## 1 Introduction
### Study Objectives  
The primary goal of this research is to create and verify the Data-Driven Personalized Learning Model (DDPLM) that enhances foreign language acquisition with the help of utilization of the big data approaches. The main purpose is to show how personalized learning paths can be formulated and ensured to enhance learning outcomes in language. For this purpose, this study seeks to achieve several specific goals. The objectives of this study include determining the main variables that influence the progress and results of language learning from a big data point of view. This study aims to utilize the predictive capacity of big data to identify regularities and trends that will, in turn, guide more efficient learning strategies [5]. It includes demographic and linguistic information and interaction data from digital learning systems. To better understand the impact of demographic variables on language learning outcomes, Table 1 provides a demographic breakdown of the learners involved in the study. Table 1 provides a detailed demographic breakdown of the learners involved in this study, including age, gender, education level, and native language. These demographic variables offer a comprehensive view of the diverse learner population, allowing for more nuanced insights into how different backgrounds may influence language acquisition. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Demographic Variable** & **Age Range** & **Percentage of Learners** \\ \hline Age & 18–24 years & 35\% \\  & 25–34 years & 45\% \\  & 35+ years & 35\% \\ Gender & Male & 55\% \\  & Female & 45\% \\ Education Level & High School & 30\% \\  & Undergraduate & 50\% \\  & Postgraduate & 20\% \\ Native Language & Korean & 40\% \\  & Chinese & 60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic Breakdown of Language Learners. Figure 1: Framework for Integrating Big Data into Language Education. As described in Section 5.4, all data were collected in accordance with strict privacy protection protocols. Personal identifiers, such as names and student IDs, were removed to anonymize the data prior to analysis, ensuring the confidentiality of the learners. This study adhered to international privacy standards, including GDPR, and local regulations in both South Korea and China. The measures taken ensured that learner data were ethically managed while maintaining the integrity and reliability of the findings. This study seeks to develop an inclusive framework that integrates these variables into a single model [6]. The framework is meant to enable the development of adaptive learning environments that respond dynamically to the demands of every learner. The operational feasibility of DDPLM will be tested thoroughly with empirical data to establish its validity in diverse language learning contexts [7]. This study will focus on DDPLM's implications for present educational practices and policies. It provides practical details on how data-oriented methodologies can be seamlessly included in current educational models to produce more individualistic and effective learning patterns. This study seeks to analyze the scalability of the model under question. It will assess the applicability of DDPLM beyond diverse linguistic and cultural arenas to all scales of language learning activities, from small groups in classrooms to mega-sized platforms on the internet. This research intends to enrich the theoretical discussion on language learning and big data by integrating fresh findings into a coherent story. This would be the linking rubber between theoretical principles and practical actions and would thus bring the field of language education into the new digital era. Table 2 lists predictive factors identified through big data analysis and their correlation with language learning success. The correlations provided in Table 2 were calculated using Pearson's correlation coefficient, a widely used statistical measure to determine the strength and direction of the linear relationship between two variables.