Using Artificial Intelligence to Predict Students’ Academic Performance in Blended Learning

Abstract

University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. This type of education is significant because it ensures that all students receive the required learning. The statistical evaluations are limited in providing good predictions of the university’s e-learning quality. That is forcing many universities to go to online and blended learning environments. This paper presents an approach of statistical analysis to identify the most common factors that affect the students’ performance and then use artificial neural networks (ANNs) to predict students’ performance within the blended learning environment of Saudi Electronic University (SEU). Accordingly, this dissertation generated a dataset from SEU’s Blackboard learning management system. The student’s performance can be tested using a set of factors: the studying (face-to-face or virtual), percentage of attending live lectures, midterm exam scores, and percentage of solved assessments. The results showed that the four factors are responsible for academic performance. After that, we proposed a new ANN model to predict the students’ performance depending on the four factors. Firefly Algorithm (FFA) was used for training the ANNs. The proposed model’s performance will be evaluated through different statistical tests, such as error functions, statistical hypothesis tests, and ANOVA tests.

Towards Independent Students’ Activities, Online Environment and Learning Performance: An Investigation through Synthetic Data and Artificial Neural Networks

“…According to Hamadneh et al [20], the four main factors influencing the students' learning performance in a blended learning environment are attendance, face-to-face or virtual studying, scores on the midterm exam, and percentage of performed assessments. Additionally, they propose an ANN-based model to predict the learning performance of students, decrease students' failure, and improve the learning process overall.…”

A Practical Model of the Application of Information Technology in Various Fields of Online Education during the COVID-19 Pandemic: Mechanical Engineering, Traffic, Informatics and Statistics, Accounting and Auditing

“…Hamadneh et al [7] proposed the combination of statistical analysis to identify factors that affect students' performance and ANNs to predict their academic performance in the blended learning environment. The four factors: mid-exams, assignments, attendances, and virtual/face are considered to test the effects on students' performance.…”

“…Motivated by the initial successes in transferring knowledge through an Internet model based on a website, teachers developed the idea of improving the quality of the adopted knowledge led by the experience of earlier research related to the creating of various solutions through ANN (Artificial Neural Network) [2][3][4][5][6][7][8][9][10]. This experience was avant-garde, but it provided great opportunities, and, due to the positive feedback in the educational community, an increasing number of professors from various universities and faculties commenced to apply this novel solution to their subjects.…”

Evaluation of Machine Learning Methods for Prediction Student Performance

“…The goals of learning analytics include accurate evaluation, clear understanding of educational challenges, and the choice and timing of efficient interventions, to name a few. Data analytics and several sectors connected to it, including such database-based information retrieval assessment of following framework prediction, text analysis and text mining, neural network-based, and or artificial intelligence techniques, have invaded the nerves of the education systems [3]. The main objective is to use technology to automate conventional methods of instruction and learning.…”