Analysis of Knowledge and Information University of Tsukuba
This course introduces the concepts of knowledge and their formation and acquisition methods. The initial units discuss the concept of knowledge. After having seen the definition of knowledge as justified true belief and its criticism (the Gettier problem etc.), we argue about the sharing of knowledge in light of theories of meaning from the viewpoint of relativism and its criticism. The subsequent units introduce three ways of thinking about knowledge formation from the viewpoint of embodied cognition and discuss them in light of recent research trends. The last units introduce methods of knowledge representation and acquisition based on informetric models, focusing on the interrelations between knowledge, information and data.
To understand contemporary arguments about the definition of knowledge and the sharing of knowledge. To understand the process of knowledge formation from the parspective of the relations between knowlodge, information and data. To understand the methods of knowledge representation and acquisition.
Knowledge application competence, Qualitative research competence, Resource expertise
Tasks based on the items indicated in the goals will be assigned multiple times, and three parts (1)-(4), (5)-(7), (8)-(10) will be evaluated as one theme each. Comprehension of assignments and completion of reports, etc. will be evaluated and the results will be quantified. For the entire subject, each theme is totaled with the weight at 1:1:1, and those with a total score of 60 points or more will pass.
(1)Definition of Knowledge: Classic Definition (Yokoyama) (2)Definition of Knowledge: The Gettier Problem (Yokoyama) (3)Sharing of Knowledge: Relativism 1 (Yokoyama) (4)Sharing of Knowledge: Relativism 2 (Yokoyama) (5)Knowledge Formation: Ecological Approach, Embodied Cognition (Matsubara) (6)Knowledge Formation: Perceptual Symbol System (Matsubara) (7)Knowledge Formation: Conceptual Metaphor Theory (Matsubara) (8)Knowledge, Information and Data: Informetric Model (Yoshikane) (9)Knowledge Representation: Statistical and Network Models (Yoshikane) (10)Knowledge Acquisition: Statistical and Network Analyses (Yoshikane)
Online Course Requirement
Classes are held face-to-face. Use manaba to communicate, distribute lecture materials, and confirm attendance. Access manaba during the regular hours of the first class.
Site for Inquiry
Please inquire about the courses at the address below.
Contact person: Yokoyama Mikiko,Matsubara Masaki
Email address: email@example.com,masaki at slis.tsukuba.ac.jp
Link to the syllabus provided by the university