Analyz Student Behavior Before and After Classroom AI-Based

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  Create New Analysis Session

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  Export Analysis Results

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  Comprehensive Emotional Analysis

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  Capture Student Emotions at Class Exit

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  Capture Student Emotions at Class Entry

Project Overview: Analyz Student Behavior Before and After Classroom AI-Based

Inspiration We built this system to provide objective, data-driven feedback on classroom instruction, aiming to quantify the emotional impact of a lesson on students instead of relying on subjective surveys. We measure student emotional states before (Entry) and after (Exit) a class to assess well-being and engagement.

What it Does This AI platform measures and compares student emotions at class Entry and Exit. It uses a Trained Keras Model to classify seven emotions from faces, calculating a definitive Overall Improvement Score. It provides Positivity Scores, Heatmaps, and Actionable Teaching Recommendations based on the emotional shift observed.

How We Built It We used a Python stack:

Deep Learning: TensorFlow/Keras for emotion classification.

Computer Vision: OpenCV for robust multi-face detection.

Analysis: Pandas for calculating comparative metrics like the Improvement Score.

Interface: Gradio for the interactive web UI.

Challenges We Ran Into The main challenge was ensuring system stability due to the Keras dependency; we created a simulated emotion detection fallback to keep the analysis functional even if the deep learning model is unavailable.

Accomplishments That We're Proud Of We delivered a complete, end-to-end system that successfully implements the unique Entry vs. Exit comparison logic to quantify the emotional change in a class, providing immediate, actionable insights.

What We Learned We mastered bridging the gap between Computer Vision (OpenCV) and Deep Learning (Keras) through precise image preprocessing and reinforced the importance of building robust systems with graceful fallbacks.

What's Next for Analyz Student Behavior Before and After Classroom AI-Based Future work includes full real-time video monitoring, individual student tracking, and developing a dedicated Teacher Dashboard for managing and comparing data across multiple sessions.

Built With

• gradio
• hdf5
• matplotlib
• numpy
• opencv
• pandas
• python
• seaborn
• tensorflow/keras