سید فخرالدین نوربهبهانی

The digital era has introduced mental health challenges, especially for youth. Despite increasing awareness, comprehensive analyses of these challenges remain limited. This study collects and examines the prevalence of 15 key mental health challenges related to digital engagement, based on a sample of 555 participants. The prevalence of these challenges varied, with pressures related to parenting, hoarding, and inappropriate content being the most common, affecting 60.13%, 52.76%, and 45.39% of the participants, respectively. The research also highlights gender and age differences, noting that males report higher levels of issues like FOMO and Nomophobia compared to females. Adults (18+) face more severe challenges, such as memory decline, while younger individuals report fewer problems. Correlation analysis revealed significant relationships between several mental health challenges, such as Nomophobia and TAD (r = 0.68) and FOMO and TAD (r = 0.50), indicating that individuals experiencing one challenge are likely to face others. A decision tree analysis was used to predict mental health challenges by examining the relationships between different mental health conditions, uncovering specific patterns and rules associated with the occurrence of these challenges. Additionally, cluster analysis in this study identified distinct population segments, with 21% of individuals falling into a cluster that experiences severe mental health challenges. The findings suggest that a significant portion of the population is at risk for severe mental health issues, highlighting the need for targeted interventions.

Online learning programs have gained significant popularity in recent years. However, despite their widespread adoption, completion and success rates for online courses are notably lower than those for traditional in-person education. If students' final academic performance could be predicted early by analyzing their behavior within the virtual learning environment, timely alerts could be issued, and targeted interventions could be recommended to prevent underperformance and course abandonment. Previous studies have predicted academic performance using various features, such as demographic data, academic history, in-term exam results, and assignment assessments. However, many online learning platforms do not provide access to such data, rendering these methods ineffective. This study focuses on the early prediction of students' academic performance by extracting novel behavioral features based on their interactions with the online learning platform. To develop robust predictive models, we utilize an integrated approach combining multiple feature selection methods to extract the most informative interaction patterns, followed by application of advanced machine learning algorithms including ensemble learning techniques and artificial neural networks (ANNs). The evaluation results demonstrate that our proposed approach can predict students' final academic performance with an accuracy of 90.62%, using only data collected during the first third of the online course.

Although there are several development processes for creating entertainment games, serious games, and gamified solutions, there is a need for a new consolidated process capable of developing different types of computer games and digital gamified solutions. This study covers the topic of computer games and gamified solutions design and development processes. First, processes of computer entertainment game production are introduced and investigated. Second, the paper describes gamification and serious game development processes. Finally, this study presents a new process called (DG)2DP to develop entertainment and serious computer games as well as digital gamified solutions. Two methods have been employed to evaluate the proposed game development method. First, the proposed process is assessed by 42 game development practitioners through an 8-factors questionnaire. Second, 16 experts that have applied the process are requested to rate the extent to which they would recommend the (DG)2DP to other game developers. The evaluation results show a 28.39% higher score for (DG)²DP than other processes. Furthermore, in comparison with other development processes, 81.25% of respondents agreed and strongly agreed to recommend the (DG)2DP to other game developers.