Linguistic characteristics of reflective states in video annotations under different instructional conditions

Published on Jul 1, 2019in Computers in Human Behavior4.31
· DOI :10.1016/j.chb.2018.03.003
Srećko Joksimović17
Estimated H-index: 17
(UniSA: University of South Australia),
Nia Dowell9
Estimated H-index: 9
(UM: University of Michigan)
+ 3 AuthorsArthur C. Graesser77
Estimated H-index: 77
(U of M: University of Memphis)
Abstract 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.
  • References (96)
  • Citations (2)
#1Dragan Gasevic (Edin.: University of Edinburgh)H-Index: 36
#2Negin Mirriahi (UNSW: University of New South Wales)H-Index: 9
Last.Srećko Joksimović (Edin.: University of Edinburgh)H-Index: 4
view all 4 authors...
Jun 1, 2015 in EDM (Educational Data Mining)
#1Scott A. Crossley (GSU: Georgia State University)H-Index: 27
#2Danielle S. McNamara (ASU: Arizona State University)H-Index: 50
Last.Yoav BergnerH-Index: 9
view all 7 authors...
#1Michelle Nicholas (Macquarie University)H-Index: 1
#2Penny Van Bergen (Macquarie University)H-Index: 7
Last.Deborah Richards (Macquarie University)H-Index: 20
view all 3 authors...
View next paperDigital video tools in the classroom: empirical studies on constructivist learning with audio-visual media in the domain of history