User Behavioral Data Enhancement and Vectorization in E-Learning Models
Keywords:
Mathematical model, E-learning, Learning trajectory, Behavioral data, Education procedureAbstract
The data acquiring mechanism on the criteria of users-interaction to the users-interface has been developed in the initial stage of this applied science research. The input system gets multiple inhomogeneous texts about the interface actions of a specific user, whereas the vector has been demonstrated by the results that describe the user in a consolidated manner. Moreover, the vector collection for various users has been employed as an input of clustering algorithm (i.e. K-means), the outcomes of behaviors of users are among the one of these (k-clusters) that differentiates the users by their behavioral types. Almost, 67.8% of the Interactional data of the interface of the user is accessible to GlobalLab platform users. However, for the electronic diary (i.e.Diary.ru) there is no corresponding data. Although not every user of the GlobalLab system takes the initiative to create the projects, ideas, questionnaire task, and educational resources, the proportion of learners have been 9.7 thousand whose value with all 4 variables varied according to a neutral one.
References
T. V. Krupa, Theoretical studies of the performed for the Stages of the ASR tasks. Moscow: GlobalLab, LLC, 2018.
L. Furukawa, “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, 2018.
G. Lipatnikova, A. S. Polyanina, “The formation of the target component of educational activities of students using decision-making techniques in the framework of the reflexive approach”, Problems and Methods of Teaching Natural Sciences and Mathematics: Materials of the III All-Russian Scientific and Practical Conference (Ekaterinburg, December 2007) (pp. 194-197). Ekaterinburg: Publishing House of the Ural Institute of Economics, Management and Law, 2007.
G. Lipatnikova, “The creation on an individual educational thakectory as one of ways to reach students in decision making”, Fundamental researches, 5: 108-110, 2009.
O. A. Granichina, “Mathematical models of the quality control of the educational process in the university with active optimization”, Stokhaisticheskaya optimizatsiya v informatike, 2: 77-108, 2006. Retrieved from https://www.math.spbu.ru/user/gran/sb2/granolga.pdf
H. Choi, K. Cho, Y. Bengio, “Context-dependent word representation for neural machine translation”, Computer Speech & Language, 45: 149-160, 2017.
M. Taguma, F. Gabriel, M. H. Lim, “Future of Education and Skills 2030: Curriculum anaktsis: A synthesis of research on learning trajectories/Progressions in mathematics”, OECD. EDU/EDPC(2018)44/ANN3, 2019. Retrieved from https://www.oecd.org/education/2030- project/about/documents/A_Synthesis_of_Research_on_Learning_Trajectories_Progressions_in_Mat hematics.pdf
“Transforming Our World, the 2030 Agenda for Sustainable Development”, United Nations General Assembly Resolution A/RES/70/1, 2015. Retrieved from http://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E
M. McCartney, The Indian Economy. Newcastle upon Tyne: Agenda Publishing, 2019. DOI: 10.2307/j.ctvnjbfk1
M. Ndlovu, “Modeling with Sketchpad to enrich students' concept image of the derivative in introductory calculus: developing domain specific understanding”, 2008. Retrieved from https://www.researchgate.net/publication/323120414_Modeling_with_Sketchpad_to_enrich_students '_concept_image_of_the_derivative_in_introductory_calculus_developing_domain_specific_understa nding
I. Azam, M. A. Hasan, T. Abbasi, S. Ahmed, M. Abbasi, “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, 2010.
Duke University. "Machine learning predicts behavior of biological circuits: Neural networks cut modeling times of complex biological circuits to enable new insights into their inner workings." ScienceDaily. ScienceDaily, 2 October 2019. Retrieved from www.sciencedaily.com/releases/2019/10/191002165235.htm.
P. Sztajn, J. Confrey, P. H. Wilson, C. Edgington, “Learning Trajectory Based Instruction: Toward a Theory of Teaching”, Educational Researcher, 41 (5), 2012.
