The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Artikel ini memperkenalkan penyiasatan kami tentang anggaran keadaan pembelajaran dalam e-pembelajaran dengan syarat pemerhatian visual dan rakaman tingkah laku pelajar adalah mungkin. Dalam penyelidikan ini, kami mengkaji kaedah penyesuaian untuk pelajar baharu yang mana sebilangan kecil data kebenaran asas boleh diperolehi.
Siyang YU
Kyoto University
Kazuaki KONDO
Kyoto University
Yuichi NAKAMURA
Kyoto University
Takayuki NAKAJIMA
Kyoto University
Masatake DANTSUJI
Kyoto University
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Salinan
Siyang YU, Kazuaki KONDO, Yuichi NAKAMURA, Takayuki NAKAJIMA, Masatake DANTSUJI, "Investigation on e-Learning Status Estimation for New Learners — Classifier Selection on Representative Sample Selection" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 4, pp. 905-909, April 2020, doi: 10.1587/transinf.2019EDL8043.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8043/_p
Salinan
@ARTICLE{e103-d_4_905,
author={Siyang YU, Kazuaki KONDO, Yuichi NAKAMURA, Takayuki NAKAJIMA, Masatake DANTSUJI, },
journal={IEICE TRANSACTIONS on Information},
title={Investigation on e-Learning Status Estimation for New Learners — Classifier Selection on Representative Sample Selection},
year={2020},
volume={E103-D},
number={4},
pages={905-909},
abstract={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.},
keywords={},
doi={10.1587/transinf.2019EDL8043},
ISSN={1745-1361},
month={April},}
Salinan
TY - JOUR
TI - Investigation on e-Learning Status Estimation for New Learners — Classifier Selection on Representative Sample Selection
T2 - IEICE TRANSACTIONS on Information
SP - 905
EP - 909
AU - Siyang YU
AU - Kazuaki KONDO
AU - Yuichi NAKAMURA
AU - Takayuki NAKAJIMA
AU - Masatake DANTSUJI
PY - 2020
DO - 10.1587/transinf.2019EDL8043
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2020
AB - 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.
ER -