Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach

• Published in: Education sciences 2023-03, Vol.13 (3), p.313
• Main Authors: Abou Naaj, Mahmoud, Mehdi, Riyadh, Mohamed, Elfadil A., Nachouki, Mirna
• Format: Article
• Language: English
• Subjects: Academic achievement; Accuracy; Analysis; Classification; COVID-19; Data mining; Digitization; Education; educational data mining; Educational Technology; Forecasts and trends; Fuzzy sets; fuzzy systems; Higher education; Machine learning; Mathematical functions; Medical research; Methods; Neural networks; neuro-fuzzy systems; Pandemics; prediction; student performance; Students; Support vector machines; Teaching Methods; Variables
• ISSN: 2227-7102; 2227-7102
• DOI: 10.3390/educsci13030313

Predicting students’ academic performance and the factors that significantly influence it can improve students’ completion and graduation rates, as well as reduce attrition rates. In this study, we examine the factors influencing student academic achievement. A fuzzy-neural approach is adopted to build a model that predicts and explains variations in course grades among students, based on course category, student course attendance rate, gender, high-school grade, school type, grade point average (GPA), and course delivery mode as input predictors. The neuro-fuzzy system was used because of its ability to implicitly capture the functional form between the dependent variable and input predictors. Our results indicate that the most significant predictors of course grades are student GPA, followed by course category. Using sensitivity analysis, student attendance was determined to be the most significant factor explaining the variations in course grades, followed by GPA, with course delivery mode ranked third. Our findings also indicate that a hybrid course delivery mode has positively impacted course grades as opposed to online or face-to-face course delivery alone.