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Analysis of the Correlation between Academic Performance and Learning Motivation in English Course under a Corpus-Data-Driven Blended Teaching Model

To explore the correlation between academic performance and learning motivation in English course under a corpus-data-driven blended teaching model, this study set research objects as 62 year-2020-enrolled undergraduate students majoring in English from a university in Jinan City, Shandong Province, eastern China. According to their previous frequencies of using information technology to learn English, these 62 students were divided into two groups: practice group with high frequency and control group with low frequency, with 31 students in each group. The two groups of students were taught 3 English lessons per week for a total of 15 weeks by the exact same teachers using a corpus-data-driven blended teaching model. The students’ English academic performances were assessed by well-organized final tests, and their English learning motivations were measured by a motivation scale and questionnaires. The results show that the correlation coefficients between the average score of motivation questionnaires, intrinsic motivation factors, extrinsic motivation factors, and the average score of academic performances in practice group were 0.894, 0.682, and 0.724, respectively, while those in control group were 0.749, 0.836, and 0.904. In all the above correlation analyses, the significance level is 0.01, and all coefficient values are higher than critical value. Hence, there is a positive correlation between learning motivation and academic performance of the two groups of subjects. It is found that the corpus-data-driven blended teaching model has a significant impact on college students’ English academic performance and learning motivation, and it has a positive effect on the improvement of their English academic performance and the cultivation of learning motivation. In general, the key to this teaching model lies in reasoning and acquisition by analyzing the language provided by the corpus, and the whole process of data-driven learning is student-centered. Students are exposed to a large number of authentic language knowledge and cultural information, which promotes the sensitivity to relevant points. The results of this paper provide a reference for further research on the analysis of the correlation between academic performance and learning motivation in English course under the corpus-data-driven blended teaching model.