Exploration of Personalized Learning Paths for New Energy Vehicles Based on Knowledge Graphs

DOI

10.2991/978-94-6463-502-7_71How to use a DOI?

Keywords

New energy vehicles; Personalized learning paths; Knowledge graph; Educational technology

Abstract

With the rapid development of new energy vehicles, it has become an urgent task to cultivate talents with relevant expertise and skills. However, traditional teaching methods struggle to meet the personalized needs of learners, thus necessitating a personalized learning path based on knowledge graphs to provide customized learning support. This study aims to explore personalized learning paths for the field of new energy vehicles based on knowledge graphs and evaluate their effectiveness through empirical experiments. We conducted a series of empirical experiments to evaluate the effectiveness of the personalized learning paths based on knowledge graphs. The experimental results demonstrate that compared to traditional fixed learning paths, the personalized learning paths based on knowledge graphs can significantly improve learners’ academic performance, learning efficiency, and satisfaction.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qian Liu
AU  - Yanjuan Li
AU  - Kun Zhang
PY  - 2024
DA  - 2024/08/31
TI  - Exploration of Personalized Learning Paths for New Energy Vehicles Based on Knowledge Graphs
BT  - Proceedings of the 2024 5th International Conference on Education, Knowledge and Information Management (ICEKIM 2024）
PB  - Atlantis Press
SP  - 674
EP  - 680
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-502-7_71
DO  - 10.2991/978-94-6463-502-7_71
ID  - Liu2024
ER  -