Machine Learning Empowering Language Acquisition: The Value and Application Path of Learning Motivation
DOI:
https://doi.org/10.62051/jedss.v1n2.07Keywords:
Machine learning, Language acquisition, Learning motivation, Self-determination theory, L2 Motivational Self System, Willingness to communicate, Adaptive learning, NLP, Intelligent tutoring system, Digital divideAbstract
Machine learning (ML) has shown some good results in second language (L2) education, so many adaptive learning systems, intelligent tutoring platforms and other natural language processing (NLP) tools have been developed. The above technologies will help improve the efficiency of learning and inspire students to study more actively. Machine learning can create personalised learning paths for students, and by the means of a conversation-based system, motivate them more intrinsically; NLP can then be used to provide students with comprehensible input to maintain their motivation as learners. Self-determination theory (SDT), the L2 Motivational Self System (L2MSS), and Vygotsky's zone of proximal development (ZPD) provide theoretical support for studying the effect of machine learning on students' motivation to learn Chinese as a second language. At the same time, there are also the problems of technology anxiety, algorithmic bias and the digital divide in the use of this technology. Based on the previous studies, this paper proposes a motivation-oriented framework for integrating ML tools in L2 teaching.
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