How to use everyday data in new ways

Schools and classrooms are overflowing with information about students and their learning, and teachers continually collect and respond to evidence of student learning in a variety of ways (Tarasawa, Gotwals, and Jackson 2018). [There’s] evidence that supports the use of everyday data as a source of information about where students are in relation to learning targets and how that data can be used to help teachers and students themselves identify next steps.

Research base for using everyday data in new ways

While test scores are one form of data, and perhaps the first type of data that comes to mind, everyday data can be gathered from the questions students ask, the dialogue between students as they collaborate, students’ responses to questions, and written student work. This type of data can be invaluable in supporting students’ day-to-day learning.

Holistic information about students such as extracurricular activities, interests outside of school, and attendance patterns also constitute data that educators can use to get to know their students. Students may be more likely to invest the effort needed to improve when their teacher has gotten to know them and has built trust (Wiliam and Leahy 2015, 108). The following sections describe how educators use dialogue, student work, and student self-assessment as data to improve learning and how to create contexts to support use of everyday data.

Dialogue as data

Verbal and written responses are rich sources of information regarding where students are in relation to the learning target. Black and Wiliam (2018, 560) advocate for teachers to “steer a learning dialogue” to elicit student thinking; they view oral classroom dialogue as the core of formative assessment. Drawing on the principles of Rosenshine (2010, 12), Sherrington (2019, 28–30) identifies a number of questioning strategies intended to solicit information regarding how well students have absorbed the content taught.

Similarly, the concepts of noticing in mathematics and ambitious teaching in science focus on eliciting students’ ideas and using those ideas to frame instruction (Tarasawa, Gotwals, and Jackson 2018). Researchers van Es and Sherin (2002, 573) describe three key aspects of noticing:

  • identifying what is important or noteworthy about a classroom situation
  • making connections between the specifics of classroom interactions and the broader principles of teaching and learning they represent
  • using what one knows about the context to reason about classroom interactions

The term ambitious teaching is used to convey an approach that elicits and supports all students’ thinking for the purpose of ongoing sensemaking while students participate in learning activities (Ball and Forzani 2011, 19; Lampert and Graziani 2009, 492; Stroupe and Gotwals 2018, 296; Windschitl, Thompson, and Braaten 2012, 879). Noticing and eliciting students’ thinking are ways of gathering everyday data that can be used to improve instruction.

In an article that addresses the tensions between misconceptions research and constructivist views of learning, Smith, diSessa, and Roschelle (1994, 150) describe the role of eliciting students’ thinking in the learning process as follows: “We still need to have students’ knowledge—much of which may be inarticulate and therefore invisible to them accessed, articulated, and considered…. Instruction should help students reflect on their present commitments, find new productive contexts for existing knowledge, and refine parts of their knowledge for specific scientific and mathematical purposes. The instructional goal is to provide a classroom context that is maximally supportive of the processes of knowledge refinement.”

School leaders can encourage the use of data by framing the process as supporting continuous improvement, rather than by emphasizing accountability, and can use their own data literacy skills to monitor, model, scaffold, guide, and encourage the use of data.

Eliciting student thinking is a way for teachers to gather information, enabling them to respond in ways that enhance ongoing learning (Klenowski 2009, 264).

Student work

Student work is another piece of everyday data that can serve multiple purposes in the classroom. In the process of planning lessons, teachers can identify key moments when learning should be noticeable and plan ways to collect evidence of that learning from each student (Hiebert et al. 2007, 52). For example, short writing tasks let teachers gather responses from all students (Sherrington 2019, 33). Compared to calling on a few individual students, collecting student work from every student provides teachers with more accurate information regarding whether students learned what was taught.

Wiliam and Leahy (2015, 42) advocate for the use of samples of student work to communicate quality to the class, noting that when students notice mistakes in other students’ work, they will be less likely to make those mistakes in their own work. They recommend starting with just two pieces of work, one strong and one weak. Once students gain experience comparing the quality of work, teachers can introduce more samples as the basis for constructing success criteria for student work.

Black et al. (2004, 13) advise providing opportunities for students to respond to comments as part of the overall learning process. Such opportunities are intended to communicate that assessment is for learning and not just of learning. As they state, by providing students with opportunities to respond to comments, “the assessment of students’ work will be seen less as a competitive and summative judgment and more as a distinctive step in the process of learning” (Black et al. 2004, 13).

[R]esearchers found that in classrooms where teachers implemented self-assessment strategies along with other formative assessment activities, students achieved greater gains on standardized tests.

Steele and King (2006, 139) note that students’ classwork and homework provide teachers with access to “a constant stream of data.” As they observe, such data can be used to inform instruction. Steele and King encourage teachers to systematically gather evidence from this data, such as by identifying specific yes-or-no questions that they can use student work to answer. For example, if students are asked to show their inferences by marking up a text, teachers might look to see whether the inferences that the students made are plausible. The answers to these questions, in turn, can inform instructional steps: What topics need to be retaught? How might students be grouped to best address learning needs?

Self-assessment to build ownership of learning

Engaging students in ongoing self-assessment can help students see themselves grow and foster a sense of agency over their own success (National Task Force on Assessment Education). To self-assess their performance on a task, students must have an understanding of what “good work” looks like; in this way, self-assessment helps students internalize the success criteria. In one study, researchers found that in classrooms where teachers implemented self-assessment strategies along with other formative assessment activities, students achieved greater gains on standardized tests (Wiliam et al. 2004, 60).

Create contexts to support use of everyday data

Teachers need support to use everyday data to inform instruction. Based on a review of research on data use, Schildkamp (2019, 12) argues that the school leader plays a critical role in supporting data use. School leaders can encourage the use of data by framing the process as supporting continuous improvement, rather than by emphasizing accountability, and can use their own data literacy skills to monitor, model, scaffold, guide, and encourage the use of data. Schildkamp (2019, 12) recommends that school leaders distribute leadership so teachers are empowered in the data-use process and believe they can take action based on data.

While test scores are one form of data, and perhaps the first type of data that comes to mind, everyday data can be gathered from the questions students ask, the dialogue between students as they collaborate, students’ responses to questions, and written student work.

Additionally, instructional coaches play a critical role in providing support to teachers as they analyze student data to guide instruction. A statewide reading program in Florida middle schools paired instructional coaches with teachers. A mixed-methods evaluation of the program revealed that it is associated with both perceived improvements in teaching and higher student achievement (Marsh, McCombs, and Martorell, 2010). [S]chool leadership and instructional coaching are key supports that enable educators to use everyday data in new ways.

Reflection questions

  1. What types of everyday data do you have in your district or school that you can incorporate into your continuous improvement processes?
  2. How can you build or articulate the coherence across multiple types of data in your district or school?

This post is an excerpt from chapter 1 of Assessment Education: Bridging Research, Theory, and Practice to Promote Equity and Student Learning. You can read case studies on the power of everyday data in action in the book.

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