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author:

Lin, Yi (Lin, Yi.) [1] | Wang, Shunbo (Wang, Shunbo.) [2] | Lan, Yangfan (Lan, Yangfan.) [3]

Indexed by:

EI

Abstract:

Learning style is the endogenous cause of students' unique behaviors when they are performing learning tasks. The adaptive learning system that considers learning style can provide a personalized experience to stimulate students' enthusiasm, which had been widely studied in recent years. However, most of such existing systems are constructed based on a desktop environment, which leads to the less-than-ideal effect of personalized learning due to the limitation of interaction means and environmental dimension. Therefore, an adaptive virtual reality learning method based on the learning style model was proposed in this study. This method continuously iterated the identification of learning style based on students' subjective and objective data. Then, the content of virtual learning environment was sustainedly adjusted according to the identification results, thus enabling the environment to dynamically adapt to students' learning styles. To evaluate the feasibility of this proposed method, a controlled experiment on 152 participants was conducted. Results show that this method obtained relatively stable and accurate results of learning style identification, with an accuracy range of 74.38%–80.30%. Moreover, learning in the virtual environment constructed based on this method had a positive impact on students' learning motivation and outcomes. © 2021 Wiley Periodicals LLC

Keyword:

Computer aided instruction Education computing E-learning Learning systems Students Virtual reality

Community:

  • [ 1 ] [Lin, Yi]College of Physics and Information Engineering, Fuzhou University, Fujian, Fuzhou, China
  • [ 2 ] [Wang, Shunbo]College of Physics and Information Engineering, Fuzhou University, Fujian, Fuzhou, China
  • [ 3 ] [Lan, Yangfan]College of Physics and Information Engineering, Fuzhou University, Fujian, Fuzhou, China

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Source :

Computer Applications in Engineering Education

ISSN: 1061-3773

Year: 2022

Issue: 2

Volume: 30

Page: 396-414

2 . 9

JCR@2022

2 . 0 0 0

JCR@2023

ESI HC Threshold:61

JCR Journal Grade:2

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 13

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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