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Aksu, Gökhan; Dogan, Nuri – Pegem Journal of Education and Instruction, 2019
The purpose of this study is to compare decision trees obtained by data mining algorithms used in various areas in recent years according to different criteria. In the study, similar and different aspects of the decision trees obtained by different methods for classifying the students as successful and unsuccessful in terms of science literacy…
Descriptors: Data Analysis, Decision Support Systems, Visual Aids, College Students
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Toprak, Emre; Gelbal, Selahattin – International Journal of Assessment Tools in Education, 2020
This study aims to compare the performances of the artificial neural network, decision trees and discriminant analysis methods to classify student achievement. The study uses multilayer perceptron model to form the artificial neural network model, chi-square automatic interaction detection (CHAID) algorithm to apply the decision trees method and…
Descriptors: Comparative Analysis, Classification, Artificial Intelligence, Networks
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Eva Expósito-Casas; Ana González-Benito; Esther López-Martín – International Journal for Educational and Vocational Guidance, 2024
The purpose of this work is to identify contextual variables that help to explain the occupational aspirations of Spanish 15-year-old students. This is done by performing a secondary analysis of the PISA2018 test. Data have been analysed using decision trees introducing the students' expected occupational status as a dependent variable (DV), and…
Descriptors: Occupational Aspiration, Secondary School Students, Foreign Countries, Self Concept
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Buyukatak, Emrah; Anil, Duygu – International Journal of Assessment Tools in Education, 2022
The purpose of this research was to determine classification accuracy of the factors affecting the success of students' reading skills based on PISA 2018 data by using Artificial Neural Networks, Decision Trees, K-Nearest Neighbor, and Naive Bayes data mining classification methods and to examine the general characteristics of success groups. In…
Descriptors: Classification, Accuracy, Reading Tests, Achievement Tests
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dos Santos, Roberta Alvarenga; Paulista, Cássio Rangel; da Hora, Henrique Rego Monteiro – Technology, Knowledge and Learning, 2023
The demand for in-depth studies on educational data presupposes the application of technologies that allow data analysis of vast quantities, and subsequently, drawing relevant information and knowledge. The research objective herein is to employ data mining techniques on PISA databases to identify potential patterns that may explain the…
Descriptors: Foreign Countries, Achievement Tests, International Assessment, Secondary School Students
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Huang, Hung-Yu – Educational and Psychological Measurement, 2020
In educational assessments and achievement tests, test developers and administrators commonly assume that test-takers attempt all test items with full effort and leave no blank responses with unplanned missing values. However, aberrant response behavior--such as performance decline, dropping out beyond a certain point, and skipping certain items…
Descriptors: Item Response Theory, Response Style (Tests), Test Items, Statistical Analysis
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Aksu, Nursah; Aksu, Gökhan; Saracaloglu, Seda – International Electronic Journal of Elementary Education, 2022
The purpose of this study is to predict the mathematical literacy levels of the students participating in the research through the data obtained from PISA 2015 exam organized by OECD using data mining and to determine the variables that affect mathematics literacy. For this purpose, students' mathematics literacy levels and the variables that…
Descriptors: Predictor Variables, International Assessment, Achievement Tests, Foreign Countries