APPLICATION OF DATA MINING TOOLS FOR ANALYSIS AND FORECASTING OF LEARNING SATISFACTION

The article discusses the features and results of using data mining tools, an application for building See5 decision trees, with which a number of parameters are determined on the basis of sociological survey data that are most important for assessing student satisfaction. Along with the features of using a software tool for data analysis in the work, the procedure for preparing data for downloading and further analysis is shown. At the same time, not only file formats loaded into the application are presented, but also approaches to cleaning and aggregating source data, including in order to reduce their dimensionality. Interpretation of the results of constructing decision trees - classification rules obtained during processing, is aimed, inter alia, at choosing the optimal settings for processing the source data. The classifier constructed using the data of a sociological survey showed that such parameters as the atmosphere of interpersonal relations, recognition of the achievements of the student, and the prestige of the educational organization have a significant impact on the classification. The paper also describes the procedure for assessing the predicted values of student satisfaction based on the input of the estimated values of the classification features.

1. Kleimenov E.A. Institutional expectations of cadets and students in an educational organization. Power and Management in the East of Russia. 2018;3:92-105. (In Russ.).

2. Rusanova A.A. Satisfaction as an indicator of the quality of education in a modern university (from the experience of a specific sociological study). Modern Research on Social Problems (electronic scientific journal). 2011;3:21-26. (In Russ.).

3. Kiseleva M.V. Involving university students in assessing and improving the quality of education. The Bulletin of Kostroma State University. Series: Pedagogy. Psychology. Sociokinetics. 2016;22(3):142-147. (In Russ.).

4. Bondar K.M., Skripko P.B. Analysis of data in education as a basis for improving the quality of educational content and the effectiveness of training. Socio-psychological and pedagogical determinants of the formation and development of professional motivation of cadets and students in the learning process in educational organizations of the Ministry of Internal Affairs of Russia Materials of the All-Russian Scientific and Practical Conference. Executive Editor A.A. Andreev. 2017:130-133. (In Russ.).

5. Pappas Ch. 9 Tips To Give and Receive eLearning Feedback. Available by: https://elearningindustry.com/9-tips-give-receive-elearning-feedback (accessed September 2, 2019).

6. Skripko P.B., Dunin V.S. Directions for the application of data mining methods for evaluating the results of e-learning. In the collection: State and law in the era of global change, materials of the international scientific and practical conference. Edited by S.K. Buryakova. 2017:307-308. (In Russ.).

7. Agaoglu M. Predicting Instructor Performance Using Data Mining. IEEE Access. 2016;4:2379-2387.

8. Дюк В., Самойленко А. Data Mining: учебный курс. СПб.: Питер; 2001:366.

9. Barseghyan A.A. et al. Analysis of data and processes: textbook allowance. St. Petersburg: BHV-Petersburg;2009:512. (In Russ.).

10. Bondar K.M., Emelyanova O.V., Skripko P.B. Methodology for conducting analytical work in the Department of Internal Affairs: a training manual. Far Eastern legal Institute of the Ministry of Internal Affairs of the Russian Federation. Khabarovsk: RIO DVUI of the Ministry of Internal Affairs of the Russian Federation;2013:144. (In Russ.).