PhD and MSc degrees holder in Computer Science from Paul Sabatier University, Toulouse III, France and BSc in Computer Science from Pau University, France. Posses more than 22 years teaching and academic experiences largely at Ajman University - former College of Information Technology whith 16 years acted as Head of the Information Systems Department within the College. Works as an active member of different committees with positive contribution at College and University levels. Published several papers in E-learning, Data Mining and cloud computing fields. Held the position of Acting Dean at the former College of Information Technology from July 2017 until January 2019. Currently, holding the position of Head of Information Technology Department within the College of Engineering and Information Technology.
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 an undergraduate information technology program gathered over the years, the authors demonstrate that the CPM attains accurate performance predictions compared to benchmark methods.
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.
In this paper, the recent applications and advances of Migrating Birds Optimization (MBO) algorithm are reviewed. The MBO originated from the V flight shape of the migrating birds in nature to reduce the air pressure and increase the swarm speed. Although MBO is relatively unknown, it has been successfully applied for a plethora of optimization problems in many research fields, such as scheduling, manufacturing, communication and networking, etc. This is due to its impressive characteristics, such as easy-to-use, simple, adaptable and flexible, and sound and complete. Therefore, the growth of MBO is exponentially increased. This review paper considers the changes in the MBO structure, the growth, the foundation and inspiration, the applications, and the limitations. The review ends up with theoretical conclusions about MBO and the possible future directions to cope with the current revolutions in the exponential age.
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.
Background Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates. Method In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students. Results Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect. Conclusion Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.