The Language of Engagement in Math Instructional Video Tutorials: A Corpus-Based Study
Abstract
This dissertation investigates the linguistic features of engagement in spoken academic online and face-to-face instruction in mathematics on two platforms: Khan Academy and MIT OpenCourseWare. In particular, the study analyses involvement features (personal pronouns and deixis) and interactional features (response elicitors, direct hypothetical reported speech). Using corpus linguistics methodology and register analysis framework (Biber &Conrad, 2009), I investigated normed frequency of occurrence for these features and multi-word expressions which contain them to reveal patterns of use. Additionally, I investigated the function of these features in concordance lines to reveal their use to engage audience in the learning process. The findings of this study suggests that Khan Academy instruction in mathematics relies on using conversational and academic spoken features similar to those found in the MIT lecture corpus, including frequent use of personal pronouns (especially we), and response elicitors (right?). The format of online video instruction also elicits more use of spatial deixis to draw attention to the elements on the virtual board. The findings of this exploratory study add to the growing literature on language used for educational purposes in online environments, especially the online academic spoken discourse.
Degree
Ph.D.
Advisors
Silva, Purdue University.
Subject Area
Linguistics|Mathematics education|Computer science|Education|Educational technology|Mathematics
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