Student academic performance and social behavior predictor using data mining techniques

Athani, Suhas; Kodli, Sharath A; Banavasi, Mayur N; Hiremath, P. S.

doi:10.1109/ccaa.2017.8229794

Cited by 23 publications

(13 citation statements)

References 7 publications

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“…Naïve Bayes classifier (NBC) is a simple supervised classification method that depends on a presumption of the class conditional independence. NBC is assumed that all attributes provided in a dataset are independent based on the Bayes rule of conditional probability [20].…”

Section: Experiments Designmentioning

confidence: 99%

Prediction of student’s performance through educational data mining techniques

Salih

Khalaf

2021

IJEECS

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Many educators have worried about the failures of students through academic education. Thus, a variety of predictions have been applied to general information including culture, social, and economic information which wasn’t related to student performance. We have gathered an actual dataset from three years of academic stages of Mustansiriyah University in Iraq. The dataset consists of academic information without any socioeconomic data, it includes forty-four undergraduate students with thirteen attributes. We have proposed a model that explains the correlation between two main subjects which are, mathematics, and control systems. This study aimed to identify student failure of the control systems subject in the third year depending on the academic features of the mathematics subjects in the first and second years. Three algorithms were applied to the dataset including Naïve Bayes, support vector machine, and multilayer perceptron. Since the dataset was imbalanced, this leads to appear overfitting problem in the results so the synthetic minority oversampling technique was utilized to solve this problem. Our results show that the support vector machine algorithm proves an efficient classification after applied synthetic minority oversampling technique. The accuracy of the classifiers was measured from the confusion matrix using the Waikato environment for knowledge analysis (WEKA) tool and its related metrics.

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Section: Experiments Designmentioning

confidence: 99%

Prediction of student’s performance through educational data mining techniques

Salih

Khalaf

2021

IJEECS

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“…The study [26] by Athani et al in 2017 aimed to enhance the behavior of secondary school students using techniques of data mining. Naïve Bayesian classifier is implemented to predict the behavior of students to create the prediction.…”

Section: B Detecting Undesirable Student Bahaviorsmentioning

confidence: 99%

Educational Data Mining Applications and Techniques

Alshareef¹,

Alhakami²,

Alsubait³

et al. 2020

IJACSA

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Educational data mining (EDM) uses data mining techniques to analyze huge amounts of student data in the educational environments. The main purpose of EDM is to analyze and solve educational issues and, consequently, improve educational processes. With the emergence of EDM applications in the educational environments, several techniques have been identified to implement these applications. This paper reviews the relevant studies in EDM including datasets and techniques used in those studies and identifies the most effective techniques. The most prevalent applications include predicting student performance, detecting undesirable student behaviors, grouping students and student modeling. These applications aim to help decision makers in the educational institutions to understand student situations, improve students' performance, identify learning priorities for different groups of students and develop learning process. The prediction accuracy is selected as the evaluation criteria for the effectiveness of educational data mining techniques. The results show that Bayesian Network and Random Forest are the most effective techniques for predicting student performance, Social Network Analysis is the best technique for detecting undesirable student behaviors, Clustering and Social Network Analysis are the most effective techniques for grouping students and student modeling, respectively. This study recommends conducting more comprehensive and extended studies to evaluate the effectiveness of EDM techniques with an extended evaluation criteria.

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“…To sum up, the application of data mining nowadays is widely prevalent in the education system as it is able to obtain meaningful information from meaningless data. It also is very useful to analyze students' performance, forecast students' graduation time and other related issues [23,24]. The accuracy of data mining can be improved as well as performing a deeper analysis of the mined data with the use of machine learning algorithms.…”

Section: Fig4 Educational Data Mining Data Phases Modelmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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Student academic performance and social behavior predictor using data mining techniques

Cited by 23 publications

References 7 publications

Prediction of student’s performance through educational data mining techniques

Prediction of student’s performance through educational data mining techniques

Educational Data Mining Applications and Techniques

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

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