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Personal Generative Libraries for Smart Computer Science Education

  • Vytautas ŠtuikysEmail author
  • Renata Burbaitė
  • Ramūnas Kubiliūnas
  • Kęstutis Valinčius
Conference paper
  • 31 Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 188)

Abstract

The difficulties of retrieving the educational content from conventional digital libraries for personalised learning (PL) are well known. In this paper, to overcome those issues and enforce learning performance, we propose the concept personal generative library (PGL). We will discuss an experimental system that integrates conventional repositories, the teacher’s PGL, the students’ PGLs, their individual repositories along with the personalised learning processes using the developed framework. The teacher’s individual repository stores the personalised content for all students along with assessment tasks for each type of the content. The teacher’s and students’ PGLs have the identical structure. The student’s content is a direct product of PL obtained during the classroom activities by modifying the teacher’s content due to the needs of personalisation or is a by-product created or searched during outside learner’s activities. We have approved this approach in one high school. We will present experimental results of the PGLs usage and the quality evaluation. Our approach enables enforcing the PL significantly in terms of higher flexibility, efficient search and more efficient procedures to form the personalised learning paths for smart CS education.

Keywords

Educational digital libraries Personal generative libraries Personalised STEM-driven CS education 

References

  1. 1.
    Zhu, Z.T., Yu, M.H., Riezebos, P.: A research framework of smart education. Smart Learn. Environ. 3(1), 1–17 (2014)Google Scholar
  2. 2.
    Paneva-Marinova, D., Pavlov, R.: Improving learner experience within educational nooks in digital libraries. In: Learner Experience and Usability in Online Education, pp. 174–193 (2018)Google Scholar
  3. 3.
    Halibas, A.S., Sathyaseelan, B., Shahzad, M.: Learning analytics: developing a data-centric teaching-research skill. In: Smart Technologies and Innovation for a Sustainable Future, pp. 213–219 (2019)Google Scholar
  4. 4.
    Fournier, H., Kop, R., Molyneaux, H.: New personal learning ecosystems: a decade of research in review. In: Emerging Technologies in Virtual Learning Environments, pp. 1–19 (2019)Google Scholar
  5. 5.
    Hendrix, M., Protopsaltis, A., Dunwell, I., de Freitas, S., Arnab, S., Petridis, P., Rolland, C., Lanas, J.L.: Defining a metadata schema for serious games as learning objects. In: 4th International Conference on Mobile, Hybrid, and On-Line Learning, pp. 14–19 (2012)Google Scholar
  6. 6.
    Cechinel, C., Ochoa, X.: A brief overview of quality inside learning object repositories. In: Proceedings of the XV International Conference on Human Computer Interaction, p. 83 (2014)Google Scholar
  7. 7.
    Self, J.A., Evans, M., Jun, T., Southee, D.: Interdisciplinary: challenges and opportunities for design education. Int. J. Technol. Des. Educ., 1–34 (2018)Google Scholar
  8. 8.
    McDonald, K.S., Waite, A.M.: Future directions: Challenges and solutions facing career readiness and development in STEM fields. Adv. Dev. Hum. Resour. 21(1), 133–138 (2019)CrossRefGoogle Scholar
  9. 9.
    Groff, J.S.: Personalised Learning: The State of the Field & Future Directions, Center for Curriculum Redesign (2017)Google Scholar
  10. 10.
    Dockterman, D.: Insights from 200+ years of personalised learning. NPJ Sci. Learn. 3(1), 15 (2018)CrossRefGoogle Scholar
  11. 11.
    Alur, R., Baraniuk, R., Bodik, R., Drobnis, A., Gulwani, S., Hartmann, B., Kafai, Y., Karpicke, J., Libeskind-Hadas, R., Richardson, D., Solar-Lezama, A., Thille, C., Vardi, M.: Computer-aided personalized education (2016). https://www.cis.upenn.edu/~alur/cape16.pdf
  12. 12.
    Štuikys, V., Burbaitė, R., Drąsutė, V., Ziberkas, G., Drąsutis, S.: A framework for introducing personalisation into STEM-driven computer science education. Int. J. Eng. Educ. 35(4), 1–18 (2019)Google Scholar
  13. 13.
    Basham, J.D., Hall, T.E., Carter Jr., R.A., Stahl, W.M.: An operationalized understanding of personalized learning. J. Special Educ. Technol. 31(3), 126–136 (2016)CrossRefGoogle Scholar
  14. 14.
    Brusilovsky, P., Cassel, L.N., Delcambre, L.M., Fox, E.A., Furuta, R., Garcia, D.D., Shipman, F.M., Yudelson, M.: Social navigation for educational digital libraries. Procedia Comput. Sci. 1(2), 2889–2897 (2010)CrossRefGoogle Scholar
  15. 15.
    Domazet D., Veljković D., Nikolić B., Jovev, L.: Clustering of learning objects for different knowledge levels as an approach to adaptive e-learning based on SCORM AND DITA. In: The Third International Conference on E-learning, pp. 27–28 (2012)Google Scholar
  16. 16.
    Sabitha, A.S., Mehrotra, D.: User centric retrieval of learning objects in LMS. In: Third International Conference on Computer and Communication Technology, pp. 14–19 (2012)Google Scholar
  17. 17.
    Chen, Y.: A High-quality digital library supporting computing education: the ensemble approach. Doctoral dissertation, Virginia Tech (2017)Google Scholar
  18. 18.
    Ochoa, X.: Learnometrics: Metrics for learning objects. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 1–8 (2011)Google Scholar
  19. 19.
    Fox, E.A.: Introduction to digital libraries. In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, pp. 283–284 (2016)Google Scholar
  20. 20.
    Deng, X., Ruan, J.: The Personal Digital Library (PDL)-based e-learning: using the PDL as an e-learning support tool. Integr. Innov. Orient E-Soc. 2, 549–555 (2007)Google Scholar
  21. 21.
    Brisebois, R., Abran, A., Nadembega, A.: A semantic metadata enrichment software ecosystem (SMESE) based on a multi-platform metadata model for digital libraries. J. Softw. Eng. Appl. 10(04), 370–405 (2017)CrossRefGoogle Scholar
  22. 22.
    Yoshinov, R., Arapi, P., Christodoulakis, Kotseva, M.: Supporting personalized learning experiences on top of multimedia digital libraries. Int. J. Educ. Inf. Technol. 10, 152–158, (2016)Google Scholar
  23. 23.
    Chen, Y., Fox, E.A.: Extending ensemble: an education digital library for computer science education. J. Comput. Sci. Coll. 31(2), 201–207 (2015)Google Scholar
  24. 24.
    Park, J.R., Brenza, A.: Evaluation of semi-automatic metadata generation tools: a survey of the current state of the art. Inf. Technol. Lib. 34(3), 22–42 (2015)Google Scholar
  25. 25.
    Miller, L.D., Soh, L.K., Samal, A., Nugent, G.: iLOG: a framework for automatic annotation of learning objects with empirical usage metadata. Int. J. Artif. Intell. Educ. 21(3), 215–236 (2012)Google Scholar
  26. 26.
    Štuikys, V., Burbaitė, R.: Smart STEM-Driven Computer Science Education: Theory, Methodology and Robot-based Practices. Springer (2018)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Vytautas Štuikys
    • 1
    Email author
  • Renata Burbaitė
    • 1
  • Ramūnas Kubiliūnas
    • 1
  • Kęstutis Valinčius
    • 1
  1. 1.Informatics Faculty, Department of Software EngineeringKaunas University of TechnologyKaunasLithuania

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