@inproceedings{9ab42a261d574065a4681b473ecdcc77,
title = "KCUBE: A Knowledge Graph University Curriculum Framework for Student Advising and Career Planning",
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.",
keywords = "Big data, Computer science, Curriculum, Information retrieval, Knowledge bases, Knowledge graphs, Knowledge representation, Learning, Machine learning, Oculus Quest 2, Ontology, Teaching, Virtual reality",
author = "Qing Li and George Baciu and Jiannong Cao and Xiao Huang and Li, {Richard Chen} and Ng, {Peter H.F.} and Junnan Dong and Qinggang Zhang and Sin, {Zackary P.T.} and Yaowei Wang",
note = "Funding Information: Acknowledgements. The authors gratefully acknowledge receipt of the following financial support for the research, authorship, and/or publication of this article. This work was supported in full by the Hong Kong Polytechnic University, Project of Strategic Importance (project number: P0036846). Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 15th International Conference on Blended Learning, ICBL 2022 ; Conference date: 19-06-2022 Through 22-06-2022",
year = "2022",
month = jul,
doi = "10.1007/978-3-031-08939-8_31",
language = "English",
isbn = "9783031089381",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "358--369",
editor = "Li, {Richard Chen} and Cheung, {Simon K.} and Ng, {Peter H.} and Leung-Pun Wong and Wang, {Fu Lee}",
booktitle = "International Conference on Blended Learning",
address = "Germany",
}