Prediction of Graduate Students for Master Degree based on Their Past Performance using Decision Tree in Weka Environment

Undavia, Jaimin N.; Dolia, Prashant M.; Shah, Nikhil P.

doi:10.5120/12930-9877

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…On the other hand, the use of Naïve Bayes Algorithms towards students achievement prediction was 84.3% accurate and this result is considered much better in comparison to the previously mentioned machine learning algorithms [9]. According to [22], his research study outlined the benefits of using data mining technique for predicting student's performance in universities. The results of his research study emphasized on the benefits to improve students' academic excellence through the use of classification algorithms such as decision trees.…”

Section: Machine Learning Methods For Predictive Analysismentioning

confidence: 98%

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Predictive Model for Students Admission Uncertainty Using Naïve Bayes Classifier and Kernel Density Estimation (KDE)

Matar

Matar²,

AlMalahmeh³

2022

Int. J. Emerg. Technol. Learn.

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Uncertainty of getting admission into universities / institutions is one of the global challenges in academic environment. The students are having good marks with high credential but not sure about getting their admission into universities / institutions. In this research study the researcher built a predictive model using Naïve Bayes Classifiers –machine learning algorithm to extract and analyze hidden pattern in students’ academic records and their credentials. The main objective of this research study is to reduce uncertainty for getting admission into universities / institutions on the basis of their previous credentials and some other essentials parameters. This research study presents a joint venture of Naïve Bayes Classification and Kernel Density Estimations (KDE) approach to predict student’s admission into universities or any higher institutions. The predictive model is built on training dataset of students’ examination score such as GPA, GRE, RANK and some other essential features that offered the admission with a predictor accuracy rate of 72% and has been experimentally verified. To improve the quality of accuracy of predictive model the researcher used the Shapiro-Walk Normality Test and Gaussian distribution on large datasets. The predictive model helps in reducing the admission uncertainty and enhances the universities decision making capabilities. The significance of this research study is to reduce human intervention for making decisions with respect to students’ admission into universities or any higher academic institutions, and it demonstrates that many universities and higher-level institutions could use this predictive model to improve their admission process without human intervention.

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Section: Machine Learning Methods For Predictive Analysismentioning

confidence: 98%

“…Implementation of Machine learning techniques and tools is a fast-growing predictive analysis with vast application in universities and higher education institutions [22]. The importance of machine learning algorithms is to find interesting and hidden pattern in volumes of data.…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for Students Admission Uncertainty Using Naïve Bayes Classifier and Kernel Density Estimation (KDE)

Matar

Matar²,

AlMalahmeh³

2022

Int. J. Emerg. Technol. Learn.

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“…The Naïve Bayes algorithms achieved 84.3% accuracy and predicted that it is much better than the other machine learning algorithms (Ksung and Prabhu, 2018). The researcher emphasized that data mining technique for predicting students in universities to improve the academic performance which was based on classification algorithms such as decision trees (Undavia, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Implementation of machine learning is fast growing predictive analysis tool with vast application in universities and higher education institutions (Undavia, 2013). The importance of machine learning algorithms is to find interesting and hidden pattern in volumes of data.…”

Section: Introductionmentioning

confidence: 99%

Predictive model for admission uncertainty in high education using Naïve Bayes classifier

Rawal

Lal

2023

JIBR

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Purpose The uncertainty of getting admission into universities/institutions is one of the global problems in an academic environment. The students are having good marks with highest credential, but they are not sure about getting their admission into universities/institutions. In this research study, the researcher builds a predictive model using Naïve Bayes classifiers – machine learning algorithm to extract and analyze hidden pattern in students’ academic records and their credentials. The main purpose of this research study is to reduce the uncertainty for getting admission into universities/institutions based on their previous credentials and some other essential parameters. Design/methodology/approach This research study presents a joint venture of Naïve Bayes Classification and Kernel Density Estimations (KDE) to predict the student’s admission into universities or any higher institutions. The researcher collected data from the Kaggle data sets based on grade point average (GPA), graduate record examinations (GRE) and RANK of universities which are essential to take admission in higher education. Findings The classification model is built on the training data set of students’ examination score such as GPA, GRE, RANK and some other essential features that offered the admission with a predictive accuracy rate 72% and has been experimentally verified. To improve the quality of accuracy, the researcher used the Shapiro–Walk Normality Test and Gaussian distribution on large data sets. Research limitations/implications The limitation of this research study is that the developed predictive model is not applicable for getting admission into all courses. The researcher used the limited data attributes such as GRE, GPA and RANK which does not define the admission into all possible courses. It is stated that it is applicable only for student’s admission into universities/institutions, and the researcher used only three attributes of admission parameters, namely, GRE, GPA and RANK. Practical implications The researcher used the Naïve Bayes classifiers and KDE machine learning algorithms to develop a predictive model which is more reliable and efficient to classify the admission category (Admitted/Not Admitted) into universities/institutions. During the research study, the researcher found that accuracy performance of the predictive Model 1 and that of predictive Model 2 are very close to each other, with predictive Model 1 having truly predictive and falsely predictive rate of 70.46% and 29.53%, respectively. Social implications Yes, it is having a significant contribution for society; students and parents can get prior information about the possibilities of admission in higher academic institutions and universities. Originality/value The classification model can reduce the admission uncertainty and enhance the university’s decision-making capabilities. The significance of this research study is to reduce human intervention for making decisions with respect to the student’s admission into universities or any higher academic institutions, and it demonstrates many universities and higher-level institutions could use this predictive model to improve their admission process without human intervention.

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“…Decision Tree technique has been found to be a very adequate technique to generate a comprehensive and precise analysis. Decision trees are used in data mining to study historical data and on the basis of the data analysis and its rules, one can predict the result [18]. SPSS combines the features of data mining activities which is a combination of Statistical analysis and database management.…”

Section: Introductionmentioning

confidence: 99%

A Decision Tree Approach for Predicting Students Academic Performance

Kolo¹,

Adepoju²,

Alhassan³

2015

IJEME

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This research is on the use of a decision tree approach for predicting students" academic performance. Education is the platform on which a society improves the quality of its citizens. To improve on the quality of education, there is a need to be able to predict academic performance of the students. The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square Automatic Interaction Detection (CHAID) in producing the decision tree structure. Factors such as the financial status of the students, motivation to learn, gender were discovered to affect the performance of the students. 66.8% of the students were predicted to have passed while 33.2% were predicted to fail. It is observed that much larger percentage of the students were likely to pass and there is also a higher likely of male students passing than female students.

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Prediction of Graduate Students for Master Degree based on Their Past Performance using Decision Tree in Weka Environment

Cited by 5 publications

References 7 publications

Predictive Model for Students Admission Uncertainty Using Naïve Bayes Classifier and Kernel Density Estimation (KDE)

Predictive Model for Students Admission Uncertainty Using Naïve Bayes Classifier and Kernel Density Estimation (KDE)

Predictive model for admission uncertainty in high education using Naïve Bayes classifier

A Decision Tree Approach for Predicting Students Academic Performance

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