Journal writing is an important and common reflective practice in education. Students' reflection journals also offer a rich source of data for formative assessment. However, the analysis of the textual reflections in class of large size presents challenges. Automatic analysis of students' reflective writing holds great promise for providing adaptive real time support for students. This paper proposes a method based on topic modeling techniques for the task of themes exploration and reflection grade prediction. We evaluated this method on a sample of journal writings from pre-service teachers. The topic modeling method was able to discover the important themes and patterns emerged in students' reflection journals. Weekly topic relevance and word count were identified as important indicators of their journal grades. Based on the patterns discovered by topic modeling, prediction models were developed to automate the assessing and grading of reflection journals. The findings indicate the potential of topic modeling in serving as an analytic tool for teachers to explore and assess students' reflective thoughts in written journals.