A talent assessment model based on learning behaviors and patterns

Haoran XIE, Yi CAI, Tak-Lam WONG, Di ZOU, Fu Lee WANG

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

Abstract

Talent assessment is an important topic in various areas like enterprise management, education, and psychology. However, it is also a challenging topic as the conventional assessment methods and models are unsuitable for talent assessment due to the following two aspects: (i) domain-dependent. The assessment of talent is highly depended on a specific domain which requires a large volume of domain knowledge in the assessment model; and (ii) behavior-based (or pattern-based). The characteristics of talents are reflected by a wide range of factors like their behaviors (patterns), emotions, self-identities, and metacognition. In this paper, we propose a talent assessment model based on online learning behaviors and patterns by using fuzzy models. Specifically, we attempt to develop a talent assessment model by identifying their learning data as we believe that the learning behaviors in the online learning platforms like massive open online courses (MOOCs) can reflect some characteristics of talents. Furthermore, we discuss what are the data sources, the learning behaviors and the potential computational methods in this assessment model in details. In addition, the potential limitations and possible improvement plans are introduced.

Original languageEnglish
Title of host publicationProceedings : 2019 International Symposium on Educational Technology, ISET 2019
EditorsFu Lee WANG, Oliver AU, Blanka KLIMOVA, Josef HYNEK, Petra HYNEK
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3-6
Number of pages4
ISBN (Electronic)9781728133874
ISBN (Print)9781728133898
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event2019 International Symposium on Educational Technology - Hradec Kralove, Czech Republic
Duration: 2 Jul 20194 Jul 2019

Conference

Conference2019 International Symposium on Educational Technology
Abbreviated titleISET 2019
CountryCzech Republic
CityHradec Kralove
Period2/07/194/07/19

Fingerprint

learning behavior
Computational methods
behavior pattern
Education
electronic learning
emotion
psychology
Industry
management

Bibliographical note

The research in this paper was supported by the Innovation and Technology Fund (Project No. GHP/022/17GD) from the Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region, and the Science and Technology Planning Project of Guangdong Province (No.2016A030310423, 2017B050506004).

Keywords

  • Fuzzy model
  • Learning behavior
  • Learning pattern
  • Talent assessment

Cite this

XIE, H., CAI, Y., WONG, T-L., ZOU, D., & WANG, F. L. (2019). A talent assessment model based on learning behaviors and patterns. In F. L. WANG, O. AU, B. KLIMOVA, J. HYNEK, & P. HYNEK (Eds.), Proceedings : 2019 International Symposium on Educational Technology, ISET 2019 (pp. 3-6). [8782210] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISET.2019.00011
XIE, Haoran ; CAI, Yi ; WONG, Tak-Lam ; ZOU, Di ; WANG, Fu Lee. / A talent assessment model based on learning behaviors and patterns. Proceedings : 2019 International Symposium on Educational Technology, ISET 2019. editor / Fu Lee WANG ; Oliver AU ; Blanka KLIMOVA ; Josef HYNEK ; Petra HYNEK. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3-6
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abstract = "Talent assessment is an important topic in various areas like enterprise management, education, and psychology. However, it is also a challenging topic as the conventional assessment methods and models are unsuitable for talent assessment due to the following two aspects: (i) domain-dependent. The assessment of talent is highly depended on a specific domain which requires a large volume of domain knowledge in the assessment model; and (ii) behavior-based (or pattern-based). The characteristics of talents are reflected by a wide range of factors like their behaviors (patterns), emotions, self-identities, and metacognition. In this paper, we propose a talent assessment model based on online learning behaviors and patterns by using fuzzy models. Specifically, we attempt to develop a talent assessment model by identifying their learning data as we believe that the learning behaviors in the online learning platforms like massive open online courses (MOOCs) can reflect some characteristics of talents. Furthermore, we discuss what are the data sources, the learning behaviors and the potential computational methods in this assessment model in details. In addition, the potential limitations and possible improvement plans are introduced.",
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XIE, H, CAI, Y, WONG, T-L, ZOU, D & WANG, FL 2019, A talent assessment model based on learning behaviors and patterns. in FL WANG, O AU, B KLIMOVA, J HYNEK & P HYNEK (eds), Proceedings : 2019 International Symposium on Educational Technology, ISET 2019., 8782210, Institute of Electrical and Electronics Engineers Inc., pp. 3-6, 2019 International Symposium on Educational Technology, Hradec Kralove, Czech Republic, 2/07/19. https://doi.org/10.1109/ISET.2019.00011

A talent assessment model based on learning behaviors and patterns. / XIE, Haoran; CAI, Yi; WONG, Tak-Lam; ZOU, Di; WANG, Fu Lee.

Proceedings : 2019 International Symposium on Educational Technology, ISET 2019. ed. / Fu Lee WANG; Oliver AU; Blanka KLIMOVA; Josef HYNEK; Petra HYNEK. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3-6 8782210.

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

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BT - Proceedings : 2019 International Symposium on Educational Technology, ISET 2019

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PB - Institute of Electrical and Electronics Engineers Inc.

ER -

XIE H, CAI Y, WONG T-L, ZOU D, WANG FL. A talent assessment model based on learning behaviors and patterns. In WANG FL, AU O, KLIMOVA B, HYNEK J, HYNEK P, editors, Proceedings : 2019 International Symposium on Educational Technology, ISET 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3-6. 8782210 https://doi.org/10.1109/ISET.2019.00011