Characteristics analysis and cultivation strategies of non-common language students’ foreign language digital narrative ability based on big data mining

Abstract

This study aims to explore the characteristics and cultivation strategies of non-common language students’ foreign language digital narrative ability based on big data mining. Through the design and distribution of 800 questionnaires, 706 valid data were recovered and analyzed. Statistical methods such as Pearson correlation coefficient, Spearman grade correlation coefficient and regression analysis were adopted to deeply analyze the relationship between students’ use frequency of digital narrative tools, self-assessment of foreign language digital narrative ability and learning motivation. The results show that the use frequency of interactive learning tools and collaborative platforms has a significant positive correlation with students’ speaking and writing ability. Students’ learning motivation also showed a positive correlation with their frequency of using digital tools and their foreign language learning effectiveness. Based on these findings, this study suggests that educators should more widely integrate digital narrative tools into foreign language teaching in non-common languages, and design teaching strategies that stimulate students’ intrinsic learning motivation. This study not only provides empirical research support for the foreign language education of non-common language students but also puts forward useful strategies and suggestions on how to effectively use digital tools to promote students’ foreign language learning. Future research can further explore other influencing factors to provide more comprehensive teaching guidance.