prajjwal295 / abhivyakti

Abhivyakti

Our project is a real-time classroom engagement platform empowered by machine learning technology. It offers teachers the ability to monitor student activities such as facial expressions, hand gestures, and body language during class sessions. Through automatic report generation and personalized insights, teachers can adapt their teaching methods to enhance student engagement and learning outcomes effectively."

Abhivyakti Instant Feedback Based Categorical System using ML

"Our ML model utilizes convolutional neural networks (CNNs) implemented with the Keras framework to analyze student activities in real-time during classroom sessions. Trained on a high-quality dataset capable of detecting facial expressions, hand gestures, and body language cues such as writing, hand raises, and eye contact, the model accurately recognizes various student behaviors. By processing live video or image feeds from classroom cameras, the model provides instant feedback to teachers, enabling them to gauge student engagement levels and adjust their teaching strategies accordingly. The model's robust architecture and advanced algorithms make it capable of handling diverse classroom environments and scenarios, ultimately contributing to more effective teaching and learning experiences." This model is capable of giving feed back of the students to the teacher .

Explore Abhivyakti.

Presentation

Introduction

Brief overview of the project's purpose and objectives. Importance of understanding student engagement for effective teaching.

The key factors of the project are:

1.Real-time analysis of student activities. 2.Utilization of a machine learning model for analysis. 3.Detection of facial expressions, gestures, eye contact, and angle. 4.Automatic report generation for teachers. 5.Personalized feedback to enhance teaching strategies.

Commitment to Excellence

Absolutely! By providing real-time feedback and insights to teachers, the app empowers them to create more engaging learning experiences for students. As teachers adapt their teaching methods based on the app's feedback, students benefit from a more dynamic and interactive classroom environment tailored to their needs and preferences. This holistic approach to classroom engagement enhances the overall learning experience, leading to improved student participation, comprehension, and academic outcomes.

How to use the model :

1. pip install -r requirements
2. Add the model to the folder named ML_Model
3. Add the data set .

Dataset

Model