Mathematics Teachers’ Use of Content-specific Data for Dialogic Grouping

Group composition affects learning by individuals. Dialogic pedagogy approaches demonstrate that this is particularly true when each grouped student knows something others do not (i.e., mutuality grouping). Learning analytics can help grouping by providing teachers with data on students’ content-specific learning. What are mathematics teachers’ considerations in grouping students based on such data? We analysed fifty-three acts of grouping by nine mathematics teachers, who used data about students’ solutions to a mathematical task on linear functions to group students into pairs. We propose a schematic model including two types of considerations: interpersonal (here, mutuality, encompassing, and similarity) and content-specific (here, methods of construction, function orientation, and correctness). In this study, encompassing was the leading interpersonal characteristic, and function orientation was the leading content-specific characteristic. Moreover, different teachers formed the same groups using various considerations. The teachers utilised learning analytics to group students, and we modelled their grouping considerations.