Understand students' self-reflections through learning analytics

Abstract

Reflective writing has been widely recognized as one of the most effective activities for fostering students' reflective and critical thinking. The analysis of students' reflective writings has been the focus of many research studies. However, to date this has been typically a very labor-intensive manual process involving content analysis of student writings. With recent advancements in the field of learning analytics, there have been several attempts to use text analytics to examine student reflective writings. This paper presents the results of a study examining the use of theoretically-sound linguistic indicators of different psychological processes for the development of an analytics system for assessment of reflective writing. More precisely, we developed a random-forest classification system using linguistic indicators provided by the LIWC and Coh-Metrix tools. We also examined what particular indicators are representative of the different types of student reflective writings.