Time Portrait of the Student’s Behavior and Possibilities of its Use
DOI:
https://doi.org/10.20535/2786-8729.4.2024.291976Keywords:
classification, student behavior, supporting educational activities, time series, neural network, machine learningAbstract
The object of research presented in this article is the process of classifying students' behavior based on a formalized description of task performance during a certain learning cycle.
The purpose of this study is to create a formalized description of the behavior of students regarding the performance of tasks during a certain learning cycle to improve the reliability of the characteristics of each student during automated data analysis in information systems.
To achieve this goal, a temporal portrait of the student's behavior is proposed. This is a stylized presentation of a time series in the form of a line, the shape of which represents the delay or advance of the deadlines for completing tasks when studying a certain discipline.
The main types of student behavior are highlighted. Each type of behavior corresponds to the shape of the line on the time portrait.
To provide information systems with the capabilities of behavior analysis, the issue of portrait classification by neural networks has been investigated. It is proposed to perform classification using a multilayer neural network. To speed up learning and facilitate further classification, it is proposed to divide the network into several subnets, each of which can be trained independently.
The issues of appropriate training of neural networks based on datasets of real training classes with groups of students are analyzed. The ability of the neural network to classify portraits of students' behavior has been proven.
The results of the research can be used for data analysis in computerized learning support systems
References
Campus. “Electronic Campus of Igor Sikorsky Kyiv Polytechnic Institute.” (in Ukrainian). Accessed: Nov. 1, 2023. [Online]. Available: https://campus.kpi.ua
MyKPI. “Organization of the educational process, practice, and internship.” (in Ukrainian). Accessed: Nov. 1, 2023. [Online]. Available: https://my.kpi.ua
E. T. Lau, L. Sun, and Q. Yang, "Modelling, prediction and classification of student academic performance using artificial neural networks," SN Appl. Sci., vol. 1, 2019, Art. no. 982, https://doi.org/10.1007/s42452-019-0884-7.
F. Yang and F. W. Li, "Study on student performance estimation, student progress analysis, and student potential prediction based on data mining," Computers & Education, vol. 123, pp. 97-108, Aug. 2018, https://doi.org/10.1016/j.compedu.2018.04.006.
F. Ouyang, M. Wu, L. Zheng, L. Zhang, and P. Jiao, "Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course," International Journal of Educational Technology in Higher Education, vol. 20, 2023, Art. no. 4, https://doi.org/10.1186/s41239-022-00372-4.
F. Guiyun and F. Muwei, "Research on learning behavior patterns from the perspective of educational data mining: Evaluation, prediction and visualization," Expert Systems with Applications, vol. 237, part B, Mar. 2023, Art. no. 121555, https://doi.org/10.1016/j.eswa.2023.121555.
K. Akhuseyinoglu and P. Brusilovsky, "Exploring Behavioral Patterns for Data-Driven Modeling of Learners. Individual Differences," Frontiers in Artificial Intelligence, vol. 5, Feb. 2022, Art. no. 807320, https://doi.org/10.3389/frai.2022.807320.
V. Poriev, “Some aspects of building an intelligent software system to support educational process,” in Proc. Int. Conf. Security, Fault Tolerance, Intelligence ( ICSFTI2019), Kyiv, Ukraine, May 14-15, 2019. pp.15-22.
G. Susto, A. Cenedese, and M. Terzi, "Time-Series Classification Methods: Review and Applications to Power," in Big Data Application in Power Systems, R. Arghandeh and Y. Zhou, Eds., New York, NY, USA: Elsevier, 2018, ch. 9, pp. 179-220,https://doi.org/10.1016/B978-0-12-811968-6.00009-7.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature.Computer Science, vol. 323, pp. 533–536, Oct. 1986, https://doi.org/10.1038/323533a0.
C. Zhicheng, C. Wenlin and C. Yixin, "Multi-Scale Convolutional Neural Networks for Time Series Classification," Mar. 2016, arXiv:1603.06995, https://doi.org/10.48550/arXiv.1603.06995.
