@article{oai:kitami-it.repo.nii.ac.jp:02000235,
 author = {AMM Sharif Ullah},
 issue = {3},
 journal = {Education Sciences},
 month = {Aug},
 note = {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.},
 title = {Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering},
 volume = {9},
 year = {2019}
}