Professional Learning Communities: Practices That Can Inhibit or Enhance Data-Driven Decision-Making
by Jacob Elmore - June 27, 2017
This commentary analyzes practices in PLCs that can inhibit or enhance teachers’ learning about students and their data-driven decision-making.
Research clearly shows the impact Professional Learning Communities (PLCs) can have on not only teacher learning, but also student learning (DuFour & Reeves, 2016; Little, 2012; Vescio et al., 2008). As teachers continue to learn and grow in their practice, multiple forms of professional development or pedagogies can be used to facilitate this learning. PLCs, in particular, have gained favorable attention in the research literature and educational practice over the last 30 years as a method for professional development (DuFour, 2014). A PLC is defined as a community of teachers who engage in work collectively to analyze a variety of topics. The foci of PLCs include (but are not limited to) instruction, curriculum, student data, and assessment methods. While all of these topics are important and need to be integrated in a PLC, student data should be the driver for all of the other work. While teachers engage in work that involves topics such as assessment or instruction, they can also benefit and learn together (Little, 2012), but certain structures need to be in place for this to happen and some practices and behaviors can hinder the process of teachers working and learning effectively in a PLC. In this commentary, Ill be analyzing practices that inhibit or enhance the learning and work of data-driven decision-making within a PLC.
There are several key practices that hinder PLCs and limit opportunities for teachers to work and learn together. The broad practices Ill be speaking to include: not using data as the forefront in a PLC, having unstructured or no protocol, and not meeting consistently as a PLC. While Ill be speaking to these practices that inhibit PLCs, Ill also be talking about methods and practices that can promote the learning and work in a PLC under these conditions.
The most effective PLCs use data as the forefront of conversation and collective decision-making (DuFour & Reeves, 2016; Jacobson, 2010). Without data, a PLC will suffer from ample opportunities of learning and getting work done. Often times, teachers will engage in work that looks and feels like a PLC: they will talk about instruction and curriculum, which includes planning lessons, making anchor charts, or copying instructional materials; but this really represents a PLC lite (DuFour, 2014). While all of these practices are needed, teacher and student learning will be on the back burner.
I argue that data is the driver in a truly effective PLC (DuFour & Reeves, 2016). Instead of just planning lessons or copying instructional materials, teachers need to put data at the pinnacle of PLCs. An example of this could include using data at the beginning of the meeting to facilitate the conversation and decision-making in regards to instruction, unit planning, and the development of instructional materials. When analyzing data, teachers need to group and organize students by skill and level of mastery, and design a set of interventions for students who need extra support or challenge. As new units arise with new academic standards and skills, students need continuous support and interventions to work toward mastery. When doing this work, teachers need to ask themselves, Where are my students currently at with these skills? Who needs supports, and what supports do they need? Currently, there has been a push in schools and school districts for standards-based work (Hochbein & Pollio, 2016). This includes planning, assessing, and evaluating student work using a frame of standards. PLCs are capable of achieving such work, but data has to be the driver.
Another ill-advised practice is using an unstructured protocol or no protocol at all while involved in a PLC. A protocol is a tool teachers can use to keep their PLC focused and efficient (Allen & Blythe, 2015), and they are not simply meeting notes. When teachers either dont use a protocol, do not stick to one, or have one that is loosely structured, conversations could lead to topics other than data-driven decision-making. These could be conversations of war stories or topics not related to student learning or the work. It can also just be conversations that have an imbalance between analysis-oriented and action-oriented work described in more detail below.
A protocol keeps teachers on track with the work at hand. By having data at the forefront, a well-structured protocol can complement the balance between analysis-oriented and action-oriented work (Levine & Marcus, 2010). Analysis-oriented work consists of interpreting data and making meaning of student learning and misconceptions. For example, a group of 3rd grade teachers in a PLC first spend their time analyzing interim (benchmark) assessment scores for 10 minutes quietly. After analyzing the data, each of the teachers can share ideas for possible types of mistakes or misconceptions their students, or subgroup of students might hold. After identifying different possible mistake types or misconceptions, this group of teachers can then spend time with action-oriented work which could then be followed by: creating subgroups of students for intervention supports, creating common formative assessments to monitor student work, and creating an agreed upon criterion that indicates when students have demonstrated mastery on a given skill or standard. Protocols are a powerful tool that can create opportunities for data-driven decision-making.
The last practice that can hinder the work in a PLC is spontaneously or infrequently meeting. The work of PLCs are complex and require a significant amount of time. The payoffs of student learning, however, are worth it. Teachers are frequently gathering data about their students day in and day out. Without having frequent and regular meetings with colleagues, this leads to a practice still common to this dayworking in isolation. When teachers are working in isolated, closed-door settings, opportunities for learning are missed. This is not to say teachers cant support their students or get work done alone, but the power of a PLC can amplify this work. Here is an example: after a day of teaching, three 7th grade ELA teachers, Mr. X, Ms. Y, and Ms. Z, sit down and begin their bi-weekly PLC. As they are analyzing the data from their formative assessments on thesis statements, Mr. X and Ms. Y point to Ms. Zs data. Mr. X states, Almost all of your students met the criterion for their thesis statements. Ours are not close. Compared to yours, how did you do that? Right here, an action-oriented conversation can begin by Ms. Z sharing with Mr. X and Ms. Y what instructional strategies and moves she used by telling and modeling how she got her students towards the mastery level of the skill of writing a thesis. After listening, watching, and gaining insight into this practice, something special may happen: Mr. X and Ms. Y may decide to modify their practice for the next lesson to support their students with this skill. While this is just a simple example, and doesnt always work so neatly, data-driven decision-making in PLCs can have a significant impact on teacher learning. However, as mentioned above, this work cannot be achieved without certain structures in place.
The structures needed for a PLC to develop and maintain include actions taken by teachers, along with school and district leadership. For teachers, this includes going to PLCs ready with all of the materials and data at hand to analyze and use with their colleagues. Teachers also need to follow and engage within PLCs with fidelity and trust for desired student learning outcomes. For school administrators, this includes crafting and supporting a system for accountability and fidelity of the work of data-driven decision-making PLCs. This can take the form of gathering written protocols and analyzing them, along with attending PLCs by being involved and doing the work alongside their teachers. For district leadership, this can include creating systems of dedicated PLC time at a district level. This can also include providing professional development and resources for both teachers and principals to use to build capacity within PLCs and develop a culture of data use (Gerzon, 2015).
Data-driven decision-making in PLCs will not revolutionize all of the needs and challenges that educators are currently facing today in US education. However, PLCs that put data at the forefront, are structured using protocols, and meet frequently can improve learning outcomes for all students.
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