## 2 Literature Review
### Review of Personalized Learning Systems  
Personalized learning systems are a significant paradigm shift in educational technology that seek to customize the learning process to a student's individual requirements and purposes. The development of big data analytics and artificial intelligence has significantly facilitated the progress and implementation of such systems. This part discusses the development of personalized learning systems, their influence on language learning, and the key role of big data in making these systems efficient.  
Early personalized learning systems were basic adaptive programs that changed task difficulty according to student performance. However, with the advancement of technology, these systems developed algorithms that could analyze complex learner data and deliver tailored learning experiences [19]. Nowadays, these systems use nearly all imaginable data points, such as real-time performance, learning preferences, engagement levels, and even emotional states, allowing them to dynamically adapt the selection of instructional content and methodologies [20].  
Big data plays several roles in personalized learning systems [21]. First, it allows for collecting and processing big sets of information from different sources, including digital books, online forums, and media files. This data integration offers a complete picture of the learner's engagement with the material and other students, which leads to a more detailed understanding of learning behaviors and preferences. As an example, interaction data can be used by language learning platforms to spot patterns in language acquisition, like frequent mistakes or topics that learners find especially difficult or amusing.  
Big data enables the development of predictive models to forecast learner needs. These models have the ability to predict learning barriers in advance and provide personalized interventions before the learner runs into trouble. Such curriculum personalization can be proactive in nature; for instance, if a learner tends to find some grammatical structures of a new language difficult systematically, the system will adjust the curriculum to provide additional practice and resources specific to that challenge.  
Big-data-powered personalized learning systems can increase motivation and engagement by linking educational content to the student's interests and professional goals. This is possible in language learning when you choose texts and activities that fit the purpose for which you intend to use the language, such as business, travel, or cultural studies. Such relevance enhances engagement and the practical use of the learning process.  
Collaborative learning is also a domain where personalized systems have made impressive progress. Big data analytics help these systems provide recommendations for peer learners with similar skills or interests, thereby enhancing the group learning experience. This functionality is particularly important in language education, where communicative and interactive skills are essential for developing proficiency.  
However, even after these developments, challenges persist in the larger implementation of personalized learning systems. Problems like data privacy, algorithmic bias, and the digital divide are significant obstacles to effectiveness and equitable access. Furthermore, the problem is how these systems can seamlessly integrate into traditional educational environments without disturbing the existing pedagogical processes.  
Language learning is one area that has benefited the most from personalized learning systems. Big data has become the heart of these advances, providing more interactive, engaging, and efficient educational processes. These systems are expected to further democratize education by making learning more accessible and personalized, although this will require the ongoing consideration of challenges stemming from these highly advanced methods.