Learning Analytics Powered Teacher Facing Dashboard to Visualize, Analyze Students’ Academic Performance and give Key DL(Deep Learning) Supported Key Recommendations for Performance Improvement. Prof. K. V. Deshpande Department of Computer Engineering. JSPM’s Rajarshi Shahu college of engineering,Tathawade. Pune, India kvdeshpande comp@jspmrscoe.edu.in Shubham Asbe Department of Computer Engineering . JSPM’s Rajarshi Shahu college of engineering,Tathawade. Pune, India asbeshubham143@gmail.com Akanksha Lugade Department of Computer Engineering. JSPM’s Rajarshi Shahu college of engineering,Tathawade. Pune, India akankshaakanksha2710@gmail.com Yash More Department of Computer Engineering. JSPM’s Rajarshi Shahu college of engineering,Tathawade. Pune, India yashmore246@gmail.com Dipali Bhalerao Department of Computer Engineering. JSPM’s Rajarshi Shahu college of engineering,Tathawade. Pune, India bhaleraodipali675@gmail.com Anuradha Partudkar Department of Computer Engineering. JSPM’s Rajarshi Shahu college of engineering,Tathawade.) Pune, India aapartudkar@jspmrscoe.edu.in Abstract—COVID-19 has forced the government to close ed- ucational institutes to reduce the spread of the virus. As a result of this decision, students lose contact with teachers and a communication gap also arises. This survey attempts to bridge the gap between students and teachers. Through this survey, we sought to understand where the students are lacking and what are the different steps that can be taken by the teacher to improve the performance of the student and whether this concept should be reviewed or not. We found that most of the researchers who have published papers that we have read did the same mistake in their research, therefore we realized that the concept of AI should be studied again, and we should try not to repeat the same mistake in our research.The main aim of our project is to build ”Teacher facing dashboard” which can help the teacher to summarize,visualize and analyze the data of the education field(academics) and also understanding the students performance using Machine Learning(ML) and Deep Learning (DL). Index Terms—teacher facing dashboard, learning analytics, students, teachers, visualize, analyze, education field, machine learning, deep learning. I. I NTRODUCTION To comprehend and optimize learning experiences, learning analytics systems use and analyse behavioural and interaction data from students. Applications for learning analytics are used. serves a variety of uses at educational institutions, including student’s performance tracking tools, learning plat- forms for college students, and teaching systems for teachers educational consultants. known early warning systems, Dash- boards examine educational data to identify underachievers facilitating timely interventions by teachers or advisers for students. Learning Analytics is gaining popularity (LA) has expanded quickly among educational institutions (HEIs) across the globe in recent years. Data will be harnessed by LA to enhance the learning process and, by extension, the contexts in which it unfolds. With a strong emphasis on educators and students, LA has a lot of opportunity to address educa- tional issues. Higher education institutions (HEIs) are facing a growing desire to live, exhibit, and enhance performance. As a result, learning analytics becomes a feasible alternative for resolving issues with student achievement, development, and attrition. Learning analytics, as contrasted to educa- tional data acquisition and academic analytics, is focused on resolving educational challenges, utilising decision making, and enhancing learning. Data cannot accurately represent an individual’s identity, interests, or values because they are 2023 International Conference for Advancement in Technology (ICONAT) Goa, India. Jan 24-26, 2023 978-1-6654-7517-4/23/$31.00 ©2023 IEEE 1 2023 International Conference for Advancement in Technology (ICONAT) | 978-1-6654-7517-4/23/$31.00 ©2023 IEEE | DOI: 10.1109/ICONAT57137.2023.10080832 Authorized licensed use limited to: AMITY University. Downloaded on June 02,2023 at 11:01:10 UTC from IEEE Xplore. Restrictions apply.