Distributed Leadership in Personalized Learning: Reframing Data-Driven Improvement in Schools

• In Event: The Relationship Between Distributed Leadership and Student Learning

Abstract

Objectives
Distributed leadership describes the work of managing a complex social organization. Tasks are the unit of analysis for the work – tasks are stretched across people (social distribution) and across the policies, programs, and routines (situational distribution) of the organization (Spillane, Halverson & Diamond, 2004). One kind of distributed leadership analysis is to identify the key tasks in the organization, then explore who engages in the tasks and which structures enable (or constrain) task enactment (Authors, 2013).

Theoretical Framework
One of the key tasks in recent public school leadership an increased use of data to inform teaching and learning practices. In the early 2000’s, schools became enthralled with strategies to mobilize student outcome data for school improvement (Reeves, Forde, O’Brien, Smitt & Tomlinson, 2002). Data-driven leaders orchestrated cycles for data collection, reflection, design, and feedback (Authors, 2007). Leaders collected student and course outcome data and created representations that invited staff to redesign the instructional program to improve outcomes. Educators monitored student performance through formative feedback to measure student gains; and tweaked the curriculum to optimize progress. Leaders gathered educators annually to reflect on and adjust the program. Data-driven practices have come to define a significant chunk of 21st century school leadership (Mandanich & Jackson, 2012).

Methods and Sources of Data
Each of these tasks involves the adults in the building working to make improvements for students. In the 2010’s, educators have begun to shift toward data-driven leadership with students. In many schools, personalized learning has emerged as a whole-school reform model that gives learners more agency in their schooling. In prior work, we documented how students work with teachers to develop learning plans, scope out learning trajectories, and document their own learning (Authors, 2016). Personalized learning creates a complementary cycle for using data to improve learning with students.
Drawing on data from 15 schools implementing personalized learning, our paper uses a distributed leadership approach to examine student-led data processes work in personalized learning schools. We identified the task of conferring at the heart of this new, student-led cycle. Conferring is a regular time for teachers and learners to talk about the student’s interests and needs. During conferring, teachers and learners co-construct learning plans and share evidence of learning gains. Conferring generates rich evidence to complement traditional measures of student outcomes, and creates new, student-led cycles of data-driven improvement. Leadership in personalized learning is thus creating the conditions for conferring to take place.

Results
Our findings demonstrate how the distribution of leadership in personalized learning shifts to include students in the data-driven decision-making process; and we document how leaders create new situations (i.e. time schedules, conferring protocols and data-sharing technologies) to share student-generated data. The cycle of adult-based data-driven tasks continue, now complemented by a student-led learning process.

Significance
Distributed leadership allows us to document how schools are shifting toward this new paradigm of practice that includes the learner in the school improvement process.

Authors

• Richard R. Halverson, University of Wisconsin - Madison

• Alan Arthur Barnicle, University of Wisconsin - Madison

• Julie M Kallio, The Breck School