Machine Learning and Student’s Educational Trajectory Mathematical Modelling
Keywords:
machine learning, mathematical model, GlobalLab, online training, educational trajectoryAbstract
The purpose of the article is to describe stages of variables formation and identification and develop a student’s educational trajectory mathematical model and to create a technological model for the application of machine learning methods to predict the optimal educational trajectory of the student. External variables are analyzed, variables’ numerical values are determined, types of collecting information sessions are highlighted. Data arrays from information sources electronic diary Diary.ru and the GlobalLab online platform were analyzed. A mathematical model of the student is presented based on variables. Using the technical and scientific results of the Project will provide useful economic, technological and technical effects.
References
Krupa, T. V. (2018). Theoretical studies of the performed for the Stages of the ASR tasks. Moscow: GlobalLab, LLC,
Furukawa, L. (2018). Trajectory of Learning Experience based on the Performance of Canada's Youth in Mathematics.
International Journal of Innovation in Science and Mathematics Education, 26(6), 62-75.
Dunphy, E., Dooley, T., Shiel, G. (Eds.), (2014). Mathematics in Early Childhood and Primary Education (3–8 years) Definitions, Theories, Development and Progression. Dublin: National Council for Curriculum and Assessment.
OECD. (2013). PISA 2012 Assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. Paris: OECD Publishing.
OECD (2014a). PISA 2012 Results: What students know and can do. Student Performance in Mathematics, Reading and Science, 1, Paris: OECD Publishing. https://doi.org/10.1787/19963777
OECD (2014b). A Profile of student performance in mathematics, in PISA 2012 Results: What Students Know and Can Do (Volume I, Revised edition, February 2014): Student Performance in Mathematics, Reading and Science, OECD Publishing, Paris. https://doi.org/10.1787/9789264208780-en
OECD (2014c). Mathematics performance (PISA) (indicator). DOI: 10.1787/04711c74-en
Xavier, J. R., Avula, R., Kalman, E. et al. (eds.). (1984). Mathematical Modelling in Science and Technology. The Fourth International Conference, Zurich, Switzerland, August 1983. Pergamon, https://doi.org/10.1016/C2013-0-06067-7
Azam, I., Hasan, M. A., Abbasi, T., Ahmed, S., Abbasi, M. (2010). Education Technology Based Models of Teaching and Learning. Proceedings of the 4th National Conference; INDIACom-2010 Computing for Nation Development, February 25 – 26, 2010 Bharati Vidyapeeth’s Institute of Computer Applications and Management, New Delhi.
Taguma, M., Gabriel, F., Lim, M. H. (2019). Future of Education and Skills 2030: Curriculum analysis A Synthesis of Research on Learning Trajectories/Progressions in Mathematics. OECD. Organisation for Economic Co-operation and Development EDU/EDPC(2018)44/ANN3, Available at: https://www.oecd.org/education/2030-project/about/documents/A_Synthesis_of_Research_on_Learning_Trajectories
_Progressions_in_Mathematics.pdf
Mitchell, D. (2015). Education that fits: Review of International trends in the education of students with special educational needs. University of Cambridge.
Konnova, L., Lipagina, L., Postovalova, G. Rylov I. Stepanyan,A. (2019). Designing Adaptive Online Mathematics Course Based on Individualization Learning. Education Sciences, 9(3):182. https://doi.org/10.3390/educsci9030182
Dogra, G. (2019). Machine Learning and the Future of Education. AI Business. Available at: https://aibusiness.com/machine-learning-and-the-future-of-education/
Sharma, D. (2019, Oct. 1). Artificial Intelligence and Big Data in Higher Education: Promising or Perilous? SmartDataCollective. Available at: https://www.smartdatacollective.com/artificial-intelligence-and-big-data-in-higher-education-promising-or-perilous/
Gardner, P. Rix, C. (2012). Learning Trajectories of Primary Student Teachers; a Cross-Cultural Comparison. Journal of Social Sciences, 8 (2): 135-142.
Makhnytkina, O. V. (2013). Modeling and optimization on an individual trajectory of an ordinary student: Abstract thesis. Novosibirsk.
Granichina, O. A. (2006). Mathematical models of the quality control of the educational process in the university with active optimization. Stokhaisticheskaya optimizatsiya v informatike, 2: 77-108. Available at: https://www.math.spbu.ru/user/gran/sb2/granolga.pdf
Sztajn, P., Confrey, J. Wilson, P. H., Edgington, C. (2012). Learning Trajectory Based Instructions: Toward a Theory of Teaching. Educational Researcher, 41(5): 147-156. Available at: https://www.jstor.org/stable/23254092?seq=1
Merzon, E., Galimullina, E., Ljunimova, E. (2019). A smart trajectory model for teacher training. In Cases on Smart Learning Environments (pp. 164-187). DOI: 10.4018/978-1-5225-6136-1.ch010
Ovchinnikov, P. V. (2014). Mathematical Models and Instruments for Designing Adaptive Educational Trajectories for Preparation of Competitive Specialists in Universities: Abstract thesis. Rostov-on-Don. Available at: http://economy-lib.com/matematicheskie-modeli-i-instrumentariy-proektirovaniya-adaptivnyh-obrazovatelnyh-traekt oriy-dlya-podgotovki-konkurentosp
Voronova, N. A., Kupchishin, A. I., Kupchishin, A. A., Kuatbayeva, A. A., Shmygaleva, T. A. (2018). Computer modeling of depth distribution of vacancy nanoclusters in ion-irradiated materials. Key Engineering Materials, 769: 358-363. https://doi.org/10.4028/www.scientific.net/KEM.769.358
Ivanova, N., Sorokina, T. (2019). Educational environment approach to preventing the growth of school students anxiety in the transition from primary to secondary school. Elementary Education Online, 19(1): 333-342. DOI: 10.17051/ilkonline.2020.661841
Dudukalov, E.V. (2010). Vzaimodeystviye tekhnologicheskikh i institutsional'nykh faktorov razvitiya informatsionnoy ekonomiki [Interaction of technological and institutional factors of the information economy development]: PhD thesis. Rostov-on-Don.
De la Hoz-Rosales, B., Camacho, J., Tamayo, I. (2019). Effects of innovative entrepreneurship and the information society on social progress: an international analysis. Entrepreneurship and Sustainability Issues, 7(2): 782-813. DOI: 10.9770/jesi.2019.7.2(1)
Bertsimas, D., Stellato, B. (2021). The voice of optimization, Machine Learning, 110, 249–277. https://doi.org/10.1007/s10994-020-05893-5
van den Heuvel-Panhuizen M., Drijvers P. (2020). Realistic Mathematics Education. In: Lerman S. (eds) Encyclopedia of Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-030-15789-0_170
van der Schaar, M., Alaa, A.M., Floto, A. et al. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110, 1–14. https://doi.org/10.1007/s10994-020-05928-x
Azevedo Santos, R., Paes, A. & Zaverucha, G. (2020). Transfer learning by mapping and revising boosted relational dependency networks. Machine Learning, 109, 1435–1463. https://doi.org/10.1007/s10994-020-05871-x
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