EduPredict – A Multi-Modal Framework for Student Performance Analysis, Academic Risk Detection, and Outcome Optimization
S t u d e n t p e r f o r m a n c e p r e d i c t i o n i s a c r i t i c a l c o m p o n e n t o f m o d e r n e d u c a t i o n a l d a t a m i n i n g , e n a b l i n g i n s t i t u t i o n s , e d u c a t o r s , a n d l e a r n e r s t o i d e n t i f y a c a d e m i c t r e n d s a n d i n t e r v e n e p r o a c t i v e l y b e f o r e o u t c o m e s d e t e r i o r a t e . H o w e v e r , m o s t e x i s t i n g a c a d e m i c p l a t f o r m s f a i l t o p r o v i d e r e a l i s t i c p r e d i c t i v e e n v i r o n m e n t s , d a t a - d r i v e n s t r a t e g y e v a l u a t i o n , a n d p e r s o n a l i z e d s t u d e n t i n s i g h t s . T o a d d r e s s t h e s e l i m i t a t i o n s ,t h i s s t u d y p r o p o s e s E d u P r e d i c t , a m u l t i m o d a l f r a m e w o r k b u i l t u s i n g M E R N a n d P y t h o n - F l a s k . T h e s y s t e m i n t e g r a t e s h i s t o r i c a l a c a d e m i c r e c o r d s , s t u d e n t - p r o v i d e d c o n t e x t u a l f e a t u r e s , a n d t r a i n e d m a c h i n e l e a r n i n g m o d e l s t o e n h a n c e p r e d i c t i o n a c c u r a c y . T h e a r c h i t e c t u r e e m p l o y s a N o d e . j s b a c k e n d f o r r e q u e s t r o u t i n g , a P y t h o n - F l a s k a n a l y t i c s e n g i n e t h a t m o d e l s a c a d e m i c p e r f o r m a n c e , r i s k c l a s s i f i c a t i o n , a n d g r a d e p r e d i c t i o n , a n d M o n g o D B f o r s t u d e n t d a t a a n d t r a n s a c t i o n s t o r a g e . F u r t h e r m o r e , t h e f r a m e w o r k i n c o r p o r a t e s A I - d r i v e n r i s k a n a l y t i c s a n d p e r f o r m an c e e v a l u a t i o n t o p r o v i d e p e r s o n a l i z e d i n s i g h t s f o r s t u d e n t d e c i s i o n s u p p o r t .E x p e r i m e n t a l r e s u l t s d e m o n s t r a t e t h a t t h e p r o p o s e d s y s t e m a c h i e v e s h i g h p r e d i c t i o n r e l i a b i l i t y ( > 8 5 % R ² s c o r e ) a n d i m p r o v e d c l a s s i f i c a t i o n a n a l y t i c s . T h e r e s u l t s i n d i c a t e t h a t E d u P r e d i c t e f f e c t i v e l y c a p t u r e s a c a d e m i c b e h a v i o u r p a t t e r n s a n d p r o v i d e s r o b u s t s u p p o r t f o r p e r f o r m a n c e t r a c k i n g , r i s k i d e n t i f i c a t i o n , a n d c a r e e r g u i d a n c e o p t i m i z a t i o n .
Authors
- Shweta Sharan
- Ujjval Jain
- Yashika Soni
- Nidhi Sinha
- Shruti Arora
Institutions
- Guru Gobind Singh Indraprastha University (IN)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-05-10
- DOI
- https://doi.org/10.5281/zenodo.20110617
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00