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Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering
https://kitami-it.repo.nii.ac.jp/records/2000235
https://kitami-it.repo.nii.ac.jp/records/200023519ee3b4c-098f-4482-8a96-ae340d119461
| 名前 / ファイル | ライセンス | アクション |
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education-09-00228.pdf (1.4 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||
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| 公開日 | 2022-05-25 | |||||||
| タイトル | ||||||||
| タイトル | Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering | |||||||
| 言語 | en | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||
| 資源タイプ | journal article | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| 著者 |
AMM Sharif Ullah
× AMM Sharif Ullah
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation manufacturing (Industry 4.0, smart manufacturing, and connected factory) necessitates learning factory (human learning) and human-cyber-physical systems (machine learning). Both learning factory and human-cyber-physical systems require semantic web-embedded dynamic knowledge bases, which are subjected to syntax (machine-to-machine communication), semantics (the meaning of the contents), and pragmatics (the preferences of individuals involved). This article argues that knowledge-aware concept mapping is a solution to create and analyze the semantic web-embedded dynamic knowledge bases for both human and machine learning. Accordingly, this article defines five types of knowledge, namely, analytic a priori knowledge, synthetic a priori knowledge, synthetic a posteriori knowledge, meaningful knowledge, and skeptic knowledge. These types of knowledge help find some rules and guidelines to create and analyze concept maps for the purposes human and machine learning. The presence of these types of knowledge is elucidated using a real-life manufacturing knowledge representation case. Their implications in learning manufacturing knowledge are also described. The outcomes of this article help install knowledge-aware concept maps for discipline-based education. |
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| 言語 | en | |||||||
| 書誌情報 |
en : Education Sciences 巻 9, 号 3, p. 228, ページ数 14, 発行日 2019-08 |
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| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 2227-7102 | |||||||
| DOI | ||||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.3390/educsci9030228 | |||||||
| 権利 | ||||||||
| 言語 | en | |||||||
| 権利情報 | © 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |||||||
| 出版者 | ||||||||
| 出版者 | MDPI | |||||||
| 言語 | en | |||||||
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| 言語 | en | |||||||
| 値 | publisher | |||||||
| 出版タイプ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
