for Bottom-Out Hints as Worked Examples. In Handbook of Educational Data Mining (Vol. 1–0, pp. 201–211). CRC Press.
  40. Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses. Journal of Asynchronous Learning Networks, 16(3), 51–61.
  41. Staker, H., & Horn, M. B. (2012). Classifying K-12 Blended Learning.
  42. Tadić, M., & Fulgosi, S. (2003). Building the Croatian Morphological Lexicon. Proceedings of the 2003 EACL Workshop on Morphological Processing of Slavic Languages, 41–46.
  43. Trowler, V. (2010). Student engagement literature review.
  44. Volarić, T. (2017). Knowledge design and delivery model for intelligent learning management systems, PhD Thesis, University of Split, Croatia.
  45. Wang, A. Y., & Newlin, M. H. (2000). Characteristics of Students Who Enroll and Succeed in Psychology Web-based Classes. Journal of Educational Psychology, 92(1), 137–143.
  46. Žitko, B. (2010). Model of Intelligent Tutoring System Based on Processing of Controlled Language over Ontology, PhD Thesis, University of Zagreb, Croatia.