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The Model of Curriculum Constructor

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

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Abstract

The curriculum constructor model based on ontology that allows establishing connection between curriculum objectives, learning outcomes, expertise and disciplines is introduced in this paper. Thus, it makes possible to consider the compliance of the Education Programme with the learning outcomes and expertise, as well as, it helps check the curriculum goal achievements, the learning outcome development and mastering the competences. The constructor lets create different subsets with different properties and set various individual trajectories.

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References

  1. Yahya, A., Osman, A.: Using data mining techniques to guide academic programs design and assessment (2019)

    Google Scholar 

  2. Rathy, G.A., Sivasankar, P., Gnanasambandhan, T.G.: Developing a knowledge structure using outcome based education in Power Electronics Engineering. Procedia Comput. Sci. 172, 1026–1032 (2020). https://doi.org/10.1016/j.procs.2020.05.150

  3. Redecker, C.: European framework for the digital competence of educators: DigCompEdu. – Joint Research Centre (Seville site) (2017), JRC107466

    Google Scholar 

  4. Bekmanova, G., Omarbekova, A., Kaderkeyeva, Z., Sharipbay, A.: Model of intelligent massive open online course development. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12250, pp. 271–281. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58802-1_20

    Chapter  Google Scholar 

  5. Chassignol, M., Khoroshavin, A., Klimova, A., Bilyatdinova, A.: Artificial intelligence trends in education: a narrative overview. Procedia Comput. Sci. 136, 16–24 (2018). https://doi.org/10.1016/j.procs.2018.08.233

  6. Matveeva, T., Galiullina, N.: An empirical investigation of language model based reverse turing test as a tool for knowledge and skills assessment. https://doi.org/10.28995/2075-7182-2020-19-696-707

  7. Shen, R.M., Tang, Y.Y., Zhang, T.Z.: The intelligent assessment system in Web-based distance learning education 31st annual frontiers in education conference. In: Impact on Engineering and Science Education. Conference Proceedings (Cat. No. 01CH37193). IEEE (2001). T. 1. C. TIF-7

    Google Scholar 

  8. Sitthiworachart, J., Joy, M., Sutinen, E.: Success factors for e-assessment in computer science education E-Learn: world conference on E-Learning in corporate, government, healthcare, and higher education. In: Association for the Advancement of Computing in Education (AACE), pp. 2287–2293 (2008)

    Google Scholar 

  9. Zawacki-Richter, O., et al.: Systematic review of research on artificial intelligence applications in higher education–where are the educators?. Int. J. Educ. Technol. High. Educ. 16(1), 1–27 (2019)

    Google Scholar 

  10. Petrovskaya, A. et al.: Computerization of learning management process as a means of improving the quality of the educational process and student motivation. Procedia Comput. Sci. 169, 656–661 (2020)

    Google Scholar 

  11. Jain, G.P., et al.: Artificial intelligence-based student learning evaluation: a concept map-based approach for analyzing a student’s understanding of a topic. IEEE Trans. Learn. Technol. 7(3), 267–279 (2014)

    Google Scholar 

  12. Kose, U., Arslan, A.: Intelligent e-learning system for improving students’ academic achievements in computer programming courses. Int. J. Eng. Educ. 32(1), 185–198 (2016)

    Google Scholar 

  13. Chakraborty, U.K., Roy, S.: Fuzzy automata inspired intelligent assesment of learning achievement IICAI, 1505–1518 (2011)

    Google Scholar 

  14. Chakraborty, U.K., Roy, S.: Neural network based intelligent analysis of learners’ response for an e-Learning environment. In: 2010 2nd International Conference on Education Technology and Computer, vol. 2, pp. V2–333-V2–337. IEEE (2010)

    Google Scholar 

  15. Chakraborty, U., Konar, D., Roy, S., Choudhury, S.: Intelligent evaluation of short responses for e-learning systems. In: Satapathy, S.C., Prasad, V.K., Rani, B.P., Udgata, S.K., Raju, K.S. (eds.) Proceedings of the First International Conference on Computational Intelligence and Informatics. AISC, vol. 507, pp. 365–372. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2471-9_35

    Chapter  Google Scholar 

  16. Hettiarachchi, E., et al.: A standard and interoperable technology-enhanced assessment system for skill and knowledge acquirement CSEDU, vol. 2, pp. 157–160 (2012)

    Google Scholar 

  17. Hettiarachchi, E., et al.: A technology enhanced assessment system for skill and knowledge learning CSEDU, vol. 2, 184–191 (2014)

    Google Scholar 

  18. Ishak, I.: Application of fuzzy logic to student performance in calculation subjects. In: Proceedings of the 4th National Symposium & Exhibition on Business & Accounting (2015)

    Google Scholar 

  19. Gokmen, G., et al.: Evaluation of student performance in laboratory applications using fuzzy logic. Procedia-Soc. Behav. Sci. 2(2), 902–909 (2010)

    Google Scholar 

  20. Voskoglou, M.G.: Fuzzy logic as a tool for assessing students’ knowledge and skills. Educ. Sci. 3(2), 208–221 (2013)

    Google Scholar 

  21. Iskander, M. (ed.): Innovations in E-Learning, Instruction Technology, Assessment and Engineering Education. Springer Science & Business Media (2007)

    Google Scholar 

  22. Ali, M., Ghatol, A.: A neuro-fuzzy inference system for student modeling in web-based intelligent tutoring systems. In: Proceedings of International Conference on Cognitive Systems, pp. 14–19 (2004)

