Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms

Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms