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- Student performance can be gauged only after exams
- Teachers do not about which students are struggling with what lessons
- Teachers do not know which students need special attention
- Precautionary measures can’t be taken to improve an individual student Performance.
- Not able to predict which student will Dropout
- Run machine learning regression models on student data Sets such as e-learning log files (when they interact with digital learning systems), Attendance data, Historical data, demographic data, educator demographic, performance data, registration data and others.
- Models Accurately predict the student performance, weak areas, strong areas, which students need special attention and on what subjects.
- Our Models also predict the risk of student dropout
- Our Models continuously learns by processing huge amounts of data and continuously improves its accuracy in prediction.
- Teachers can identify the students who are at risk of dropping out, why they are struggling, as well as provide insight into the interventions needed to overcome their learning challenges: