An Artificial Intelligence Approach to Identifying Skill Relationship

Authors

  • Tak-Lam WONG Department of Computing Studies & Information Systems, Douglas College, Canada Author
  • Yuen Tak YU Department of Computer Science, City University of Hong Kong, Hong Kong Author
  • Chung Keung POON School of Computing and Information Sciences, Caritas Institute of Higher Education, Hong Kong Author
  • Haoran XIE Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong Author
  • Fu Lee WANG School of Computing and Information Sciences, Caritas Institute of Higher Education, Hong Kong Author
  • Chung Man TANG Author

Abstract

Designing a good curriculum or an appropriate learning path for learners is challenging because it requires a very good and clear understanding of the subjects concerned as well as many other factors. One common objective of educational data mining and learning analytics is to assist learners to enhance their learning via the discovery of interesting and useful patterns from learning data. We have recently developed a technique called skill2vec, which utilizes an artificial neural network to automatically identify the relationship between skillsfrom learning data. The outcome of skill2vec can help instructors, course planners and learners to have a more objective and data-informed decision making. Skill2vec transforms a skill to a vector in a new vector space by considering the contextual skills. Such a transformation, called embedding, allows the discovery of relevant skills that may be implicit. We conducted experiments on two real-world datasets collected from an online intelligent tutoring system. The results show that the outcome of skill2vec is consistent and reliable.

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Published

2017-12-04

How to Cite

An Artificial Intelligence Approach to Identifying Skill Relationship. (2017). International Conference on Computers in Education. http://library.apsce.net/index.php/ICCE/article/view/2226