SEM02-How to Investigate Connected Learning Environments on Campus (separate registration is required)

Monday, February 03 | 9:00AM–12:00PM | Jasperwood
Session Type: Professional Development
What do faculty and students experience in connected learning environments? How can stakeholders better understand and describe the pedagogical, cultural, intellectual, institutional, and technical characteristics of these complex and unique spaces? How can stakeholders implement evaluation programs that allow them to first learn what to attend to, and then to gather both deep and broad information on the resulting themes in an organized, meaningful, and context-appropriate way?


Participants will discuss-and participate in a simulation of-a mixed-methods research process that begins with ethnographic tools to develop emergent themes that then lead to quantitative data gathering to explore broad applicability across the study population. The organizers will review their recent study of one connected learning environment, Michigan's Design Lab 1, including observation and time-lapse image capture, interviews, focus groups, thematic analysis, and surveys (see http://www.scup.org/page/scup_phe/v42n1/observations). Participants will explore adapting and applying this approach in their own local contexts. The session will close with a discussion of how these methods can inform ongoing design processes for both existing and new connected learning environments.


OUTCOMES: UUnderstand what constitutes a connected learning environment as defined by the current literature and leading work as well as by the session participants themselves * Understand the different research tools used, how they inform and support each other, and how they can be adapted * Understand how a coherent sequence of mixed methods can inform the emergence of research questions in an ongoing connected learning environment design process

Presenters

  • Linda Knox

    Learning Design Librarian, University of Michigan-Ann Arbor
  • Steven Lonn

    Director of Data, Analytics, and Research, University of Michigan-Ann Arbor