KCUBE: A Knowledge Graph University Curriculum Framework for Student Advising and Career Planning

Qing Li, George Baciu, Jiannong Cao, Xiao Huang, Richard Chen Li, Peter H.F. Ng, Junnan Dong, Qinggang Zhang, Zackary P.T. Sin, Yaowei Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Knowledge representations and interactions are at the forefront of teaching, learning, and career planning activities in all endeavors of education and career development. University students are increasingly faced with a myriad of interdisciplinary topics that are seemingly unrelated when unstructured knowledge representations are presented, especially during advising and career orientation sessions. This is especially challenging in fast changing technical domains such as Computer Science and Engineering where university curricula are reviewed on an annual basis. This makes it increasingly difficult for instructors and administrators to present both the big picture as well as the detailed knowledge components of degree programs to students when choosing a career or establish a plan of study and assessment. This paper introduces the KCUBE project, a virtual reality knowledge graph framework for structuring and presenting both the overall view of the Computer Science curriculum taught in the Department of Computing at the Hong Kong Polytechnic University as well as the scheduling alternatives in managing course content and presentation views by instructors and students. We employ computational information storage and retrieval methods, machine learning, and interactive virtual reality to better understand, manipulate, and visualize abstract concepts and relationships in the development of teaching and learning activities in our department.

Original languageEnglish
Title of host publicationInternational Conference on Blended Learning
Subtitle of host publicationEngaging Students in the New Normal Era - 15th International Conference, ICBL 2022, Proceedings
EditorsRichard Chen Li, Simon K. Cheung, Peter H. Ng, Leung-Pun Wong, Fu Lee Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages358-369
Number of pages12
ISBN (Print)9783031089381
DOIs
Publication statusPublished - Jul 2022
Event15th International Conference on Blended Learning, ICBL 2022 - Virtual, Online
Duration: 19 Jun 202222 Jun 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13357 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Blended Learning, ICBL 2022
CityVirtual, Online
Period19/06/2222/06/22

Keywords

  • Big data
  • Computer science
  • Curriculum
  • Information retrieval
  • Knowledge bases
  • Knowledge graphs
  • Knowledge representation
  • Learning
  • Machine learning
  • Oculus Quest 2
  • Ontology
  • Teaching
  • Virtual reality

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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