An Intelligent Prediction System for Educational Data Mining Based on Ensemble and Filtering approaches

Ashraf, Mudasir; Zaman, Majid; Ahmed, Muheet

doi:10.1016/j.procs.2020.03.358

Cited by 75 publications

(39 citation statements)

References 14 publications

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“…A framework of an intelligent recommender system based on background factors was designed by Goga et al [14] to recommend necessary actions for improvement. Ashraf et al [15] also develop an intelligent prediction system based on ensemble…”

Section: Introductionmentioning

confidence: 99%

Predicting Students Graduate on Time Using C4.5 Algorithm

Yuliansyah

Imaniati

Wirasto

et al. 2021

JISEBI

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Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students’ on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students’ graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.

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Section: Introductionmentioning

confidence: 99%

Predicting Students Graduate on Time Using C4.5 Algorithm

Yuliansyah

Imaniati

Wirasto

et al. 2021

JISEBI

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“…Ashraf, et al (Ashraf et al 2020 ) proposed a prediction approach to evaluate academic student papers in educational data mining. They used boosting algorithm with a combined synthetic minority oversampling technique and J48 classifier to compare with Naïve Bayes algorithm.…”

Section: Related Workmentioning

confidence: 99%

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Tan

Lin

2021

Soft Comput

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Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

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“…The educational data mining technique was introduced to improve graduate students' performance in research (Ashraf et al, 2020). The purpose of their work was to solve the problem of students' low grades (Satı, 2018).…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Model-Based Approach for Performance Prediction of Students

Razaque¹,

Alajlan²

2020

Journal of Computer Science

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Predicting students' performance is one of the crucial issue for learning contexts, since it helps to develop alternative recommendation systems for academically weak students. Thus, several methods and practices have been applied for educational improvement. However, most of the existing methods do not determine the performance of the students. In this study, we have studied the execution of six machine learning models (Decision tree, Random Forest, Support Vector Machine, Logistic Regression, Ada Boost, Stochastic Gradient Descent) to analyze and evaluate the students' achievements. The performance is evaluated in term of accuracy, precision, sensitivity and f-measure. Among the selected models, the results validate that the efficiency of Stochastic Gradient Descent is better in training small dataset. In addition, it also produces the higher accuracy as compared with other models. This contribution aims to develop the best model which may derive the conclusion on students' academic achievement.

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An Intelligent Prediction System for Educational Data Mining Based on Ensemble and Filtering approaches

Cited by 75 publications

References 14 publications

Predicting Students Graduate on Time Using C4.5 Algorithm

Predicting Students Graduate on Time Using C4.5 Algorithm

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Supervised Machine Learning Model-Based Approach for Performance Prediction of Students

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