Discovering Student Dropout Prediction through Deep Learning

There have been increased incidences of dropout that have been noticed in the universities in the recent years. These increased reports have been instrumental in introducing the graduation rate of the course completion rate for majority of universities all over the globe. Dropouts are highly undesirable and are an indication of some underlying inconsistencies that have been plaguing the course since a long time. Therefore, an effective system for the purpose of prediction of the dropout rate is the need of the hour. To reach these goals this research article has utilized machine learning approaches. The proposed methodology utilizes the K Nearest Neighbor, Fuzzy Artificial Neural Network and Decision Tree. This approach has been illustrated in utmost detail in this research article, highlighting the execution of the various important modules of the methodology. The experimentation has been performed to achieve the performance of the approach which has yielded highly accurate results. Shashikant Karale | Rajani Pawar | Sharvari Pawar | Poonam Sonkamble "Discovering Student Dropout Prediction through Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43700.pdf Paper URL: https://www.ijtsrd.comhumanities-and-the-arts/education/43700/discovering-student-dropout-prediction-through-deep-learning/shashikant-karale