In this paper, we have reviewed the current state of the art about attentiveness detection methods used in e-learning. Classification of the prior works is done based on features measured, affective states considered, affect classification method and e-learning context. We have listed the drawbacks of webcam based elearning systems. We have also proposed a framework for elearning classroom. The different attention patterns exhibited by students under e-learning are studied. The potential applications of attentiveness detection system in e-learning are explored.
%0 Conference Paper
%1 narayanan2014computer
%A Narayanan, S. Athi
%A Kaimal, M. R.
%A Bijlani, Kamal
%A Prasanth, M.
%A Kumar, K. Sunil
%B Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing
%C New York, NY, USA
%D 2014
%I ACM
%K Affective Evaluation Methodology Self-assessment analytics biometrics biosensors classroom education learning vision
%P 51:1--51:5
%R 10.1145/2660859.2660965
%T Computer Vision Based Attentiveness Detection Methods in E-Learning
%U http://doi.acm.org/10.1145/2660859.2660965
%X In this paper, we have reviewed the current state of the art about attentiveness detection methods used in e-learning. Classification of the prior works is done based on features measured, affective states considered, affect classification method and e-learning context. We have listed the drawbacks of webcam based elearning systems. We have also proposed a framework for elearning classroom. The different attention patterns exhibited by students under e-learning are studied. The potential applications of attentiveness detection system in e-learning are explored.
%@ 978-1-4503-2908-8
@inproceedings{narayanan2014computer,
abstract = {In this paper, we have reviewed the current state of the art about attentiveness detection methods used in e-learning. Classification of the prior works is done based on features measured, affective states considered, affect classification method and e-learning context. We have listed the drawbacks of webcam based elearning systems. We have also proposed a framework for elearning classroom. The different attention patterns exhibited by students under e-learning are studied. The potential applications of attentiveness detection system in e-learning are explored.},
acmid = {2660965},
added-at = {2014-10-15T17:38:13.000+0200},
address = {New York, NY, USA},
articleno = {51},
author = {Narayanan, S. Athi and Kaimal, M. R. and Bijlani, Kamal and Prasanth, M. and Kumar, K. Sunil},
biburl = {https://www.bibsonomy.org/bibtex/282e4ebf6a22a582d54e13c7cb5685f0c/yish},
booktitle = {Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing},
doi = {10.1145/2660859.2660965},
interhash = {502947eab02e6a551c997ab8cc4947cc},
intrahash = {82e4ebf6a22a582d54e13c7cb5685f0c},
isbn = {978-1-4503-2908-8},
keywords = {Affective Evaluation Methodology Self-assessment analytics biometrics biosensors classroom education learning vision},
location = {Amritapuri, India},
numpages = {5},
pages = {51:1--51:5},
publisher = {ACM},
series = {ICONIAAC '14},
timestamp = {2014-10-15T17:38:13.000+0200},
title = {Computer Vision Based Attentiveness Detection Methods in E-Learning},
url = {http://doi.acm.org/10.1145/2660859.2660965},
year = 2014
}