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