Young Researcher Paper Award 2023
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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 35, Number 12(3) (2023)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 4325-4336
S&M3483 Research Paper of Special Issue https://doi.org/10.18494/SAM4483 Published: December 26, 2023 MCQA: A Responsive Question-answering System for Online Education [PDF] Yi Wang, Jinsheng Deng, Xi Yang, Jianyu Yi, and Zhaohui Ye (Received April 28, 2023; Accepted December 8, 2023) Keywords: QA system, MOOCs, question classification, similarity retrieval, similarity computation, chitchat generation
Massive Open Online Courses (MOOCs) are now considered as representatives of online education. In addition to watching course videos and taking tests, online question & answering (Q&A) also plays an important role during MOOC learning. In this paper, we introduce a question-answering (QA) system called MCQA for MOOCs. The system comprises several modules, including question classification, similarity retrieval, similarity computation, and chitchat generation. On a real MOOC platform, MCQA demonstrates exceptional performance, with experimental results showing a precision rate exceeding 90% and an average duration of a Q&A session of less than 100 ms. Compared with other Chinese-based QA systems, MCQA provides superior open-ended QA capabilities, excelling in performance and covering numerous learning scenarios.
Corresponding author: Yi Wang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yi Wang, Jinsheng Deng, Xi Yang, Jianyu Yi, and Zhaohui Ye, MCQA: A Responsive Question-answering System for Online Education, Sens. Mater., Vol. 35, No. 12, 2023, p. 4325-4336. |
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