Student Affects in Virtual Environments as Predictors and Outcomes: A Hierarchical Linear Modeling Approach

• In Event: Science and Technology

Abstract

The research of virtual environments and how they may assist learning--paired with student affect--has yet to be addressed through Heirarchical Linear Modeling (HLM) techniques. This research used a variety of demographic, affective and scientific inquiry assessment data within a scaled-up MUVE science activity. Findings suggest that self-reported affect (such as presence/immersion) did not play a role in positive student learning outcomes. Instead, other factors such as early detection of non-participation, or providing an inquiry skill foundation prior to the intervention, may be more important for learning than designing for engagement. Interactions between variables of gender, inquiry ability, computer use, gaming frequency, high-frequency actions, and minutes spent during activity are discussed.

Authors

• Brett E. Shelton, Boise State University

• Jody E. Clarke-Midura, Harvard University

• Andrew Walker, Utah State University