| Sumario: | Video-based self-reflection and annotation is receiving increasing attention within the education literature. The importance of such technologies in education relate, in part, to the interactive nature and functionality these tools bring to aid learning engagement. In particular, these tools are well aligned with the need to promote and develop student meta-cognitive skills through the use of self-reflection activities. However, in the context of video-based learning environments, the nature of a students' self-reflective process is not well understood. We attempt to address this gap in the literature in two main ways. First, we developed a coding instrument to assess the depth of a students' self-reflection captured through the use of a video annotation tool. We then explored the linguistic and discourse properties of the student self-reflections using Coh-Metrix, a theoretically grounded computational linguistics facility. The adopted approach applies comprehensive analysis of language and discourse features associated with the specificity of students' internal self-feedback , which is externalized as self-reflections in video annotations. The results suggest that levels of self-reflection have characteristically different linguistic properties, and these differences align with the underlying cognitive mechanisms associated with distinct reflective activities. The paper provides a detailed discussion of the findings in the context of the theoretical, methodological, and practical implications associated with video-based self-reflection and video annotation. • Self-reflection and annotation are important strategies in video-based learning. • We develop a coding instrument to assess the depth of a students' self-reflection. • We explore the linguistic and discourse properties of the student self-reflections. • The levels of self-reflection have consistently different linguistic properties. • The findings also highlight the importance of effective learning design.
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