Comparison of Classification Performances of Mathematics Achievement at PISA 2012 with the Artificial Neural Network, Decision Trees and Discriminant Analysis

Toprak, Emre; Gelbal, Selahattin

doi:10.21449/ijate.778864

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…However, when the dataset contains sensitive personal information (e.g., patient's diagnosis information and customer's shopping information), mining and analyzing the dataset using Pearson correlation coefficient-based decision trees may create privacy leakage problems, which may threaten the privacy security of users. Based on the Pearson correlation coefficient-based decision tree algorithm, we propose a Pearson correlation coefficient-based differential Occupational Therapy International privacy decision tree algorithm to ensure the effectiveness and usability of the algorithm while satisfying the differential privacy [26].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

Wang

Zhang

2022

Occupational Therapy International

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With the development of society, psychological health becomes a basic standard for a college student to grow into a qualified person. This study is aimed at using data mining principles and methods to excavate the factors that lead to psychological problems of college students, to purposefully carry out psychological interventions, use visual art design methods to promote college students’ psychological health treatment, and build a perfect system of college students’ psychological treatment. Based on the theories of data mining, we built a data analysis model, elaborated the data preprocessing method, and applied the Apriori algorithm to analyze the data of obsessive-compulsive symptoms and interpersonal sensitivity symptoms, and various psychological problem attributes extracted the strong association rules and analyzed the results. Take advantage of the corresponding unique school environment and educational advantages, to build a set of mental health education methods suitable for modern college students, and truly enable them to obtain satisfactory psychological interventions in a reasonable art design treatment phase. Based on the above association pattern mining results, a series of preprocessing operations were performed on the data, and then, the Apriori algorithm was applied to discover the potential association relationships among 9 psychological dimension factors of college students, and then, the ID3 decision tree algorithm was used to construct a decision tree and pruning process, from which the classification rules of students’ psychological problems were analyzed and discovered. These studies provide some practical reference basis for school counseling work.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

Wang

Zhang

2022

Occupational Therapy International

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“…This study differs from other studies in the literature on PISA in terms of the EDM methods and variables used [4][5][6][7]. Random Forest [4,7], Discriminant Analysis [8], Decision Tree [5,7,9], Logistic Regression [10], Naïve Bayes [11,12] studies are encountered in the literature. However, no article was found using these methods and the data set mentioned in the article.…”

Section: Introductionmentioning

confidence: 72%

“…As a result of the study, it was stated that the Random Forest algorithm produced more successful results. Toprak and Gelbal [8] compared classification performances of artificial neural networks, decision trees and discriminant analysis at PISA 2012 mathematical literacy score for different sample sizes. They used all student data for analysis with 17 mathematical success related variables.…”

Section: Literaturementioning

confidence: 99%

Classification of Students’ Mathematical Literacy Score Using Educational Data Mining: PISA 2015 Turkey Application

Şehitoğlu¹,

Serkan²,

Demir³

2022

Cumhuriyet Science Journal

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PISA 2015 mathematical literacy score of Turkey is 420 while the average score of all countries is 461. It is understood that; Turkish students’ PISA 2015 mathematical literacy score was lower than the average. The basic reasons for the below average score need to be truly examined and developmental activities should be revealed. The aim of this study is to classify students according to the factors affecting their mathematical literacy score and to reveal the effects of these factors in classification.The data of the study is obtained from 5895 students who participated in PISA 2015. In this study, we used Random Forest, Naïve Bayes Classifier, Logistic Regression, Decision Tree Algorithm and Discriminant Analysis as classifiers. According to the results, Random Forest method produced more accurate scores than other methods with 76.32% accuracy. We also calculated the correct classification rate and determined the factors that positively and negatively affect the classification with discriminant analysis. According to the discriminant analysis home possessions, information and computer technology resources at home and students' expected occupational status were the most positive effective variables on mathematical literacy score. On the other hand, family wealth possessions, student-related factors affecting school climate and anxiety have negative effect on mathematical literacy score.

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“…Zijian Chen et al nested an ensemble learning training model on top of the original classical model, but in this study, only three categories of academic performance prediction results were classified, and the prediction accuracy was low [25]. Emre Toprak et al compared artificial neural networks, decision trees and linear discriminant methods, and analyzed the advantages and disadvantages of each method for different data types [26]. Pratya et al combined artificial neural networks, decision tree algorithms and Bayesian algorithms to construct prediction models and validate the models using confusion matrices, accuracy, precision, recall, etc.…”

Section: Prediction Model Of Education Fieldmentioning

confidence: 99%

An Intelligent Boosting and Decision-Tree-Regression-Based Score Prediction (BDTR-SP) Method in the Reform of Tertiary Education Teaching

Zhu

Liu

et al. 2023

Information

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The reform of tertiary education teaching promotes teachers to adjust timely teaching plans based on students’ learning feedback in order to improve teaching performance. Thefore, learning score prediction is a key issue in process of the reform of tertiary education teaching. With the development of information and management technologies, a lot of teaching data are generated as the scale of online and offline education expands. However, a teacher or educator does not have a comprehensive dataset in practice, which challenges his/her ability to predict the students’ learning performance from the individual’s viewpoint. How to overcome the drawbacks of small samples is an open issue. To this end, it is desirable that an effective artificial intelligent tool is designed to help teachers or educators predict students’ scores well. We propose a boosting and decision-tree-regression-based score prediction (BDTR-SP) model, which relies on an ensemble learning structure with base learners of decision tree regression (DTR) to improve the prediction accuracy. Experiments on small samples are conducted to examine the important features that affect students’ scores. The results show that the proposed model has advantages over its peer in terms of prediction correctness. Moreover, the predicted results are consistent with the actual facts implied in the original dataset. The proposed BDTR-SP method aids teachers and students to predict students’ performance in the on-going courses in order to adjust the teaching and learning strategies, plans and practices in advance, enhancing the teaching and learning quality. Therefore, the integration of information technology and artificial intelligence into teaching and learning practices is able to push forward the reform of tertiary education teaching.

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Comparison of Classification Performances of Mathematics Achievement at PISA 2012 with the Artificial Neural Network, Decision Trees and Discriminant Analysis

Cited by 5 publications

References 11 publications

Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students

Classification of Students’ Mathematical Literacy Score Using Educational Data Mining: PISA 2015 Turkey Application

An Intelligent Boosting and Decision-Tree-Regression-Based Score Prediction (BDTR-SP) Method in the Reform of Tertiary Education Teaching

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