Identifying the Classification Performances of Educational Data Mining Methods: A Case Study for TIMSS

doi:10.12738/estp.2017.5.0634

Cited by 17 publications

(21 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The factors given above were also found important variables in earlier researches. The "Student Confident" factor was proven to be an important element of success in various studies (Liu & Meng, 2010;Hammouri, 2010;Askin & Gokalp, 2013;Kılıç-Depren, Askin, & Öz, 2017). Topçu, Erbilgin, and Arıkan (2016) asserted in their study that those students who have ample access to educational resources achieve success which is corroborated by other studies too making "Home Educational Resources" as another important element.…”

Section: Discussionmentioning

confidence: 80%

“…In many existing studies, performed algorithms within EDM suggesting that LR and SVM-POLY were most appropriate for classifying student success. LR was considered by many scholars to be the best modelling technique among the various data mining methods to ascertain academic performance of students (Schreiber, 2002;Kılıç-Depren, Askin, & Öz, 2017). Another research by Delen (2010) employed ANN, SVM, C5 decision tree algorithm and LR to develop analytical models to forecast attrition among students and established that SVM gave the best results in this area (Delen, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…This study conducted by Topçu, Erbilgin, and Arıkan in 2016 also reviewed the consequences their findings could have on education. An analysis was undertaken to find the best possible algorithms to help classify eighth grade students from Turkey by applying NB, DT, ANN, and LR to TIMSS 2011 data (Kılıç-Depren, Askin, & Öz, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Finding the Best Algorithms and Effective Factors in Classification of Turkish Science Student Success

Filiz

Öz

2019

JBSE

View full text Add to dashboard Cite

Educational Data Mining (EDM) is an important tool in the field of classification of educational data that helps researchers and education planners analyse and model available educational data for specific needs such as developing educational strategies. Trends International Mathematics and Science Study (TIMSS) which is a notable study in educational area was used in this research. EDM methodology was applied to the results of TIMSS 2015 that presents data culled from eighth grade students from Turkey. The main purposes are to find the algorithms that are most appropriate for classifying the successes of students, especially in science subjects, and ascertaining the factors that lead to this success. It was found that logistic regression and support vector machines – poly kernel are the most suitable algorithms. A diverse set of features obtained by feature selection methods are “Computer Tablet Shared”, “Extra Lessons Last 12 Month”, “Extra Lessons How Many Month”, “How Far in Education Do You Expect to Go”, “Home Educational Resources”, and “Student Confident in Science” and these features are the most effective features in science success. Keywords: classification algorithms, educational data mining, eighth grade, science success, TIMSS 2015.

show abstract

Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Finding the Best Algorithms and Effective Factors in Classification of Turkish Science Student Success

Filiz

Öz

2019

JBSE

View full text Add to dashboard Cite

show abstract

“…In the literature, empirical research have been made to specify the underlying factors of students' achievement (Contini, Di Tommaso, & Mendolia, 2017;Sheldrake, Mujtaba, & Reiss, 2017;Kılıç Depren, Aşkın, & Öz, 2017;Kılıç Depren, 2018). Zhang, Khan, and Tahirsylaj (2015) have used regression methods to measure the factors affecting students' performance using the dataset of PISA 2009 participating countries.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Factors Affecting Students’ Science Achievement Level in Turkey and Singapore: An Application of Quantile Regression Mixture Model

Depren

2020

JBSE

View full text Add to dashboard Cite

In the last decade, the usage of advanced statistical models is growing rapidly in many different disciplines. However, the Quantile Regression Mixture Model (QRMIX), which is a developed approach of the Finite Mixture Model (FMM), is an applicable new method in the educational literature. The aim of the proposed study was to determine factors affecting students' science achievement using the QRMIX approach. To reach this aim, data of the Programme for International Student Assessment (PISA) survey, which has been conducted by the Organization Economic for Co-Operation and Development (OECD) every 3 years, was used. Dataset used in the research is composed of 6,115 students from Singapore, which is the top-performer country among the participant countries, and 5,895 students from Turkey. The results showed that the factors affecting students' science achievement and its importance on the achievement differentiated according to the achievement levels of the students. In conclusion, it was revealed that Turkish students with the lowest science achievement level should be supported with home possessions, perceived feedback, and environmental awareness and Singaporean students with the lowest achievement level should be supported with perceived feedback, enjoyment of science, and epistemological beliefs.

show abstract

“…Despite the potential that these statistical techniques may hold, their use in the establishment of performance prediction models in compulsory education is sporadic (Hung et al, 2012; Oskouei and Askari, 2014; S̨ara et al, 2015), and their use for the exploration of large-scale assessments is extremely limited (Liu and Ruiz, 2008; Liu and Whitford, 2011; Kılıç et al, 2017; Asensio et al, 2018). A significant presence of EDM can, however, be observed in the study of performance in university education (Guruler et al, 2010; Kasih et al, 2013; Romero et al, 2013; Kirby and Dempster, 2014; Akçapinar et al, 2015; Tan and Shao, 2015; Asif et al, 2017; Casey and Azcona, 2017; Costa et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach

Martínez‐Abad

2019

Front. Psychol.

View full text Add to dashboard Cite

The study of school effectiveness and the identification of factors associated with it are growing fields of research in the education sciences. Moreover, from the perspective of data mining, great progress has been made in the development of algorithms for the modeling and identification of non-trivial information from massive databases. This work, which falls within this context, proposes an innovative approach for the identification and characterization of educational and organizational factors associated with high school effectiveness. Under a perspective of basic research, our aim is to study the suitability of decision trees, techniques inherent to data mining, to establish predictive models for school effectiveness. Based on the available Spanish sample of the PISA 2015 assessment, an indicator of the school effectiveness was obtained from the application of multilevel models with predictor variables of a contextual nature. After selecting high- and low-effectiveness schools in this first phase, the second phase of the study was carried out and consisted of the application of decision trees to identify school, teacher, and student factors associated with high and low effectiveness. The C4.5 algorithm was calculated and, as a result, we obtained 120 different decision trees based on five determining factors (database used; stratification in the initial selection of schools; significance of the predictor variables of the models; use of items and/or scales; and use of the training or validated samples). The results show that the use of this kind of technique could be appropriate if mainly used with correctly pre-processed data that include the combined information available from all educational agents. This study represents a major breakthrough in the study of the factors associated with school effectiveness from a quantitative approach, since it proposes and provides a simple and appropriate procedure for modeling and establishing patterns. In doing so, it contributes to the development of knowledge in the field of school effectiveness that can help in educational decision-making.

show abstract

Identifying the Classification Performances of Educational Data Mining Methods: A Case Study for TIMSS

Cited by 17 publications

References 29 publications

Finding the Best Algorithms and Effective Factors in Classification of Turkish Science Student Success

Finding the Best Algorithms and Effective Factors in Classification of Turkish Science Student Success

Determination of the Factors Affecting Students’ Science Achievement Level in Turkey and Singapore: An Application of Quantile Regression Mixture Model

Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach

Contact Info

Product

Resources

About