Top 2% of scientists by Stanford University,Ranking and Accreditation Expert (NBA, ABET, NIRF,NAAC, QS),Speaker(300+talks),300 +high-impact SCI papers ,200 Books,AI Expert.
Deep Learning to Predict & Prevent University Dropouts: The Future of AI in Education Student dropouts remain a critical challenge for universities worldwide. But what if we could predict failures before they happen and provide timely interventions? Deep Learning (DL) is revolutionizing higher education by identifying at-risk students early and enabling personalized learning experiences. Here’s how the future looks: Early Warning Systems – AI-driven models analyze academic performance, attendance, and engagement to detect students at risk of failing or dropping out. Personalized Learning Paths – Adaptive AI recommends customized coursework and study strategies tailored to each student's needs. Multimodal Data Integration – Combining academic records, behavioral signals, and even sentiment analysis from student interactions to get a 360-degree risk assessment. AI-Powered Chatbots & Mentors – Virtual assistants offer real-time academic and emotional support, keeping students engaged and motivated. Predictive Analytics for Universities – Institutions use AI-driven insights to optimize curriculum, faculty engagement, and student services, leading to higher retention rates. The Future? AI will not replace educators but will empower them with data-driven insights to provide proactive, targeted interventions. Universities that integrate deep learning with strong human-led strategies will redefine student success. What are your thoughts? Could AI be the key to reducing dropout rates and improving student outcomes? Let’s discuss! #AIinEducation #DeepLearning #StudentSuccess #HigherEd #PredictiveAnalytics #FutureOfEducation Glad to publish a paper titled "Enhancing Student Outcomes with LSTM-CNN and Data Analytics in Higher Education" during International Conference on Intelligent and Innovative Practices in Engineering & Management (IIPEM 2024) at Amity Global Institute,Singapore Shiv Nadar University With a focus on the use of Long Short-TermbMemory (LSTM) and Convolutional Neural Network (CNN) approaches to predict students' academic performance, the study highlights the possible advantages of implementing cutting-edge technology innovations like analytics and data mining in learning environments. Future research has exciting opportunities as the educational landscape changes, including the possibility of applying transfer learning models and the possibility of using lightweight models with extensive features for identifying students' learning results.