A Review on Students’ Performance Prediction using Learning Analytics

Journal: GRENZE International Journal of Engineering and Technology
Authors: Jyoti, Dharminder Kumar, Sakshi Dhingra
Volume: 10 Issue: 1
Grenze ID: 01.GIJET.10.1.354 Pages: 929-936

Abstract

Learning Analytics is a research area that is growing rapidly. It deals with selecting, analyzing, and reporting educational data collected from various learning environments and finding relevant patterns in students’ behavior. Different online learning platforms like Massive Open Online Courses (edX, Udemy, Udacity, etc.), Learning Management Systems (Moodle, Blackboard, Classroom, etc.), and Virtual Learning Environments (DEEDS, Coursera, etc.). Learning analytics can be viewed as a multidisciplinary field that includes Machine Learning (ML) Techniques, Educational Data Mining (EDM), Statistics, Social Network Analysis, and Natural Language Processing. This paper comprises the study of Learning Analytics and its different techniques used in predicting students’ performance. Further, it consists of an analysis of previous studies described in tabular form for better understanding. A basic learning analytics model is described which consists of the steps involved in creating any project

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