How to build motivation during learning language for students
DOI:
https://doi.org/10.54097/ehss.v8i.4368Keywords:
Learning motivation; Social identity; Interest; Level and attitude of teachers and administrators.Abstract
This paper expounds on the causes of language learning motivation, and the degree to which it is affected by the causes of different dimensions and demonstrates the factors of language learning motivation, which not only includes the object field studied by pedagogy but also includes a discussion on the issue of educational justice in social sciences and humanities. Finally, it is concluded that learners, teachers, administrators, and social systems are needed to build motivation for students better to learn a language.
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Darvin R, Norton B. Investment and motivation in language learning: What's the difference? [J]. Language Teaching, 2021: 1-12.
Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.
SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.
Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
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