DEVELOPMENT OF AN INFORMATION AND EDUCATIONAL PORTAL OF DISTANCE LEARNING BASED ON EDUCATIONAL DATA MINING

S. Toxanov, D. Abzhanova, A. Faizullin

Аннотация


Currently, there is an increase in demand for distance education programs, which actualizes the problems of organizing the educational process at universities using these technologies. The article highlights and describes the characteristic features and prospects of using the analysis of educational data in the information and educational portal of distance learning, in order to implement adaptive learning and learning in accordance with dynamically formed individual trajectories. The task is to create a fundamentally new information system of the university using the results of the analysis of educational data. One of the functions of such a system is to extract knowledge from the data accumulated during operation. Creating own system of this type is an iterative and time-consuming process that requires preliminary research and step-by-step prototyping of modules. The novelty lies in the fact that there is currently no methodology for developing such systems in Kazakhstan, so a number of experiments were conducted in order to collect data, select suitable methods for studying the collected data, and then interpret them. As a result of the experiment, the authors identified the sources of educational data available for analysis in the information environment of the university. The data of semester academic performance obtained from the Toraighyrov University information system, data obtained as a result of independent work of students and data obtained using specially developed Google-forms were taken as a basis. An information and educational portal was created for the automated collection, processing and analysis of educational data. Based on the study of students’ behavior, it becomes possible to form recommendations for teachers to improve the content and structure, as well as recommendations for the training of students. The data contained in the activity logs are examined to obtain information, search for dependencies by filtering relevant logs, structuring information from them and providing data in a form convenient for analysis and drawing conclusions. The data of the main types of events generated as a result of recording user actions in the learning management system and scenarios for using the results of the analysis of these data are considered. The elements of the software implementation of this system are described in detail, conclusions are made about the availability of the data sources used, and conclusions are drawn about the prospects for further development.

Ключевые слова


information and educational portal, data mining, Educational Data Mining, e-learning, learning analytics.

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Литература


Larose, D., Larose, Ch. (2014). Discovering knowledge in data: an introduction to data mining. Second Edition. New Jersey: John Wiley & Sons, 316.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers and Education, 49(2), 396-413.

Liaw, S. S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers and Education, 51(2), 864-873.

Ozkan, S., Koseler, R. (2009). Multi-dimensional students' evaluation of e-learning systems in the higher education context: An empirical investigation. Computers and Education, 53(4), 1285-1296.

Zervas, P., Kardaras, V., Sampson, D. G. (2014). An online educational portal for supporting open access to teaching and learning of people with disabilities. Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014 6901541, 564-565.

Maldonado, U. P. T., Khan, G. F., Moon, J., Rho, J. J. (2011). E-learning motivation and educational portal acceptance in developing countries. Online Information Review, 35(1), 66-85.

Romero, C., Ventura, S., García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers and Education, 51(1), 368-384.

Romero, C., Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.

Shahiri, A. M., Husain, W., Rashid, N. A. (2015). Review on Predicting Student's Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414-422.

Asif, R., Merceron, A., Ali, S. A., Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers and Education, 113, 177-194.

Dutt, A., Ismail, M. A., Herawan, T. (2017). A Systematic Review on Educational Data Mining. IEEE Access, 5, 15991-16005.

Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 41(4), 1432-1462.

Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), 49-64.

Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019). A Systematic Review of Deep Learning Approaches to Educational Data Mining. Journal of Computer Networks and Communications, 4(1).




DOI: http://dx.doi.org/10.37943/AITU.2022.48.68.003

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