The field of sign language recognition and student trajectory modeling is witnessing significant advancements, driven by the development of innovative frameworks and models. Researchers are focusing on creating more accurate and efficient sign language recognition systems, with a particular emphasis on low-resource languages. Additionally, there is a growing interest in modeling student trajectories in higher education, with a focus on understanding the impact of various factors such as teacher strikes, inflation, and curriculum structure on student dropout rates. Noteworthy papers in this area include: A Leakage-Aware Data Layer For Student Analytics, which introduces a leakage-aware data layer for student trajectory analytics, and BdSL-SPOTER, which presents a transformer-based framework for Bengali Sign Language recognition with cultural adaptation. These studies demonstrate the potential of advanced modeling techniques to improve our understanding of complex educational phenomena and develop more effective support systems for students and sign language users.