Investigation on e-Learning Status Estimation for New Learners — Classifier Selection on Representative Sample Selection

Siyang YU  Kazuaki KONDO  Yuichi NAKAMURA  Takayuki NAKAJIMA  Masatake DANTSUJI  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.4   pp.905-909
Publication Date: 2020/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDL8043
Type of Manuscript: LETTER
Category: Educational Technology
e-Learning status estimation,  visual sensing,  interpersonal differences,  adaptation to new learner,  

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This article introduces our investigation on learning state estimation in e-learning on the condition that visual observation and recording of a learner's behaviors is possible. In this research, we examined methods of adaptation for a new learner for whom a small number of ground truth data can be obtained.