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Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

  • 12 Similar Profiles
Students Engineering & Materials Science
learning behavior Social Sciences
learning Social Sciences
quiz Social Sciences
broken symmetry Physics & Astronomy
neutrinos Physics & Astronomy
learning environment Social Sciences
behavior analysis Social Sciences

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Research Output 2006 2019

  • 151 Citations
  • 7 h-Index
  • 10 Article
  • 9 Conference contribution
  • 1 Conference article
reading behavior
behavior pattern
Human engineering
Students
learning behavior
3 Citations (Scopus)

Evaluating learning style-based grouping strategies in real-world collaborative learning environment

Taniguchi, Y., Gao, Y., Kentaro, K. & Konomi, S., Jan 1 2018, Distributed, Ambient and Pervasive Interactions: Technologies and Contexts - 6th International Conference, DAPI 2018, Held as Part of HCI International 2018, Proceedings. Konomi, S. & Streitz, N. (eds.). Springer Verlag, p. 227-239 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10922 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Learning Styles
Collaborative Learning
Collaborative Environments
Learning Environment
Grouping
7 Citations (Scopus)

Gauge symmetry breaking patterns in an SU (5) grand gauge-Higgs unification model

Kentaro, K., Takenaga, K. & Yamashita, T., Jan 1 2017, In : Physical Review D. 95, 1, 015021.

Research output: Contribution to journalArticle

broken symmetry
vacuum
symmetry
subgroups
guy wires

The standard model gauge symmetry from higher-rank unified groups in grand gauge-Higgs unification models

Kentaro, K., Takenaga, K. & Yamashita, T., Jun 1 2017, In : Journal of High Energy Physics. 2017, 6

Research output: Contribution to journalArticle

symmetry
broken symmetry
fermions
boundary conditions
vacuum

Learning activity features of high performance students

Okubo, F., Hirokawa, S., Terai, M., Shimada, A., Kentaro, K., Yamada, M. & Ogata, H., Jan 1 2016, In : CEUR Workshop Proceedings. 1601, p. 28-33 6 p.

Research output: Contribution to journalConference article

Students
Support vector machines