Predicting Student Inquiry Processes and Products: A Hierarchical Linear Modeling Approach

• In Event: Instructional Technology SIG Paper Session: Students’ Technology Experiences

Abstract

Several investigations of virtual environments have provided key insights into effective mechanisms to promote student learning. Building on those efforts, this research explores gaps in the literature through Heirarchical Linear Modeling (HLM) and data analytics with combination of inquiry process and inquiry product outcomes. For most tests, data supported use of HLM (ICC above 0.10), but multiple regression was also used when prudent. Findings suggest that self-reported affect (such as presence/immersion) did not play a role in predicting learning. Time spent and actions recorded in the game were consistent predictors of learning. Gender was an inconsistent predictor favoring male students for some process outcomes, female students for some product outcomes, and without significance for the bulk of the models.

Authors

• Andrew Walker, Utah State University

• Jody E. Clarke-Midura, Harvard University

• Brett E. Shelton, Boise State University