    Google Scholar 

  23. Stathacopoulou, R., et al.: Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis. Inf. Sci. 170(2–4), 273–307 (2005)

    Google Scholar 

  24. Zadeh, L.A.: Fuzzy logic. Computer 21(4), 83–93 (1988)

    Google Scholar 

  25. Weon, S., Kim, J.: Learning achievement evaluation strategy using fuzzy membership function 31st Annual Frontiers in Education Conference. In: Impact on Engineering and Science Education Conference Proceedings (Cat. No. 01CH37193), vol. 1, pp. T3A-19. IEEE (2001)

    Google Scholar 

  26. Samarakou, M., et al.: Application of fuzzy logic for the assessment of engineering students. In: 2017 IEEE Global Engineering Education Conference (EDUCON), pp. 646–650. IEEE (2017)

    Google Scholar 

  27. Karthika, R., Deborah, L.J., Vijayakumar, P.: Intelligent e-learning system based on fuzzy logic. Neural Comput. Appl., 1–10 (2019)

    Google Scholar 

  28. Milani, A., Suriani, S., Poggioni, V.: Modeling educational domains in a planning framework. In: ACM International Conference Proceeding Series, vol. 113, pp. 748-753 (2005). https://doi.org/10.1145/1089551.1089687

  29. Sasipraba, T., et al.: Assessment tools and rubrics for evaluating the capstone projects in outcome based education. Procedia Comput. Sci. 172, 296–301, ISSN 1877–0509 (2020). https://doi.org/10.1016/j.procs.2020.05.047

  30. Srimadhaven, T., Chris Junni, A.V., Naga, H., Jessenth Ebenezer, S., Shabari Girish, S., Priyaadharshini, M.: Learning analytics: virtual reality for programming course in higher education. Procedia Comput. Sci. 172, 433–437, ISSN 1877–0509 (2020). https://doi.org/10.1016/j.procs.2020.05.095

  31. Lueny, M.: An undergraduate engineering education leadership program. is it working? outcomes of the second phase. Procedia Comput. Sci. 172, 337–343, ISSN 1877–0509 (2020). https://doi.org/10.1016/j.procs.2020.05.169

  32. Taylor, P.H.: Introduction: curriculum studies in retrospect and prospect. New Directions in Curriculum Studies 33, 9–12 (2018). https://doi.org/10.4324/9780429453953-1

    Article  Google Scholar 

  33. Young, M.: Curriculum theory: what it is and why it is important. [Teoria do currículo: O que é e por que é importante] Cadernos De Pesquisa. 44(151), 191–201 (2014). https://doi.org/10.1590/198053142851

  34. Jadhav, M.R., Kakade, A.B., Jagtap, S.R., Patil, M.S.: Impact assessment of outcome based approach in engineering education in India. Procedia Comput. Sci. 172, 791–796, ISSN 1877–0509 (2020). https://doi.org/10.1016/j.procs.2020.05.113

  35. Somasundaram, M., Latha, P., Saravana Pandian, S.A.: Curriculum design using artificial intelligence (AI) back propagation method. Procedia Comput. Sci. 172, 134–138, ISSN 1877–0509 (2020). https://doi.org/10.1016/j.procs.2020.05.020

  36. Kulkarni, V.N., Gaitonde, V.N., Kotturshettar, B.B., Satish, J.G.: Adapting industry based curriculum design for strengthening post graduate programs in Indian scenario, Procedia Comput. Sci. 172, 253–258 (2020), ISSN 1877–0509. https://doi.org/10.1016/j.procs.2020.05.040

  37. Priyambada, S.A., Mahendrawathi, E.R., Yahya, B.N.: Curriculum assessment of higher educational institution using aggregate profile clustering. Procedia Comput. Sci. 124, 264–273, ISSN 1877–0509 (2017). https://doi.org/10.1016/j.procs.2017.12.155

  38. Bendatu, Y., Yahya, B.N.: Sequence matching analysis for curriculum development. Jurnal Teknik Industri, 17 (2015). https://doi.org/10.9744/jti.17.1.47-52

  39. Cao, P.Y., Ajwa, I.A.: Enhancing computational science curriculum at liberal arts institutions: a case study in the context of cybersecurity. Procedia Comput. Sci. 80, 1940–1946, ISSN 1877–0509 (2016). https://doi.org/10.1016/j.procs.2016.05.510

  40. Rodriguez, J.: Modularization of new course for integration in existing curriculum. Procedia Comput. Sci. 172, 817–822, ISSN 1877–0509 (2020). https://doi.org/10.1016/j.procs.2020.05.117

  41. Ellahi, R.M., Khan, M.U.A., Shah, A.: redesigning curriculum in line with industry 4.0. Procedia Comput. Sci. 151, 699–708, ISSN 1877–0509 (2019). https://doi.org/10.1016/j.procs.2019.04.093

  42. Bekmanova, G., Ongarbayev, Y.: Flexible model for organizing blended and distance learning. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12250, pp. 282–292. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58802-1_21

    Chapter  Google Scholar 

  43. https://protege.stanford.edu/

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Bekmanova, G., Nazyrova, A., Omarbekova, A., Sharipbay, A. (2021). The Model of Curriculum Constructor. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_32

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  • DOI: https://doi.org/10.1007/978-3-030-86960-1_32

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  • Online ISBN: 978-3-030-86960-1

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