Predicting and analyzing secondary education placement-test scores: A data mining approach

Şen, Baha; Uçar, Emine; Delen, Dursun

doi:10.1016/j.eswa.2012.02.112

Cited by 81 publications

(24 citation statements)

References 16 publications

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“…This leads to different analyses and different outcomes in similar studies, as it is the case in the prediction of academic achievement. However, it is seen that the lowest classification prediction and the highest network performance vary between 51.88% and 91.77%, respectively, in the findings of studies (such as linear regression, logistic regression and decision tree) on the comparison of the predictions and the statistical methods with respect to artificial neural networks related to education and training (Bahadır, 2013;Çırak, 2012;Gülçin, Çırak, & Çokluk, 2013;Demir, 2015;Guo, 2010;Ibrahim & Rusli, 2007;Karamouzis & Vrettos, 2008;Kardan, Sadeghi, Ghidary, & Sani, 2013;Moridis & Economides, 2009;Naser, Zaqout, Ghosh, Atallah, & Alajrami, 2015;Oancea, Dragoescu, & Ciucu, 2013;Oladokun, Adebanjo, & Charles-Owaba, 2008;Paliwal & Kumar, 2009;Romero, Ventura, & García, 2008;Rusli, Ibrahim, & Janor, 2008;Şen, Uçar, & Delen, 2012;Şengür, 2013;Şengür & Tekin, 2013;Tepehan, 2011;Tosun, 2007;Turhan, Kurt, & Engin, 2013;Vandamme, Meskens, & Superby, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…Some of the artificial neural network studies conducted in Turkey have been directed towards the prediction of standard tests such as TEOG (Şen, Uçar, & Delen, 2012), KPSS (Demir, 2015), and PISA (Tepehan, 2011) as well as students' course scores (Turhan, Kurt, & Engin, 2013) and general academic scores (Şengür, & Tekin, 2013). In general, two types of benchmarking studies on artificial neural networks are widely available on the basis of education and training.…”

Section: Discussionmentioning

confidence: 99%

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Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks

Akgün

Demir

2018

International Journal of Assessment Tools in Education

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In this study, it was aimed to predict elementary education teacher candidates' achievements in "Science and Technology Education I and II" courses by using artificial neural networks. It was also aimed to show the independent variables importance in the prediction. In the data set used in this study, variables of gender, type of education, field of study in high school and transcript information of 14 courses including end-of-term letter grades were collected. The fact that the artificial neural network performance in this study was R=0.84 for the Science and Technology Education I course, and R=0.84 for the Science and Technology Education II course shows that the network performance overlaps with the findings obtained from the related studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks

Akgün

Demir

2018

International Journal of Assessment Tools in Education

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“…Logistic regression has its roots in statistics and it is widely used for classification problems (see Delen, 2009;Şen et al, 2012). Traditionally, linear regression is used to investigate the relationship between continuous dependent variable and a set of independent variables.…”

Section: Logistic Regressionmentioning

confidence: 99%

Towards reliable prediction of academic performance of architecture students using data mining techniques

Aluko

Daniel

Oshodi

et al. 2018

JEDT

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PurposeIn recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities.Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. Design/methodology/approachThe input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. FindingsThe results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Research limitationsAlthough the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. Originality/valueThe developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.

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“…BahaSen [28] they developed models to predict secondary education placement test results, and using sensitivity analysis on those prediction models they find out the most important predictors. They proposed a decision tree algorithm for analysis of the success factor behind placement test that may help understand potentially improve the achievement.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Classification Through Machine Learning Technique: C4. 5 Algorithm based on Various Entropies

Sharma¹,

Agrawal²,

Sharma³

2013

IJCA

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Data mining is an interdisciplinary field of computer science and is referred to extracting or mining knowledge from large amounts of data. Classification is one of the data mining techniques that maps the data into the predefined classes and groups. It is used to predict group membership for data instances. There are many areas that adapt Data mining techniques such as medical, marketing, telecommunications, and stock, health care and so on. The C4.5 can be referred as the statistic Classifier. This algorithm uses gain radio for feature selection and to construct the decision tree. It handles both continuous and discrete features. C4.5 algorithm is widely used because of its quick classification and high precision. This paper proposed a C4.5 classifier based on the various entropies (Shannon Entropy, Havrda and Charvt entropy, Quadratic entropy) instance of Shannon entropy for classification. Experiment results show that the various entropy based approach is effective in achieving a high classification rate.

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Predicting and analyzing secondary education placement-test scores: A data mining approach

Cited by 81 publications

References 16 publications

Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks

Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks

Towards reliable prediction of academic performance of architecture students using data mining techniques

Classification Through Machine Learning Technique: C4. 5 Algorithm based on Various Entropies

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