A Process Mining Framework for Analyzing Learning Clickstream Data
In this paper, we propose a process mining framework for analyzing large-scale learning clickstream data collected from a major US university’s learning management system. We address a number of modeling and analysis challenges from a process mining perspective and propose new concepts for process-centric learning analytics.
This study attempted to analyze the relationship between students’ participation in disucssion and their course performance.
Learning Behavior Analysis
Based on the Canvas Data, this research created a dashboard which provides early intervention signal to the student and faculty.
This project tried to predict chance of admit from important parameters such as GRE, TOEFL, and PS, etc.
Research is correlated with GRE and TOEFL.
CGPA is the most importance feature to increate admission probability