For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
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
Publication Date: 2020/04/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Educational Technology
e-Learning status estimation, visual sensing, interpersonal differences, adaptation to new learner,
Full Text: PDF(657.1KB)>>
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.