Analyzing Patterns of Emerging Understanding and Misunderstanding in Collaborative Science Learning: A Method for Unpacking Critical Turning Points

Abstract

When students learn science in a computer-supported, collaborative, delayed-instruction environment, how does understanding (and misunderstanding) emerge? Are there patterns in the pivotal moments when emerging understanding turns for the better or worse? While components such as modeling software, delayed instruction methods such as productive failure, and analogical-encoding methods such as contrasting cases have all been shown effective at supporting deep learning in science, little is known about the micro-level mechanisms explaining how and why students might be more or less successful when working in an environment combining all three. This paper details our refinements of an innovative method for unpacking the micro-level mechanisms contributing to turning points in the successes and failures in collaborative understanding when learning science with computer modeling. In unpacking our methodology, we discuss work including Sanderson and Fisher’s (1994) exploratory sequential data analysis (ESDA) guidelines and the productive multivocality project (Suthers, 2013) to frame our approach.