Rating of Online Courses: A Machine Learning Based Prediction Model

Title

Rating of Online Courses: A Machine Learning Based Prediction Model

Creator

Inder S.; Dua G.K.; Verma R.; Sinha S.

Description

Online courses market has provided an economical and easy access to knowledge. When it comes to make a decision related to purchase of online course, little is known about what attributes can be depended upon to guess the quality of an online course. Ratings for online courses act as a reliable signal for assessing the quality of a course. The study discusses the prediction of ratings for online courses using Artificial Neural Network based on Particle Swarm Optimization (ANN-PSO). The experimental results suggests that ANN-PSO model has the capacity to predict the ratings for online courses on the basis of its attributes with accuracy. 2021 IEEE.

Source

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

Date

2021-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

ANN-PSO; Online courses; Prediction; Ratings

Coverage

Inder S., Chitkara University, Chitkara Business School, Punjab, India; Dua G.K., Chitkara University, Chitkara Business School, Punjab, India; Verma R., Chitkara University, Chitkara Business School, Punjab, India; Sinha S., CHRIST (Deemed to Be University), India

Rights

Restricted Access

Relation

ISBN: 978-166541703-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICRITO51393.2021.9596449

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123363463&doi=10.1109%2fICRITO51393.2021.9596449&partnerID=40&md5=30cc891e37f901b39c93becd5abc7b9b