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24 Pt. 2 J. Legal Ethical & Regul. Isses 1 (2021)
Using E-Learning Factors to Predict Student Performance in the Practice of Precision Education

handle is hein.journals/jnlolletl2424 and id is 2389 raw text is: Journal of Legal, Ethical and Regulatory Issues USING E-LEARNING FACTORS TO PREDICT STUDENT PERFORMANCE IN THE PRACTICE OF PRECISION EDUCATION Abdulla Alsharhan, The British University in Dubai Said A. Salloum, University of Sharjah Ahmad Aburayya, Dubai Health Authority ABSTRACT The emergence of Covid-19 has accelerated digital transformation, while simultaneously disrupting traditional education. The implementation of distance education practices produced a massive amount of data generated by the deployed learning management systems. Educational data mining tools using machine learning methods can produce thorough student-level insights into what has become known as precision education. This study aims to investigate a convenient approach to analyze a data set of 480 students in the Middle East using three supervised machine learning methods (artificial neural networks, decision trees, and Naive Bayes) to predict overall performance using SPSS. The findings indicate that the naive Bayes algorithm achieved the highest accuracy of 89.85%, while the artificial neural networks algorithm achieved the lowest variance, with a standard deviation of 2.37. Besides, there are more valuable insights beyond accuracy that other Machine learning models can provide in the SPSS environment, such as visual representation and normalized importance of independent variables. Moreover, in the context of missing student data, the data set was evaluated if e-learning parameters alone can predict student performance. The findings suggest that e-learning parameters alone can predict student performance with an average accuracy of 84.49%. This study contributes to limit grade inflation in the age of online learning due to educational malpractices. Keywords: Educational Data Mining, Academic Performance, Prediction Model, E-Learning, Precision Education, Performance Education, Artificial Neural Network, Decision Tree, Naive Bayes, Machine Learning, SPSS, Educational Technologies, Human-Computer Interaction INTRODUCTION The COVID-19 pandemic has accelerated digital transformation in a novel manner (Taryam et al., 2020; Ahmad et al., 2021; Al-Maroof et al., 2021; Taryam et al., 2021). Education is among the areas that the pandemic has heavily impacted (Capuyan et al., 2021). The learning process was disrupted, cutting off 1.5 million students from physical attendance (Kupchina, 2021). Consequently, new blended learning methods are emerging. e-learning and distance learning are becoming a normality. At the same time, the learning analytics generated from e-learning platforms can be harnessed in many ways (Salloum et al., 2017; Alshurideh et al., 2019; Al Kurdi et al., 2020). Educational Data Mining (EDM) is commonly used to search for and detect patterns to improve educational performance. The new EDM approaches that are emerging offer more personalized education in what is known as precision education This refers to obtaining the right intervention in place for the right person for the right reason (Cook, Kilgus & Bums 2018). The precision education approach was borrowed from precision medicine, and their methods are used in EDM to diagnose, predict, treat, and prevent undesirable educational behavior (Yang et al., 2021). In order to provide the correct intervention, we need to first identify and classify specific patterns in students either in highly performing students or those who need assistance. Making Legal Ethics and Responsibilities Volume 24, Special Issue 6, 2021 1544-0044-24-S6-45