• Login
  • Register
  • Search

Construction of College Students' Adaptive Learning System Based on Knowledge Map

Yahong Ma, Jianxin Guo, Shanwen Zhang, Changqing Yu, Jing Li

Abstract


The individualized learning method of teaching students in accordance with their aptitude is the ultimate pursuit of education. The outbreak of the COVID-19 has prompted online teaching to quickly become a hot spot in current education. The existing online teaching mode has formed a good foundation in teaching resource sharing and basic teaching activities. However, compared with the traditional face-to-face teaching, there are also some problems, such as the teaching efficiency is not high enough and students' personalized learning can not be well satisfied. Firstly, this paper analyzes the current situation and advantages of online education, and according to the characteristics of private college students, takes the core courses of electronic information major as an example to build a personalized adaptive learning system model to provide a basis for the continuous development of online education.


Keywords


Knowledge Map; College Student; Adaptive Learning; System Construction

Full Text:

PDF

Included Database


References


Song D, Feng X, He H, Wang N. Research on adaptive learning mode driven by knowledge map and education big data [J]. Research on higher engineering education 2022;(01):163-168.

Yang J, Du X, Li H. Research on the construction of educational knowledge map model in adaptive learning system [J]. China education Informatization 2021;(24): 24-29.

Chen M, Xu T, Xu Z. Research on adaptive learning system based on knowledge map [J]. Modern teaching 2020; (Z3):19-21.

Zhong Z, Yang Y, Zhong S, Zhao Y. Research on the construction of educational knowledge map model supported by artificial intelligence [J]. E-education research 2020;41(04):62-70.

Zhu Y, Fan Y, Zhao Y. Construction of knowledge model of adaptive learning system based on knowledge map [J]. Journal of Jilin University (Information Science Edition) 2018;36(03):345-350.




DOI: http://dx.doi.org/10.18686/ahe.v6i7.4837

Refbacks