I. Gatopolos, “Vectorization: How to speed up your Machine Learning algorithm by x78”, Towards Data Science, 2019. Retrieved from https://towardsdatascience.com/vectorisation-how-to-speed-up- your-machine-learning-algorithm-by-x78-times-faster-e330df8c9b27
E. Merzon, E. Galimullina, E. Ljunimova, “A smart trajectory model for teacher training”, In Cases on Smart Learning Environments (pp. 164-187), 2019. DOI: 10.4018/978-1-5225-6136-1.ch010
M. N. Ryzhkova, “The mathematical model of the process of educayion management”, Bestnik Cherepovetskogo gosudarstvennogo universiseta, 6: 41-47, 2015. Retrieved from https://cyberleninka.ru/article/n/matematicheskaya-model-protsessa-upravleniya-obucheniem
A. Freiberg-Hoffman, J. B. Stover, N. Donis, “Influence of Learning Strategies on Learning Styles: Their Impact on Academic Achievement of College from Buenos Aires”, Problems of education in the 21st century, 75 (1), 2017. Available: http://www.scientiasocialis.lt/pec/node/files/pdf/vol75/6- 18.Freiberg-Hoffmann_Vol.75-1_PEC.pdf
H. Jafari, A. Aghaei, A. Khatony, “Relationship between study habits and academic achievement in students of medical sciences in Kermanshah-Iran”, Advanced in Medical Education and Practice, 10, 2019. DOI: 10.2147/AMEP.S208874
P. V. Ovchinnikov, Mathematical Models and Instruments for Designing Adaptive Educational Trajectories for Preparation of Competitive Specialists in Universities: Abstract thesis. Rostov-on- Don, 2014. Retrieved from http://economy-lib.com/matematicheskie-modeli-i-instrumentariy- proektirovaniya-adaptivnyh-obrazovatelnyh-traektoriy-dlya-podgotovki-konkurentosp
D. Sharma, “Artificial Intelligence and Big Data in Higher Education: Promising or Perilous?”, SmartDataCollective. Retrieved from https://www.smartdatacollective.com/artificial-intelligence- and-big-data-in-higher-education-promising-or-perilous/
C. Romero, S. Ventura, “Educational Data Mining: A Review of the State of the Art. Systems, Man, and Cybernetics, Part C: Applications and Reviews”, IEEE Transactions, 40: 601-618, 2010. DOI: 10.1109/TSMCC.2010.2053532
Ye.V. Dudukalov, “Usloviya ekonomicheskogo rosta na etape postindustrial'noy transformatsii mirovoy ekonomiki [Conditions for economic growth at the stage of post-industrial transformation of the world economy]”, Gosudarstvennoye i munitsipal'noye upravleniye. Uchenyye zapiski SKAGS, 3: 89-94, 2013.
M. Hitka, S. Lorincová, M. Vetráková, I. Hajdúchová, I. Antalík, “Factors related to gender and education affecting the employee motivation”, Entrepreneurship and Sustainability Issues, 7(4): 3226-3241, 2020, DOI: 10.9770/jesi.2020.7.4(43)
V. Volchik, E. Maslyukova, “Trust and development of education and science”, Entrepreneurship and Sustainability Issues, 6(3): 1444-1455, 2019, DOI: 10.9770/jesi.2019.6.3(27)
M.Y. Kizatova, Y.B. Medvedkov, A.A. Shevtsov, A.V. Drannikov, D.A. Tlevlessova, “Experimental-statistical analysis and multifactorial process optimization of the crust from melon pulp separation process”, Journal of Engineering and Applied Sciences, 12: 1762-1771, 2017, DOI: 10.36478/jeasci.2017.1762.1771
Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e- learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143.
Harrati, N., Bouchrika, I., Tari, A., & Ladjailia, A. (2016). Exploring user satisfaction for e-learning systems via usage-based metrics and system usability scale analysis. Computers in Human Behavior, 61, 463–471
Hssina, B., & Erritali, M. (2019). A personalized pedagogical objectives based on a genetic algorithm in an adaptive learning system. Procedia Computer Science, 151, 1152-1157.
Colchester, K., Hagras, H., Alghazzawi, D., & Aldabbagh, G. (2017). A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 47-64.
Wardhana, A. E., Subroto, I. M. I., & Prasetyowati, S. A. D. (2017). Optimizing Group Discussion Generation Using K-Means Clustering and Fair Distribution. Journal of Telematics and Informatics (JTI), 5(2), 1-7.
Baharudin, A. F., Sahabudin, N. A., & Kamaludin, A. (2017). Behavioral tracking in E-learning by using Learning styles approach. Indonesian Journal of Electrical Engineering and Computer Science, 8(1), 17-26.
El Haddioui, I., & Khaldi, M. (2012). Learner behavior analysis on an online learning platform. International Journal of Emerging Technologies in Learning (iJET), 7(2), 22-25.
Meghji, A. F., Mahoto, N. A., Unar, M. A., & Shaikh, M. A. (2018, April). Analysis of student performance using edm methods. In 2018 5th International Multi-Topic ICT Conference (IMTIC) (pp. 1-7). IEEE
Grigorova, K., Malysheva, E., & Bobrovskiy, S. (2017, April). Application of data mining and process mining approaches for improving e-learning processes. In 3rd International Conference on Information Technology and Nanotechnology (pp. 25-27)
Mahajan, G., & Saini, B. (2020). Educational Data Mining: A state-of-the-art survey on tools and techniques used in EDM. International Journal of Computer Applications & Information Technology, 12(1), 310-316
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