• Register
  • Login

European Journal of Molecular & Clinical Medicine

  • Home
  • Browse
    • Current Issue
    • By Issue
    • By Subject
    • Keyword Index
    • Author Index
    • Indexing Databases XML
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
Advanced Search

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 7, Issue 4
  3. Authors

Online ISSN: 2515-8260

Volume7, Issue4

Students Attention and Engagement Prediction Using Machine Learning Techniques

    Leelavathy S Jaichandran R Shantha Shalini K Surendar B Aswin K Philip Dekka Raja Ravindra

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 3011-3017

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

Real-time student engagement tracking is an important step towards education. Current approach doesn’t consider student engagement detection using biometric features. In this project, we propose a hybrid architecture invoking student’s eye gaze movements, head movements and facial emotion to dynamically predict student attention and engagement level towards the tutor and based on the output value the content is changed dynamically. Hence this concept has a huge scope in e-learning, class room training, analyse human behaviour. This project covers main process like Eye Ball, facial emotion and head movements Human Beings. For feature extraction step, we used Principal Component Analysis (PCA) for facial emotion recognition, Haar Cascade for pupil detection and Local Binary Patterns for recognizing head movements and OpenCV for machine learning model generation and comparison.
Keywords:
    Eye gaze head movement facial emotion Machine learning Haar Cascade PCA
  • PDF (963 K)
  • XML
(2020). Students Attention and Engagement Prediction Using Machine Learning Techniques. European Journal of Molecular & Clinical Medicine, 7(4), 3011-3017.
Leelavathy S; Jaichandran R; Shantha Shalini K; Surendar B; Aswin K Philip; Dekka Raja Ravindra. "Students Attention and Engagement Prediction Using Machine Learning Techniques". European Journal of Molecular & Clinical Medicine, 7, 4, 2020, 3011-3017.
(2020). 'Students Attention and Engagement Prediction Using Machine Learning Techniques', European Journal of Molecular & Clinical Medicine, 7(4), pp. 3011-3017.
Students Attention and Engagement Prediction Using Machine Learning Techniques. European Journal of Molecular & Clinical Medicine, 2020; 7(4): 3011-3017.
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 54
  • PDF Download: 128
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
Journal Information

Publisher:

Email:  info@ejmcm.com

  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

Editorial Team:  editor@ejmcm.com

For Special Issue Proposal : chiefeditor.ejmcm@gmail.com / info@ejmcm.com

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus