Automated Analysis of Text in Student-created Virtual Reality Content

Assessments of digital maker activities increasingly rely on automatically analyzing student-created products and their components, such as their textual output. In particular, recent learning analytics research has proposed incorporating text analytic feedback for facilitating students' virtual reality (VR) content creation, though lacking direct empirical evidence from student-created artefacts. Thus, this study examined the relationships between metrics on text in student-created VR content and their learning performance. VR narration scripts and performance scores were collected from 102 students in a maker-based general education course. Results of statistical testing and text mining show that high and low-performing students demonstrated significant differences in such metrics as word counts, vocabulary sizes, and frequent unigrams and bigrams. This study makes methodological and practical contributions in the domains of maker education and learning analytics.