Predicting Student Drop-Out Rates Using Data Mining Techniques: A Case Study

Pérez, Boris; Castellanos, Camilo; Correal, Darío

doi:10.1007/978-3-030-03023-0_10

Cited by 26 publications

(12 citation statements)

References 16 publications

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“…To estimate the probability of new university students failing throughout their learning process, AI applications can be employed as such (Wu, Chen, & Tsai, 2018). There have been several attempts (e.g., Abu Zohair, 2019;Hew, Hu, Qiao, & Tang, 2020;Pérez, Castellanos, & Correal, 2018) to predict student performance or dropout using algorithms in higher education research in order to help at-risk students by assuring their retention. For instance, to predict students' performance in a university course, Abu Zohair (2019) used clustering algorithms and a small dataset for training and model construction, establishing a reliable and accurate prediction model with a prediction accuracy of approximately 70%.…”

Section: Introductionmentioning

confidence: 99%

Precision education with statistical learning and deep learning: a case study in Taiwan

Tsai

Chen

Shiao

et al. 2020

Int J Educ Technol High Educ

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The low birth rate in Taiwan has led to a severe challenge for many universities to enroll a sufficient number of students. Consequently, a large number of students have been admitted to universities regardless of whether they have an aptitude for academic studies. Early diagnosis of students with a high dropout risk enables interventions to be provided early on, which can help these students to complete their studies, graduate, and enhance their future competitiveness in the workplace. Effective prelearning interventions are necessary, therefore students' learning backgrounds should be thoroughly examined. This study investigated how big data and artificial intelligence can be used to help universities to more precisely understand student backgrounds, according to which corresponding interventions can be provided. For this study, 3552 students from a university in Taiwan were sampled. A statistical learning method and a machine learning method based on deep neural networks were used to predict their probability of dropping out. The results revealed that student academic performance (regarding the dynamics of class ranking percentage), student loan applications, the number of absences from school, and the number of alerted subjects successfully predicted whether or not students would drop out of university with an accuracy rate of 68% when the statistical learning method was employed, and 77% for the deep learning method, in the case of giving first priority to the high sensitivity in predicting dropouts. However, when the specificity metric was preferred, then the two approaches both reached more than 80% accuracy rates. These results may enable the university to provide interventions to students for assisting course selection and enhancing their competencies based on their aptitudes, potentially reducing the dropout rate and facilitating adaptive learning, thereby achieving a win-win situation for both the university and the students. This research offers a feasible direction for using artificial intelligence applications on the basis of a university's institutional research database.

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Section: Introductionmentioning

confidence: 99%

Precision education with statistical learning and deep learning: a case study in Taiwan

Tsai

Chen

Shiao

et al. 2020

Int J Educ Technol High Educ

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“…P´erez et al [17] presented their initial results that prediction of attrition of students from a large dataset of Systems Engineering (SE) undergraduate students after six years of registration at a Colombian university, the dataset includes 762 students. In their study, they applied four algorithms which are Decision Tree, Random Forest, Naive Bayes and Logistic Regression.…”

Section: Related Workmentioning

confidence: 99%

The Most Efficient Classifiers for the Students’ Academic Dataset

Al-Fairouz¹,

Abdullah²

2020

IJACSA

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Educational institutions contain a vast collection of data accumulated for years, so it is difficult to use this data to solve problems related to the progress of the educational process and also contribute to achieving quality. For this reason, the use of data mining techniques helps to extract hidden knowledge that helps in making the decisions necessary to develop education and achieve quality requirements. The data of this study obtained from the College of Business and Economics at Qassim University. Three of the classifiers were compared in this study Decision Tree, Random Forest and Naïve Bayes. The results showed that Random Forest outperforms other algorithms with 71.5% of Precision, 71.2% F1-score, and also it got 71.3% of Recall and Classification Accuracy (CA). This study helps reduce failure by providing an academic advisor to students who have weaknesses in achieving a high-Grade Point Average (GPA). It also helps in developing the educational process by discovering and overcoming weaknesses.

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“…The work showed that the accuracy of the naïve bayes classification technique is more prominent than another algorithm. P´erez et al [14], illustrated the results of an educational data analytics case study concentrated on the dropout detection of systems engineering undergraduate students after six years of enrollment. Primary data is prolonged and improved using an engineering process which is known as a feature engineering process.…”

Section: Literature Reviewmentioning

confidence: 99%

Registration Status Prediction of Students using Machine Learning in the Context of Private University of Bangladesh

Mia*,

Biswas,

Sattar

et al. 2019

IJITEE

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Bangladesh is a densely populated country where a large portion of citizens is living under poverty. In Bangladesh, a significant portion of higher education is accomplished at private universities. In this twenty-first century, these students of higher education are highly mobile and different from earlier generations. Thus, retaining existing students has become a great challenge for many private universities in Bangladesh. Early prediction of the total number of registered students in a semester can help in this regard. This can have a direct impact on a private university in terms of budget, marketing strategy, and sustainability. In this paper, we have predicted the number of registered students in a semester in the context of a private university by following several machine learning approaches. We have applied seven prominent classifiers, namely SVM, Naive Bayes, Logistic, JRip, J48, Multilayer Perceptron, and Random Forest on a data set of more than a thousand students of a private university in Bangladesh, where each record contains five attributes. First, all data are preprocessed. Then preprocessed data are separated into the training and testing set. Then, all these classifiers are trained and tested. Since a suitable classifier is required to solve the problem, the performances of all seven classifiers need to be thoroughly assessed. So, we have computed six performance metrics, i.e. accuracy, sensitivity, specificity, precision, false positive rate (FPR) and false negative rate (FNR) for each of the seven classifiers and compare them. We have found that SVM outperforms all other classifiers achieving 85.76% accuracy, whereas Random Forest achieved the lowest accuracy which is 79.65%.

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Predicting Student Drop-Out Rates Using Data Mining Techniques: A Case Study

Cited by 26 publications

References 16 publications

Precision education with statistical learning and deep learning: a case study in Taiwan

Precision education with statistical learning and deep learning: a case study in Taiwan

The Most Efficient Classifiers for the Students’ Academic Dataset

Registration Status Prediction of Students using Machine Learning in the Context of Private University of Bangladesh

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