Abstract:

Introduction:
Data-informed decision-making (DIDM) is widely viewed as a powerful driver of school improvement and student achievement, but many schools and districts continue to struggle with implementing effective data use routines. Because data use is complex, there is an emerging consensus that it is best undertaken collaboratively. As a result, major investments are made in creating and supporting collaborative data structures such as data teams, professional learning communities, and research-practice partnerships in education. However, the lack of clear articulation of the mechanisms and processes through which such data-based collaborations may facilitate institutionalization of data use routines is currently impeding efforts to rigorously assess their efficacy. This presentation introduces the capacity-opportunity-motivation (COM) model as means for tracking and evaluating the mechanisms and processes that can support effective data-based collaborations.

Methodology:
An integrative review of the research utilization literature on data use in teams published over the past decade (N = 67 original and review articles) was conducted to extract the key COM components and mechanisms that can productively guide the design, implementation, and evaluation of collaborative data use interventions.

Results:
At the level of the individual team member, data use was most frequently conceptualized and operationalized as the cognitive process of transforming data into actionable knowledge. A person’s capacity, opportunity, and motivation to use data can therefore be predicted from the same set of theoretical constructs that explain human behavior more generally, including data literacy and data competencies (capacity); expectancies, self-efficacy, response efficacy, perceived barriers, and perceived norms related to data use (motivation); and data access, workflow integration, and external incentives for using data (opportunity). Team processes, in contrast, involve social interaction processes (e.g., group communication patterns) and the emergent perceptual states that result from these processes (e.g., cohesion, trust, shared accountability, and collective efficacy) that are necessary to support effective data-based collaboration. Team capacity to collaborate on DIDM was consistently found to be a function of team composition, leadership, role knowledge, and conflict management style. Opportunities for team collaborations were generally found to be a function of organizational arrangements and resources as well as of the degree to which data use is integrated with workflow and professional routines. Available measures of individual and team-level COM variables were compiled as part of this project.

Conclusions:
The COM model is a useful framework for evaluating DIDM-based collaborations in education for a number of reasons: it places “data use” on a behavioral continuum that can inform the design and implementation of interventions and collaborative structures that are optimally tailored to educators and organizations; it unpacks the “black box” that connects program inputs to outcomes, thus allowing to track, monitor, and evaluate progress on goals; it can be used to diagnose the element (capacity, motivation, or opportunity) that can benefit the most from targeted investments; and it paves the way for the use of rigorous methodologies and measures to evaluate data use.