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
Yahya, A., Osman, A.: Using data mining techniques to guide academic programs design and assessment (2019)
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
Redecker, C.: European framework for the digital competence of educators: DigCompEdu. – Joint Research Centre (Seville site) (2017), JRC107466
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
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
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
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
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)
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)
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)
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)
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)
Chakraborty, U.K., Roy, S.: Fuzzy automata inspired intelligent assesment of learning achievement IICAI, 1505–1518 (2011)
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)
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
Hettiarachchi, E., et al.: A standard and interoperable technology-enhanced assessment system for skill and knowledge acquirement CSEDU, vol. 2, pp. 157–160 (2012)
Hettiarachchi, E., et al.: A technology enhanced assessment system for skill and knowledge learning CSEDU, vol. 2, 184–191 (2014)
Ishak, I.: Application of fuzzy logic to student performance in calculation subjects. In: Proceedings of the 4th National Symposium & Exhibition on Business & Accounting (2015)
Gokmen, G., et al.: Evaluation of student performance in laboratory applications using fuzzy logic. Procedia-Soc. Behav. Sci. 2(2), 902–909 (2010)
Voskoglou, M.G.: Fuzzy logic as a tool for assessing students’ knowledge and skills. Educ. Sci. 3(2), 208–221 (2013)
Iskander, M. (ed.): Innovations in E-Learning, Instruction Technology, Assessment and Engineering Education. Springer Science & Business Media (2007)
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)
Stathacopoulou, R., et al.: Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis. Inf. Sci. 170(2–4), 273–307 (2005)
Zadeh, L.A.: Fuzzy logic. Computer 21(4), 83–93 (1988)
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)
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)
Karthika, R., Deborah, L.J., Vijayakumar, P.: Intelligent e-learning system based on fuzzy logic. Neural Comput. Appl., 1–10 (2019)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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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