“Doing” mindsets in the classroom: A coding scheme for teacher and student mindset-related verbalizations

Abstract

There is a growing body of research showing the crucial role that students’ growth versus fixed ability-mindsets have in their school achievement, enjoyment, and resilience. The overwhelming majority of this research adopts a variable-oriented approach. As a result, little is known about how teachers and students co-regulate each other’s mindsets within classroom interactions. This manuscript addresses the need for more person-oriented research that examines how teachers and students do mindsets in naturalistic settings, i.e., their mindset-related verbalizations. In this manuscript, we provide a coding scheme to study the moment-to-moment dynamics of mindset-related verbalizations of both teachers and students within Science, Technology, Engineering, and Mathematics (STEM) contexts: The STEAM (Student-TEAcher-Mindset) coding scheme. We demonstrate the utility of the coding system through content and ecological validity, inter-rater reliability, and a case study of STEAM-generated time-series data. We show how these data can be used to chart moment-to-moment dynamics that occur between teacher and student. The coding scheme provides teachers and researchers with a practical tool for analyzing how person-specific mindset-related language can wax and wane in the context of peer and teacher interactions within STEM lessons.

Reading Struggle Stories of Role Models Can Improve Students' Growth Mindsets

“…As the difficulty of courses in college is higher than that in high school, the fixed mindsets of undergraduates may be higher than that of high school students. Moreover, the fixed mindsets of teachers also influence the mindsets of students (de Ruiter et al, 2020;Davison et al, 2021). Scherr et al (2017) paid attention to the fixed mindsets in physics graduate admissions and found that many teachers would choose students with a talent in physics so that they can be competent for more difficult graduate courses.…”

A process model of mindsets: Conceptualizing mindsets of ability as dynamic and socially situated.

“…A promising method for the observation of real life situational attributions and responses is behavioral coding. De Kraker-Pauw et al (2017) and De Ruiter et al (2020), for example, use coding schemes to organize categories of observable verbalizations and behavior that are performance versus learning-oriented. Quantitative analytical methods, such as timeseries analysis, can then be used to analyze how mindset relevant behaviors are organized temporally during social interaction.…”

“…Quantitative analytical methods, such as timeseries analysis, can then be used to analyze how mindset relevant behaviors are organized temporally during social interaction. De Ruiter et al (2020), for example, demonstrated that the moment to moment variability that teachers and students demonstrate (i.e., variability between learning and performance-behavior) can be synchronized across their interaction. Qualitative analytical methods, such as conversation analysis, could elucidate how students’ learning orientation is directed in social interactions, and how mindset information (e.g., feedback) is conveyed and understood (see Gardner, 2008; Koole, 2012).…”