%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 }