The use of data or "data-based decision-making" (DBDM) in education has been an important research topic, as research showed it contributes to school improvement (e.g. Schildkamp, 2019) and under the right conditions to student learning (e.g. Lai et al., 2014). Research on effective data use underlines that data use is socially embedded. Collaboration is depicted as a prerequisite for DBDM (e.g. Schildkamp et al., 2015). However, authors such as Verhaeghe et al. (2010) mention that educators often don’t feel comfortable working with data and report that they don’t have the necessary skills to collaborate around data use. Rincon-Gallardo and Fullan (2016) put the concept of “outward brokering” forward as a way to make up for the lack of these skills: “When the problem of practice at hand requires expertise that falls beyond the capacity of the group (…) activating these outside connections can offer access to required expertise and new ideas” (p.16). Therefore, this study aims to explore how bringing together different kinds of educators and experts in a learning network can support educators in DBDM.

Brown and Poortman (2018) define professional learning networks as “any group who engages in collaborative learning with others outside of their everyday community of practice; with the ultimate aim of improving outcomes for children” (p.1).  The potential of networks lies in two domains. First, it gives access to resources, expertise and knowledge which would otherwise remain unconnected. Secondly, good ideas generated by individuals can be tested and further developed and flourish to innovations and solutions to complex problems, whereas otherwise they would remain in isolated classrooms (Rincon-Gallardo & Fullan, 2016). Although there is increasing attention for learning networks and its potential, De Lima (2010) warns that the implementation of networks “has traveled at a much faster rate than the research on their effectiveness” (p.2). We can see the same trend in interventions researching DBDM in education. They often implement a form of learning networks, yet there is little research on how the learning network influences DBDM-skills. This research aims to explore the different purposes of implementing learning networks in a DBDM-intervention and how it is believed to influence educators’ DBDM.

Expert interviews were set up to explore how learning networks can contribute to data-based decision-making. Expert interviews have numerous advantages in an exploratory phase of research (Van Audenhove & Donders, 2019). As there are learning networks in the field of DBDM, but little research on them, expert interviews give fast access to these unknown fields and a quick way to obtain information.  Van Audenhove and Donders (2019) define an expert interview as a “qualitative semi-structured or open interview with a person holding ‘expert knowledge” (p. 179). In this study, expert knowledge is the result of experience, education and scholarship, independent of the position or status a person holds. 24 international researchers were contacted with an interview invitation, based on their knowledge and experience with DBDM-interventions involving some kind of learning network. 14 researchers gave a positive answer to the invitation. Researchers who didn’t respond often were co-authors on projects of experts we did interview. We asked all 14 researchers which experts we should contact and no new research teams were suggested. A semi-structured interview protocol was developed based on a systematic literature review on DBDM and learning networks. The protocol contained questions on the general outline of the intervention, what the rationale was behind implementing learning networks, who was involved and what the perceived outcome was. The duration of the interviews was between 30 and 60 minutes. The interviews were conducted online and recorded. Afterwards they were transcribed and coded in NVIVO. The codes were derived both inductively and deductively. Intercoder reliability was measured to guarantee validity and consistency.

The preliminary results show that learning networks are believed to bring added value to DBDM in different ways. We identified four main reasons: regulation of motivation, sharing knowledge, co-creating solutions to problems, large-scale capacity building. First, PLN’s are believed to motivate participants. Participants are reassured when they hear colleagues from other schools commenting on similar struggles and problems and they are inspired to push through the difficult moments when learning more about how others have coped with similar situations. Second, PLN’s are reported to facilitate knowledge sharing. Educators in networks shared their problems and strategies and received feedback. The other participants provided a different perspective than the educators already involved in the schools’ data use proces. This is especially appreciated for smaller schools or schools in rural areas where the learning network provides teachers with the opportunity to share experiences and strategies with teachers in the same subject, which they might not have in their own school.. Third, PLN's are believed to contribute to the co-construction of new knowledge. When educators come together for joint work and deep-collaboration, different expertise and perspectives come together and new knowledge is co-constructed Fourth, experts report that PLN’s can help to obtain large-scale change. Educators in the learning network are trained in PLN’s so that they can support the professional learning of their own teams as knowledge brokers with regards to DBDM. It is referred to as a cost-efficient way to increase the impact to obtain larger-scale change. We can conclude that learning networks are often implemented by researchers in their DBDM-project and four different ways in which they contribute to DBDM are reported. We suggest further research to investigate under which conditions these four functions are reported by participants in PLN’s for DBDM and how these experiences actually contribute to DBDM.

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