An automatic approach for discovering skill relationship from learning data

Tak-Lam WONG, Haoran XIE, Fu Lee WANG, Chung Keung POON, Di ZOU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

1 Scopus Citations

Abstract

We have developed a method called skill2vec, which applies big data techniques to automatically analyze the learning data to discover skill relationship, leading to a more objective and datainformed decision making. Skill2vec is a neural network architecture which can transform a skill to a new vector space called embedding. The embedding can facilitate the comparison and visualization of different skills and their relationship. We conducted a pilot experiment using benchmark dataset to demonstrate the effectiveness of our method.

Original languageEnglish
Title of host publicationProceedings of the Seventh International Learning Analytics & Knowledge Conference
PublisherAssociation for Computing Machinery
Pages608-609
Number of pages2
ISBN (Print)9781450348706
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event7th International Learning Analytics and Knowledge Conference - Simon Fraser University , Vancouver, Canada
Duration: 13 Mar 201717 Mar 2017
http://lak17.com

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Learning Analytics and Knowledge Conference
Abbreviated titleLAK 2017
CountryCanada
CityVancouver
Period13/03/1717/03/17
Internet address

    Fingerprint

Bibliographical note

The work described in this paper is substantially supported by the grants from Research Grants Council of the HKSAR (Ref.: UGC/FDS11/E06/14, UGC/FDS11/E02/15, UGC/FDS11/E03/16).

Keywords

  • Data analytics
  • Deep learning
  • Embedding
  • Skill relationship

Cite this

WONG, T-L., XIE, H., WANG, F. L., POON, C. K., & ZOU, D. (2017). An automatic approach for discovering skill relationship from learning data. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 608-609). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3027385.3029485