Predicting students performance in final examination using linear regression and multilayer perceptron

Widyahastuti, Febrianti; Tjhin, Viany Utami

doi:10.1109/hsi.2017.8005026

Cited by 32 publications

(11 citation statements)

References 14 publications

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“…High-level categories of learning analytics models range from statistical techniques, educational data mining methods to advanced machine learning models [6], [7], [13]. Specific examples of prominent algorithms predicting future academic achievement of students include regression models [39], decision trees [17], collaborative filtering [40], support vector machine [29], and artificial neural networks [8]. However, most of these techniques have been used separately (i.e., as single models), focusing mainly on supervised learning to predict student performance [13], [29].…”

Section: B Approaches To Predicting Student Performancementioning

confidence: 99%

Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Alshanqiti

Namoun

2020

IEEE Access

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Understanding, modeling, and predicting student performance in higher education poses significant challenges concerning the design of accurate and robust diagnostic models. While numerous studies attempted to develop intelligent classifiers for anticipating student achievement, they overlooked the importance of identifying the key factors that lead to the achieved performance. Such identification is essential to empower program leaders to recognize the strengths and weaknesses of their academic programs, and thereby take the necessary corrective interventions to ameliorate student achievements. To this end, our paper contributes, firstly, a hybrid regression model that optimizes the prediction accuracy of student academic performance, measured as future grades in different courses, and, secondly, an optimized multi-label classifier that predicts the qualitative values for the influence of various factors associated with the obtained student performance. The prediction of student performance is produced by combining three dynamically weighted techniques, namely collaborative filtering, fuzzy set rules, and Lasso linear regression. However, the multi-label prediction of the influential factors is generated using an optimized self-organizing map. We empirically investigate and demonstrate the effectiveness of our entire approach on seven publicly available and varying datasets. The experimental results show considerable improvements compared to single baseline models (e.g. linear regression, matrix factorization), demonstrating the practicality of the proposed approach in pinpointing multiple factors impacting student performance. As future works, this research emphasizes the need to predict the student attainment of learning outcomes.

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Section: B Approaches To Predicting Student Performancementioning

confidence: 99%

Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Alshanqiti

Namoun

2020

IEEE Access

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“…Over recent years, there has been a significant growth of research published in predicting student performance, focusing on course drop-out/ retention using the technique of classification (supervised learning). These researches concentrate on predicting final grade or Cumulative Grade Point Average (CGPA) of students by utilizing classifier algorithm [10]- [13], predicting student's performance in Massive Online Open Courses (MOOC) environment [14], and predicting students at risk of not graduating high school on time [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Drop-Out Prediction in Higher Education Among B40 Students

Sani¹,

Nafuri²,

Othman³

et al. 2020

IJACSA

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Malaysia citizens are categorized into three different income groups which are the Top 20 Percent (T20), Middle 40 Percent (M40), and Bottom 40 Percent (B40). One of the focus areas in the Eleventh Malaysia Plan (11MP) is to elevate the B40 household group towards the middle-income society. In 2018, it was estimated that 4.1 million households belong to this group. The government of Malaysia has widened access to higher education for the B40 group in an effort to reduce the gaps in socioeconomics and to improve their living standards. Statistical data shows that since 2013, a yearly intake of students in bachelor's degree programs in Malaysia's public universities amounts to more than 85,000. Despite this huge number of enrolments, not all were able to graduate, including students from low-income family background. Data mining approach with machine learning techniques has been widely used effectively and accurately to predict students at risk of dropping out in general education. However, machine learning related works on student attrition in Malaysia's higher education is generally lacking. Therefore, in this research, three machine learning models were developed using Decision Tree, Random Forest and Artificial Neural Network algorithm in order to classify attrition among B40 students in bachelor's degree programs in Malaysia's public universities. Comparative performance analysis between the three models indicates that the Random Forest model is the best model in predicting student attrition in this study. Random Forest model outperforms the other two models in terms of accuracy, precision, recall and Fmeasure with the value of 95.93%, 97.10%, 81.26% and 88.50%, respectively. Nevertheless, there is a statistically significant difference in performance between the Random Forest model and Decision Tree model but no statistically significant difference between Random Forest models and Artificial Neural Network model.

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“…Hence, in higher learning institutions, student's performance is an important part to be focus by the management of the university. Ability to predict the student performance using data mining (DM) has received much attention [1][2][3][4][5][6][7][8][9][10]. Though predicting the student's performance is a complex task due to the increase in the number of data available relating to student's academic results in higher learning institution, data mining application can help the academic management systems to investigate and identify group of excellent students and group of dropped out students from the university.…”

Section: Introductionmentioning

confidence: 99%

Supervised data mining approach for predicting student performance

Yaacob

Nasir

Sobri

2019

IJEECS

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<span>Data mining approach has been successfully implemented in higher education and emerge as an interesting area in educational data mining research. The approach is intended for identification and extraction of new and potentially valuable knowledge from the data. Predictive model developed using supervised data mining approach can derive conclusion on students' academic success. The ability to predict student’s performance can be beneficial for innovation in modern educational systems. The main objective of this paper is to develop predictive models using classification algorithm to predict student’s performance at selected university in Malaysia. The prediction model developed can be used to identify the most important attributes in the data. Several predictive modelling techniques of K-Nearest Neighbor, Naïve Bayes, Decision Tree and Logistic Regression Model models were used to predict student’s performance whether excellent or non-excellent. Based on accuracy measure, precision, recall and ROC curve, results show that the Naïve Bayes outperform other classification algorithm. The Naïve Bayes reveals that the most significant factors contributing to prediction of excellent students is when the student scores A+ and A in Multivariate Analysis; A+, A and A- in SAS Programming and A, A- and B+ in ITS 472.</span>

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Predicting students performance in final examination using linear regression and multilayer perceptron

Cited by 32 publications

References 14 publications

Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Drop-Out Prediction in Higher Education Among B40 Students

Supervised data mining approach for predicting student performance

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