Mahmoud Abou Naaj

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.

Plagiarism in programming assignments is a common and current challenge. However, insufficient studies have examined plagiarism in the Middle East region. Thus, this research surveyed 422 students from a middle eastern university. It primarily purported to assess the students’ perception of plagiarism in writing programming assignments. Additionally, this study reported the changes in students’ perceptions of plagiarism in programming assignments between 2018 and 2021, the extent of this dishonest behaviour, and the demographical factors that influence it. A comparative analysis of the data from the 2017–2018 and 2020–2021 surveys of students specialising in Information Technology-related programmes found that those in the latter survey considered plagiarism less acceptable. In addition, the female students and those with a Cumulative Grade Point Average (CGPA) higher than or equal to three also considered cheating and plagiarism behaviours in programming assignments to be less acceptable. Furthermore, these findings did not report a substantial perception variance related to student class standing or specialisation.

The Covid-19 pandemic constrained higher education institutions to switch to online teaching, which led to major changes in students’ learning behavior, affecting their overall performance. Thus, students’ academic performance needs to be meticulously monitored to help institutions identify students at risk of academic failure, preventing them from dropping out of the program or graduating late. This paper proposes a CGPA Predicting Model (CPM) that detects poor academic performance by predicting their graduation cumulative grade point average (CGPA). The proposed model uses a two-layer process that provides students with an estimated final CGPA, given their progress in second- and third-year courses. This work allows academic advisors to make suitable remedial arrangements to improve students’ academic performance. Through extensive simulations on a data set related to students registered in undergraduate information technology program gathered over the years, we demonstrate that the CPM attains accurate performance predictions compared to benchmark methods.