Article Preview
TopIntroduction
The field of second language acquisition (SLA), which gained prominence in the late 1960s and early 1970s, focuses on how individuals acquire a second language within its social context, assuming prior competence in their native language (Clahsen & Felser, 2006). A deep understanding of SLA is essential for developing effective teaching methodologies. However, there is no consensus on the influence of age on this process. Saito and Ebsworth (2004) emphasize the critical role of learners as active participants, highlighting the importance of examining individual factors, such as motivation, which significantly impacts second language learning (Panagiotidis et al., 2023; Rahman et al., 2017).
Debates continue about the optimal age for initiating second language instruction, with some advocating for an early start due to better acquisition outcomes, while others argue that adults, despite superior cognitive skills, may struggle to achieve native-like fluency (DeKeyser, 2012; Piller, 2002). The exact effects of age on language acquisition remain contentious (Elsherif et al., 2023). Meanwhile, the integration of emerging technologies like cloud computing, artificial intelligence (AI), and big data is transforming education, enhancing automation, efficiency, and decision-making (Kumar, 2024; Neti & Parte, 2023; Ryzko, 2020). Big data, in particular, is revolutionizing education by informing and optimizing teaching and assessment methods (Carroll, 2023; Manire et al., 2023; Rukiati et al., 2023; Shreeve, 1995).
This study explores the intersection of age, motivation, and technology in SLA, aiming to bridge the gap between traditional views and the benefits of advanced technologies. It examines how age influences learning in smart classrooms, the role of motivation across different age groups, and the potential of big data and data mining to improve personalized learning experiences. By synthesizing relevant literature, including autonomous learning theory (Kononenko, 2010), this paper analyzes English teaching strategies through the lens of educational big data, leading to the design of a big data-based English teaching system. This work aims to provide a theoretical foundation for enhancing English language education, making a significant impact on the field of SLA and global education.
This study is significant and original as it addresses the optimal age for SLA by providing empirical evidence from a technologically enhanced environment. By using big data and data mining, it offers personalized and adaptive learning solutions that can transform traditional education globally. The findings will inform more effective and inclusive language teaching, benefiting educators, policymakers, and learners worldwide.
The research explores how age, motivation, and technology intersect in SLA, bridging the gap between traditional views and the benefits of advanced technologies. It examines the influence of age on learning in smart classrooms, the role of motivation across different age groups, and the potential of big data and data mining to improve personalized learning experiences.
By providing these insights, the study aims to enhance language instruction in various educational settings, supporting more effective, engaging, and equitable language learning. This research contributes to a deeper understanding of SLA and has practical implications for global education.