Strategies for Cultivating Postgraduates' English Autonomous Learning Ability Under the Background of Big Data

Literature Review

With the continuous progress of technology and the deepening of education system reform, society's demand for graduate students' self-learning ability in English is increasing (Fan et al., 2014). At present, graduate students in universities are facing increasingly extensive and detailed English knowledge, but traditional school education still has certain limitations, and most students are unable to obtain extracurricular English knowledge (George et al., 2014). The concept of self-discipline or learner self-discipline belongs to the category of educational philosophy. Autonomous English learning refers to learners taking responsibility for their own learning during the learning process (Gill, 2007). Autonomous learning is essentially the result of complementary abilities and attitudes (Harford, 2014). Obviously, English self-directed learning ability is a multidimensional concept that includes at least three factors: ability, psychology, and behavior. Students with autonomous learning abilities have the ability to develop learning plans, execute them, evaluate and reflect on learning outcomes, and take on learning responsibilities (Hoff, 2003). Autonomous learning is achieved through the awareness of students, which is crucial for self-improvement of learning habits. In addition, due to the inherent individual differences among students, some of the factors that lead to these individual differences are immutable, while others can be compensated for through one's own diligence and hard work (Krause, 1964). In these situations, self-discipline in learning is also very important for students. Therefore, cultivating the autonomous learning ability of graduate students can be said to be one of the most effective methods to compensate for individual differences (Li et al., 2010). At the same time, there are some problems in current graduate English education that restrict the development of graduate English proficiency (Manathunga & Goozée, 2007).

In the context of big data, it is very convenient for graduate students to learn English independently (McAfee et al., 2012). Graduate students can learn English through the internet and leave traces and digital fragments in the process (McNitt-Gray et al., 2007). By tracking and analyzing these digital fragments, teachers can implement personalized teaching for students, subverting traditional vocabulary and grammar teaching methods (Pavlović et al., 2009). By using these digital fragments, students can have a more comprehensive and systematic understanding of their learning situation, while discovering and mastering specific learning patterns (Rampino, 2011). Instead of relying on limited judgment from teachers as before, they often find more suitable learning methods, adjust and improve learning strategies based on actual situations, and improve their English proficiency. In addition, under the background of big data, the content of English teaching resources is very rich (Reich, 1994). Graduate students can flexibly access teaching resources such as online teaching courses, lectures from renowned teachers, and self-study CDs (Riskin et al., 2006). By watching teaching videos, students can achieve online real-time learning and practice English reading and writing skills (Salite, 2015). Teachers can also utilize high-quality online teaching resources to edit courseware and enrich its content. In short, both students and teachers have benefited greatly from it (Soto et al., 2009).