Adapting for Scalability: Automating the Video Assessment of Instructional Learning

Authors

  • Amy M Roberts University of Virginia
  • Jennifer LoCasale-Crouch University of Virginia
  • Bridget K Hamre University of Virginia
  • Jordan M Buckrop University of Virginia

DOI:

https://doi.org/10.24059/olj.v21i1.961

Keywords:

Automated assessment, scalability, teacher education

Abstract

Although scalable programs, such as online courses, have the potential to reach broad audiences, they may pose challenges to evaluating learners’ knowledge and skills. Automated scoring offers a possible solution. In the current paper, we describe the process of creating and testing an automated means of scoring a validated measure of teachers’ observational skills, known as the Video Assessment of Instructional Learning (VAIL). Findings show that automated VAIL scores were consistently correlated with scores assigned by the hand scoring system. In addition, the automated VAIL replicated intervention effects found in the hand scoring system. The automated scoring technique appears to offer an efficient and reliable assessment. This study may offer additional insight into how to utilize similar techniques in other large-scale programs and interventions.

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Published

2017-03-21

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Section

Students, Community, and Online Learning