Hey, wake up: Come alongwith the artificial learning companion to the e-learner's outcomes high!

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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.
Publisher
ACM Special Interest Group on Computer-Human Interaction (SIGCHI)
Issue Date
2017-05
Language
English
Citation

2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017, pp.1763 - 1770

DOI
10.1145/3027063.3053123
URI
http://hdl.handle.net/10203/307179
Appears in Collection
RIMS Conference Papers
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