Blended learning combines multiple delivery media that are designed to complement each other and promote learning and application-learned behavior (Smith & Dillon, 1999). This article reports on a study conducted in the College of Information Technology to evaluate levels of student' satisfaction with blended learning. The particular blend of learning modalities used at the college combines an equal balance of traditional face-to-face and videoconference learning, complemented with the use of a learning management system (Moodle). Recently, discussions of blended learning have begun to examine the benefits derived from learning situations characterized by face-to-face education and mixed modalities of instruction. Regardless of comparisons made by researchers and developers, those studying blended learning have agreed that student satisfaction is a baseline requirement for successful implementation. Student satisfaction is considered an important factor in measuring the quality of blended learning. It results from a combination of factors. In this study a model is proposed by the aggregation of these factors into five groups: instructor, technology, class management, interaction, and instruction. The purpose of this study is to develop and validate an instrument that can be used to measure perceived student satisfaction with blended learning and explore whether satisfaction differs according to gender. The results indicate that the Student Satisfaction Survey Forms (SSSF) used were a valid measure of student satisfaction. They also show that students were satisfied with all components, although the level of satisfaction varied according to gender.

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.

The COVID-19 pandemic has forced most universities worldwide to convert to distance education to ensure the educational process remains uninterrupted. The COVID-19 pandemic-related confinement orders have led students to be more engaged with online games. However, for a minority of students, excessive playing can become problematic and addictive. Few studies investigated the long-term effect of COVID-19 on game addiction among university students. The present study investigates the changes in online game addiction rates between May 2021 and May 2020 and aims at determining the impact of playing online games on students' academic performance. It also examines the demographic factors associated with video game addiction. A sample (n= 418) of students from one private university in UAE was randomly selected, and data were analyzed. The study has determined a reduction in online game addiction levels in the second year of pandemic compared with the first year. Gender and academic level were considered the most predominant features expressively related to online games addiction. It has also been found that digital game addiction is positively associated with academic performance.

Cyber ethics are essential components of information technology. The COVID-19 situation has brought unprecedented challenges to traditional higher education institutions, especially for students using their electronic devices in all their learning activities. This study focused on cyber ethics perceptions among university undergraduates’ students during COVID-19 conditions. It aims to analyze the extent to which distinct attributes, such as gender, education level, grades, or Cumulative Grade Point Average (CGPA), and major are related to cyber ethics awareness. An online survey was conducted on a sample of 322 undergraduates studying Information Technology majors and other majors to assess university students' cyber ethics awareness levels at a University in the UAE. The results show that, in general, respondents were aware of cyber ethics. In particular, gender and education level were found to directly affect cyber ethics awareness, while major and grades have no statistically significant effect.

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.