Compared to offline learners, online learners' attitude during the learning process is relatively poor, and a feeling of loneliness is entailed as they often study alone. This results in a low learning outcome. So far, no examples exist for the design of a learning companion to this end. Herein we present a pioneering work on a co-existing, artificial learning companion capable of improving the learner's attitude through sleepiness detection. We capture, analyze and estimate the level of sleepiness employing a machine learning technique with the pilot study data. Then, we propose a prototype called LearniCube using a sleepiness detection model with an experimental evaluation of LearniCube.
|Title of host publication||CHI 2017 Extended Abstracts - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems|
|Subtitle of host publication||Explore, Innovate, Inspire|
|Publisher||Association for Computing Machinery|
|Number of pages||8|
|Publication status||Published - 2017 May 6|
|Event||2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017 - Denver, United States|
Duration: 2017 May 6 → 2017 May 11
|Name||Conference on Human Factors in Computing Systems - Proceedings|
|Other||2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017|
|Period||17/5/6 → 17/5/11|
Bibliographical noteFunding Information:
We thank Jioon Park and Mincheol Shin who provided helpful comments on the prototype. This research was supported by the MSIP, Korea, under the G-ITRC support program (IITP-2017-R6812-15-0001) supervised by the IITP. This research was supported by the Ministry of Education(NRF-2016R1D1A1B02015987).
Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Graphics and Computer-Aided Design