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AuCM: Course Map Data Analytics for Australian IT Programs in Higher Education

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13725))

Abstract

Concept maps have emerged as an essential tool for illustrating the mutual relationships between knowledge in the domain. It provides guidance and suggestions in many educational applications such as optimal study order generating, curriculum design and course evaluation. However, there are no consistent datasets for concept map research. In this work, we aim to build a comprehensive dataset for learning concept maps. Specifically, we collect 1292 undergraduate courses in Information Technology (IT) and Computer Science (CS) from 14 Australian universities including course ID, category and prerequisite requirements. Besides, we analyze the semantic properties based on the concepts retrieved from the course description and visualize them to illustrate how our dataset could be used. To the best of our knowledge, this is the first dataset containing course information from Australian universities.

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Acknowledgements

This research was supported by a grant from Australian Research Council Linkage Project with Grant Number LP180100750.

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Correspondence to Yifu Tang or Taige Zhao .

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Xia, J., Tang, Y., Zhao, T., Li, J. (2022). AuCM: Course Map Data Analytics for Australian IT Programs in Higher Education. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_13

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  • DOI: https://doi.org/10.1007/978-3-031-22064-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22063-0

  • Online ISBN: 978-3-031-22064-7

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