Preconditions of teachers’ collaborative practice: New insights based on time-sampling data - Maag Merki, Katharina; Grob, Urs; Rechsteiner, Beat; Rickenbacher, Ariane; Wullschleger, Andrea (Frontline Learning Research, 2022)
Previous findings on the preconditions of teachers’ collaboration are inconsistent. This might be related to the research methods used to assess the teachers’ collaborative practice. Retrospective assessments by self-report on a relatively general level prevail. The validity of these self-reports is limited, however. In contrast, time-sampling methods have the potential to investigate collaborative practice specifically and longitudinally as a day-to-day process over time validly. But to date, no research on collaborative activities in schools based on time-sampling methods is available. In this study, we extended the current state of research by analysing the variability and preconditions of teachers’ collaboration at four secondary schools over three weeks based on time-sampling data collected by a newly developed online practice log. Recorded were collaborative activities outside of teaching with a focus on administrative and organisational tasks and on school subject-specific tasks. The results revealed that teachers’ collaborative activities varied significantly between weekdays, showing a linear decrease from Monday to Friday, regardless of the content of collaboration. Collaboration that focused on administrative-organisational tasks seemed to be quite stable over the weeks and was hardly influenced by teachers’ individual characteristics. Instead, collaborative activities that focused on school subject-specific tasks varied significantly between weeks; moreover, they were influenced by teachers’ leadership role and gender. The results indicate that rather stable routinised patterns of day-to-day collaboration over the weeks decrease the influence of teachers’ individual characteristics. Hence, by collecting data that is closer to content-specific day-to-day collaborative activities, time-sampling methods can be seen as a driver for new insights.