Qualitative analysis of video data: Standards and heuristics

Kay E. Ramey, Dionne N. Champion, Elizabeth B. Dyer, Danielle T. Keifert, Christina Krist, Peter Meyerhoff, Krystal Villanosa, Jaakko Hilppö

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Video research is an increasingly important method in the learning sciences. Video provides unique analytical affordances to researchers but also presents unique tensions, many of which have not yet been adequately addressed in the literature. The authors of this symposium draw on their diverse experiences, analyzing a variety of video corpuses, to provide theoretical and methodological standards and heuristics for the process of video analysis. We focus on three themes central to the process of video analysis that would benefit from increased theoretical and methodological attention: transcription tensions, defining the unit of analysis, and representing context. We discuss how our approaches to video analysis are framed by theory and how we have applied them to specific datasets, to answer a variety of research questions. In doing so, we make explicit some crosscutting methodological norms and invite continued discussion about these norms from multiple analytic traditions.

Original languageEnglish (US)
Title of host publication12th International Conference of the Learning Sciences, ICLS 2016
Subtitle of host publicationTransforming Learning, Empowering Learners, Proceedings
EditorsChee-Kit Looi, Joseph L. Polman, Peter Reimann, Ulrike Cress
PublisherInternational Society of the Learning Sciences (ISLS)
Number of pages8
ISBN (Electronic)9780990355083
StatePublished - 2016
Externally publishedYes
Event12th International Conference of the Learning Sciences: Transforming Learning, Empowering Learners, ICLS 2016 - Singapore, Singapore
Duration: Jun 20 2016Jun 24 2016


Other12th International Conference of the Learning Sciences: Transforming Learning, Empowering Learners, ICLS 2016


  • Analysis
  • Data representation
  • Methods
  • Qualitative
  • Video

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Education


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