Student Progress Analyzer is an application built for teachers to track progress of their students and predict the performance of the students in upcoming exams. The application keeps track of the student's attendance and their participation in the interactive activities happening in the classroom and using this information, it predicts with about 80% accuracy how the students will perform in the forecoming examinations. It is powered by a machine learning model trained with SVM on a data-set of similar data.
Nowadays, due to large number of students in a single class, teachers find it hard to identify weak students and to focus on boosting their performance. As a result, many students do not receive proper attention at the right time and end up failing their exams. Our application is an attempt to resolve this problem by predicting performance of the students beforehand and making it possible for the teachers to focus on weaker students in time.
Our application has a very intuitive and simple UI. On the home page, we provide options for login and signup. Once the teacher has created an account and they login, they are redirected to the progress tracking page where they are greeted with a table which shows a list of their student's names along with the prediction of the student's performance in upcoming tests based on currently available data. They can also access the detail page of a particular student by clicking on that student's entry in the table. The students are classified into three categories: average performance, good performance and excellent performance. On the progress tracking page, teachers have the option to add new students and delete older ones. They also have the option to update data of all the students after they have taken a class.
- Django
- Django Rest Framework
- drf-yasg (Swagger Generator)
- AJAX
- Sci-Kit Learn
Nuture The Future
