Using a Multi Module Model for Learning Analytics to Predict Learners’ Cognitive States and Provide Tailored Learning Pathways and Assessment

  • Christos TroussasEmail author
  • Akrivi Krouska
  • Maria Virvou
Part of the Intelligent Systems Reference Library book series (ISRL, volume 158)


Learning analytics brings considerable challenges in the field of e-learning. Researchers increasingly use the technological advancements emerging from learning analytics in order to support the digital education. The way learning analytics is used, can vary. It can be used to provide learners with information to reflect on their achievements and patterns of behavior in relation to others, or to identify students requiring extra support and attention, or to help teachers plan supporting interventions for functional groups such as course teams. In view of the above, this paper employs learning analytics and presents the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Furthermore, it presents a multi module model consisting of the identification of target material, curriculum improvement, cognitive states and behavior prediction and personalization in order to support learners and further enhance their learning experience. The evaluation results are very promising and show that learning analytics can bring new insights that can benefit learners, educators and administrators.


E-learning Learning analytics Cognitive states prediction Personalization Target material Evaluation 


  1. 1.
    Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thüs, H.: A reference model for learning analytics. Int. J. Technol. Enhanced Learn. 4(5/6), 318–331 (2012)CrossRefGoogle Scholar
  2. 2.
    Hung, J.L., Hsu, Y.C., Rice, K.: Integrating data mining in program evaluation of k-12 online education. Educ. Technol. Soc. 15(3), 27–41 (2012)Google Scholar
  3. 3.
    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(6), 601–618 (2010)CrossRefGoogle Scholar
  4. 4.
    Clow, D.: An overview of learning analytics. Teach. High. Educ. 18(6), 683–695 (2013)CrossRefGoogle Scholar
  5. 5.
    Scheffel, M., Drachsler, H., Stoyanov, S., Specht, M.: Quality indicators for learning analytics. Educ. Technol. Soc. 17(4), 117–132 (2014)Google Scholar
  6. 6.
    Troussas, C., Krouska, A., Virvou, M.: Automatic predictions using LDA for learning through social networking services. In: 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI), Boston, MA, pp. 747–751 (2017)Google Scholar
  7. 7.
    Karthikeyan, K., Kavipriya, P.: On Improving student performance prediction in education systems using enhanced data mining techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 7(5), 935–941 (2017)CrossRefGoogle Scholar
  8. 8.
    Acharya, A., Sinha, D.: Early prediction of students performance using machine learning techniques. Int. J. Comput. Appl. 107(1), 37–43 (2014)Google Scholar
  9. 9.
    Troussas, C., Virvou, M., Espinosa, K.J.: Using visualization algorithms for discovering patterns in groups of users for tutoring multiple languages through social networking. J. Netw. 10(12), 668–674 (2015)Google Scholar
  10. 10.
    Kavitha, G., Raj, L.: Educational data mining and learning analytics—educational assistance for teaching and learning. Int. J. Comput. Organ. Trends 41(1), 21–25 (2017)CrossRefGoogle Scholar
  11. 11.
    Troussas, C., Espinosa, K.J., Virvou, M.: Intelligent advice generator for personalized language learning through social networking sites. In: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), Corfu, 2015, pp. 1–5 (2015)Google Scholar
  12. 12.
    Shorfuzzamana, M., Shamim Hossainbc, M., Nazir, A., Muhammadd, G., Alamri, A.: Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Comput. Hum. Behav. (2018) (in press)Google Scholar
  13. 13.
    Gašević, D., Kovanović, V., Joksimović, S.: Piecing the learning analytics puzzle: a consolidated model of a field of research and practice. Learn. Res. Pract. 3(1), 63–78 (2017)Google Scholar
  14. 14.
    Dyckhoff, A.L., Zielke, D., Bültmann, M., Chatti, M.A., Schroeder, U.: Design and implementation of a learning analytics toolkit for teachers. J. Educ. Technol. Soc. 15(3), 58–76 (2012)Google Scholar
  15. 15.
    Papamitsiou, Z., Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educ. Technol. Soc. 17(4), 49–64 (2014)Google Scholar
  16. 16.
    Nunn, S., Avella, J.T., Kanai, T., Kebritchi, M.: Learning analytics methods, benefits, and challenges in higher education: a systematic literature review. Online Learn. 20(2), 13–29 (2016)CrossRefGoogle Scholar
  17. 17.
    Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S.H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., Rubio, M.Á., Sheard, J., Skupas, B., Spacco, J., Szabo, C., Toll, D.: Educational data mining and learning analytics in programming: literature review and case studies. In: The 20th Annual Conference on Innovation and Technology in Computer Science Education—Working Group Reports, ACM, New York, NY, pp. 41–63 (2015)Google Scholar
  18. 18.
    Troussas, C., Virvou, M., Alepis, E.: Comulang: towards a collaborative e-learning system that supports student group modeling. SpringerPlus 2(1), 1–9 (2013)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Christos Troussas
    • 1
    Email author
  • Akrivi Krouska
    • 1
  • Maria Virvou
    • 1
  1. 1.Software Engineering Laboratory, Department of InformaticsUniversity of PiraeusPiraeusGreece

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