Analysing preservice teachers' reflection journals using text-mining techniques. Issue 2 (28th September 2021) - Chen, Ye; Yu, Bei; Yu, Yihan
Reflection journaling is a common practice in teacher education. However, analysing large amounts of textual reflections presents challenges. Automatic analysis is needed so that teacher educators could quickly uncover valuable patterns and provide adaptive, real-time support. This study proposed a text-mining method to discover the important themes and patterns in preservice teachers' reflection journals. We also examined the potential text features through which the quality of reflection could be assessed. A total of 367 journals from 80 preservice teachers were analysed. The results showed that our text-mining method was able to accurately identify the weekly teaching focus and the themes in which participants had long-standing interest. We found that when participants engaged in higher-level of reflection, their journals achieved higher topic relevance to weekly teaching focus and they tended to write longer reflections. Based on the text-mining results, we further developed prediction models to automate the assessment of written reflections.