Predicting University Dropout trough Data Mining: A systematic Literature

Albán, Mayra; Mauricio, David; Systems, Ecuador Computer

doi:10.17485/ijst/2019/v12i4/139729

Cited by 64 publications

(60 citation statements)

References 2 publications

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“…Besides that, there are other factors that had been identified could result to attrition which is due to the ability of student to study, failed to maintain academic performance and adaptation to the course enrolled, lack of appropriate qualification for the course, professional opportunities and the course offered may not suitable with their capabilities [19,20]. Personal reasons such as poor self-esteem, depression, dissatisfactions with course enrolled or institution, felt isolated likes does not have a sense of belonging, lack of social support and health problems also can be the reason to attrition [21,22].…”

Section: A Factors Of Student Attritionmentioning

confidence: 99%

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Tarmizi¹,

Mutalib²,

Hamid³

et al. 2019

IJMECS

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Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students' information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE.

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Section: A Factors Of Student Attritionmentioning

confidence: 99%

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Tarmizi¹,

Mutalib²,

Hamid³

et al. 2019

IJMECS

View full text Add to dashboard Cite

show abstract

“…The outcomes recommended that all working models were effective for this context, but Decision Tree performed better comparatively. A systematic literature review was presented by Alban et al [9] considering the data mining aspects for predicting the dropout at university. They recognized 1,681 elemental related studies and selected 67 documents according to the established inclusion and exclusion criteria and also recognizing five major dimensions.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the Enrollment and Dropout of Students in the Post-Graduation Degree using Machine Learning Classifier

Biswas¹,

Majumder²,

Mia³

et al. 2019

IJITEE

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Nowadays, In Bangladesh, the dropout rate at post-graduation level or incompletion of the post-graduation degree is considered as a serious problem in the education sector. This work can be used to support for identifying the specific individuals as well as the institutional factors which may next lead to the enrollment or drop out at the post-graduation degree. The real dataset is used to accomplish this work. Here, seven classification algorithms namely Naïve Bayes, Multilayer Perceptron, Logistic, Locally Weighted Learning (LWL), Random Forest, Random Tree, and Part are applied in this context. A confusion matrix is calculated for each classification model. Then, we computed all the seven performance evaluation metrics (accuracy, sensitivity, precision, specificity, F1 score, FPR, and FNR). Each classifier's performances are analyzed and measured from the computed performance evaluation metrics. Naïve Bayes, LWL, and Part classifier perform better than all other working classifiers attaining 86.36% accuracy and on the contrary, Random Tree classifier performs worst achieving 74.24% accuracy. After further analyzing of the result based on performance evaluation metrics, it is observed that LWL classifier performed best in this context among all the classifiers.

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“…A review on the prediction of student dropout is done through data mining methods. It deals with the aspects considered for the prediction of student admission in the university [11]. The machine learning feature selection and extraction methods can be used for the prediction of any factor in different application can be learnt through this article [12]- [16].…”

Section: A Literature Surveymentioning

confidence: 99%

Feature Snatching and Performance Assessment for Connoting the Admittance Likelihood of student using Principal Component Reduction

Devi¹,

Mathew²,

Suguna³

2019

IJRTE

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Abstract: Recently, engineers are concentrating on designing an effective prediction model for finding the rate of student admission in order to raise the educational growth of the nation. The method to predict the student admission towards the higher education is a challenging task for any educational organization. There is a high visibility of crisis towards admission in the higher education. The admission rate of the student is the major risk to the educational society in the world. The student admission greatly affects the economic, social, academic, profit and cultural growth of the nation. The student admission rate also depends on the admission procedures and policies of the educational institutions. The chance of student admission also depends on the feedback given by all the stake holders of the educational sectors. The forecasting of the student admission is a major task for any educational institution to protect the profit and wealth of the organization. This paper attempts to analyze the performance of the student admission prediction by using machine learning dimensionality reduction algorithms. The Admission Predict dataset from Kaggle machine learning Repository is used for prediction analysis and the features are reduced by feature reduction methods. The prediction of the chance of Admit is achieved in four ways. Firstly, the correlation between each of the dataset attributes are found and depicted as a histogram. Secondly, the top most high correlated features are identified which are directly contributing to the prediction of chance of admit. Thirdly, the Admission Predict dataset is subjected to dimensionality reduction methods like principal component analysis (PCA), Sparse PCA, Incremental PCA , Kernel PCA and Mini Batch Sparse PCA. Fourth, the optimized dimensionality reduced dataset is then executed to analyze and compare the mean squared error, Mean Absolute Error and R2 Score of each method. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment.Experimental Result shows that the CGPA, GRE Score and TOEFL Score are highly correlated features in predicting the chance of admit. The execution of performance analysis shows that Incremental PCA have achieved the effective prediction of chance of admit with minimum MSE of 0.09, MAE of 0.24 and reasonable R2 Score of 0.26.

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Predicting University Dropout trough Data Mining: A systematic Literature

Cited by 64 publications

References 2 publications

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Predicting the Enrollment and Dropout of Students in the Post-Graduation Degree using Machine Learning Classifier

Feature Snatching and Performance Assessment for Connoting the Admittance Likelihood of student using Principal Component Reduction

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