A Diachronic Investigation of Individual Differences and Writing Quality in Data-Driven Learning

This study examined variation in learners’ individual differences in data-driven learning, including foreign language enjoyment and anxiety, engagement, and autonomy, as well as learners’ writing quality and the impact of these individual differences on writing quality. Using a longitudinal mixed-methods design, this study tracked changes in the individual differences and writing quality of 15 English-major English-as-a-foreign-language undergraduates across 3 time points. Quantitative data were analyzed using linear mixed-effects models and post-hoc pairwise comparisons, while qualitative data were examined through thematic analysis. The results showed that the data-driven-learning approach contributed to a significant increase in learning autonomy and writing quality. Furthermore, learners’ foreign language enjoyment, behavioral engagement, and autonomy were found to be significant predictors of their writing quality in the data-driven-learning instruction. Findings in this study could enrich the understanding of how individual differences function in a data-driven-learning context, enabling teachers to develop more tailored data-driven-learning instructional strategies.