More than just noise? A multi-disciplinary study of heterogeneity in school students’ perceptions of instruction

01/09/2020 to 31/08/2022

Instructional quality is a key driver of educational achievement and motivation (Kunter et al., 2013). Notwithstanding, students can differ considerably in their perceptions of the same teacher’s instruction (Bardach et al., 2019). This heterogeneity has been largely ignored by prior research and practical applications (e.g., students’ ratings of instructional quality to identify (in)effective teachers): They typically use class-level aggregates, i.e., the mean of the
ratings of all students in a class, and treat heterogeneity in students’ perceptions as ‘noise’. However, rather than noise, heterogeneous student perceptions of instruction could also arise for substantial reasons. For example, students enter class with different sets of characteristics, which might shape their perceptions of instruction or specific interactions between a teacher and individual students could take place, leading to divergent views of the students within a class. Hence, there is a clear need for research moving beyond the focus on the class mean.

The aim of this EFG is to provide fresh insights into heterogeneity by engaging in novel, innovative, and multi-disciplinary research approaches. We bring together researchers from the fields of education, personality psychology/behavioural genetics, sociology, developmental psychology, informatics, educational neuroscience, and statistics to discuss
and explore …
(1) reasons for heterogeneity in students’ perception of instruction, e.g.,
- individual student characteristics (ranging from family SES to personality, genetic
makeup etc.) and teacher characteristics
- interactions between teachers and students
- other social classroom processes
(2) implications of heterogeneity, e.g., for students’ learning progress
(3) methodological choices and challenges, e.g., regarding
- innovative designs (ranging from classroom research to laboratory studies; from
videos and social network data to polygenic scores),
- the refinement of existing statistical methods to capture the complexity of the topic
(4) ways to combine insights from different disciplines into an overarching theoretical
framework of heterogeneity, its sources, and implications