• Raghvendra Kumar Jha
• Neeraj Sharma
• Richa Nehra
• Rahmi Verma

During global lock down period due COVID-19 pandemic one of the immensely affected area was education sector. On the one hand shutting down of schools & colleges for indefinite period severely affected the traditional education system whereas on the other hand it has given hope to new research areas in education system using machine learning (ML) algorithms. In this research paper we have proposed a new machine learning model for performance evaluation of student vis a vis teacher and making recommendations for the best suitable student teacher pair based on ML algorithms. It introduces the principles behind ML algorithms and illustrates how these algorithms could be applied by the system to evaluating student academic performance based on his academic and behavioral parameters vis a vis teachers performance based on the feedback system. The ML model proposed in this paper aims to adaptively adjust the training for each student based on his/her own pace of learning. The ML model will monitor the student’s progress and can make decision about next step training. Several approaches using ML techniques have been proposed to provide a practical method for evaluating student / teacher performance and making final recommendations for student teacher collaboration.

Data Science, Machine Learning, Online Education Analysis, Analysis and Recommendation System, Student Learning Analysis

"STUDENT LEARNING ANALYSIS & RECOMMENDATION SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.e783-e786, August-2021, Available :http://www.jetir.org/papers/JETIR2108585.pdf

"STUDENT LEARNING ANALYSIS & RECOMMENDATION SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppe783-e786, August-2021, Available at : http://www.jetir.org/papers/JETIR2108585.pdf