INTELLIGENT ANALYSIS OF STUDENTS DATASET

Todorka Atanasova (t_atanasova@ue-varna.bg), Nadezhda Filipova (filipova@ue-varna.bg), Snezhana Sulova (ssulova@ue-varna.bg), Yanka Alexandrova (yalexandrova@ue-varna.bg) and Julian Vasilev (vasilev@ue-varna.bg)
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Todorka Atanasova: University of Economics Varna, Bulgaria
Nadezhda Filipova: University of Economics Varna, Bulgaria
Yanka Alexandrova: University of Economics Varna, Bulgaria
Authors registered in the RePEc Author Service: Yanka Georgieva Aleksandrova (yalexandrova@ue-varna.bg)

in Monographic library "Knowledge and business" Varna from Publishing house "Knowledge and business" Varna

Abstract: The purpose of this work is to create a methodology for intelligent data analysis for students, which includes: data collection and processing with different methods of analysis, in the environment of different software products, using them by researchers with knowledge in various aspects of the intelligent analysis of data and study of the studied dataset in several perspectives. In particular, the proposed methodology uses: statistical methods, neural networks, decision trees and specialized methods for processing unstructured information. The subject of study is the students of the "Informatics" and "Business Information Systems" specialties from the second and fourth year, trained at the University of Economics Varna. The subject of this research is their opinions, attitudes and assessments related to the learning process. The research team processed data from two consecutive years using modern analytical methods using the following specialized software products: Rapid Miner, Alyuda NeuroIntelligence and PSPP. A knowledge database for students is formed. In this work, the intelligent data analysis methodology is validated by empirical research. The monographic work is developed under the research project NP-156/2015, financed under Ordinance 3 "Methodology for intelligent analysis of data for students and creation of a knowledge base", headed by Assoc. Prof. Dr. T. Atanasova.

Keywords: Educational Data Mining; PSPP; Rapid Miner; neural networks (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:kab:monogr:4

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