Learning through online peer discourse: Structural equation modeling points to the role of discourse activities in individual understanding

Abstract

Although learning through discourse activities seems well-documented, it is unclear which mechanisms and behavioral variables are involved. What exactly contributes to learning when two or more learners interact in online learning environments? To analyze interrelations between central discourse activities and individual learning outcomes at the level of constructs, we applied structural equation modeling to data collected from 160 dyads engaging in written online learning discourses within a series of homogeneous experiments. We analyzed three theory-based indicators of conceptual elaboration activities during online discourse: the number of questions asked to receive information and expand knowledge, the number of explanations formulated to express individual knowledge, and the amount of on-task discourse. Individual conceptual understanding was represented by objective learning parameters that varied in each particular experimental task. These measured general understanding of the topic addressed and particular understanding of conceptual terms, complemented by the gain in individual self-assessed knowledge. Results of structural equation modeling revealed a strong effect of dyadic conceptual elaboration on individual understanding at the construct level, demonstrating that dyadic elaboration fosters the development of an elaborated individual understanding of specialist concepts and general content knowledge. Moreover, conceptual elaboration was best measured by the number of explanations during discourse. Implications regarding which features of collaborative learning settings promote mutual conceptual elaboration are discussed. ► We examine indicators of learning in online discourse. ► Structural equation modeling revealed strong effect of dyadic conceptual elaboration on individual conceptual understanding. ► Conceptual elaboration is best measured by explanations. ► Features for computer-mediated collaborative learning settings are discussed.