AI DRIVEN CLASSROOM ANALYTICS AND STUDENT SUCCESS SYSTEM

7. Student Performance Analysis: Visualizes assessment trends, attendance records, and LMS activity to explain student risk patterns.
3. Student Risk Profile: Displays individual risk scores, engagement metrics, contributing factors, and personalized intervention plans.
6. CSV Data Ingestion: Uploads institutional CSV records, validates data, and initiates feature computation and risk reassessment.
4. Student Risk Registry: Searchable student records featuring risk classifications, attendance, assessment trends, and engagement scores.
5. Medium-Risk Filtering: Filters the student registry to display medium-risk students for focused monitoring and support.
1.Dashboard Overview: Classroom analytics dashboard showing student risk levels, attendance, assessment scores, and early-warning indicators
2. Early-Warning Alerts: Identifies at-risk students and recommends timely academic interventions based on risk levels.

Inspiration

Traditional classroom assessment methods commonly discover academic problems too late. We wanted to see how Artificial Intelligence and Machine Learning could detect patterns of learning and behaviors to give insight to educators and offer help. This sparks the idea of the AI Driven Classroom Analytics and Student Success System.

What it does

AI Driven Classroom Analytics and Student Success System is a system designed to analyze academic student based data, discover patterns of performance, detect students who may need help, and share insights with educators.

How we built it

We began the process of building our project by brainstorming factors that contribute to a student's success and learning. We thought of ways that data analysis and machine learning can help turn those factors into something useful and meaningful for educators. Our project is based on the concept of analyzing student data, finding patterns that may suggest a concerning behavior, and putting it into a form that educators can easily comprehend.

Challenges we ran into

One of the challenges we encountered was figuring out what information about the students will be useful and meaningful. Additionally, we had to think of how analysis and machine learning can be utilized to turn information into meaningful results. Finally, we had to consider the privacy of the students and make sure that no information is taken advantage of.

Accomplishments that we're proud of

We are very proud to be able to conceptualize and think of an idea that combines the idea of Artificial Intelligence and Machine Learning to be applied into a field related to education. We are also proud to be able to think of ways that data analysis and machine learning can help students succeed and provide meaningful insights for educators.

What we learned

We learned a lot in terms of artificial intelligence and machine learning, and how the two can be combined to provide meaningful insights and solutions to problems. We also learned that when making a system that is concerned with data analysis, there are a lot of things to take into consideration besides just predicting an outcome.

What's next for AI DRIVEN CLASSROOM ANALYTICS AND STUDENT SUCCESS SYSTEM

We want to improve our system by making it more accurate and analyzing more factors that may contribute to a student's performance. In addition, we want to be able to make charts and graphs that can be easily interpreted by educators. We also want to make it possible for educators to have a personalized feed and receive notifications for students who may need assistance and monitor the students' progress. Lastly, we want to make the AI Driven Classroom Analytics and Student Success System into a fully featured and functioning program that can truly make an impact in the minds of the educators and students.