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.
|Title of host publication||Proceedings : 2019 International Symposium on Educational Technology, ISET 2019|
|Editors||Fu Lee WANG, Oliver AU, Blanka KLIMOVA, Josef HYNEK, Petra HYNEK|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|Publication status||Published - Jul 2019|
|Event||2019 International Symposium on Educational Technology - Hradec Kralove, Czech Republic|
Duration: 2 Jul 2019 → 4 Jul 2019
|Conference||2019 International Symposium on Educational Technology|
|Abbreviated title||ISET 2019|
|Period||2/07/19 → 4/07/19|
Bibliographical noteThe 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).
- Fuzzy model
- Learning behavior
- Learning pattern
- Talent assessment