By: Anne Childers
Guide students to be agents of their own learning through data analysis and SEL
There is tremendous power in coupling school data and social and emotional learning. While most schools have access to a surplus of student data, figuring out how to use it to drive learning remains a mystery. One innovation I have been developing as a district data coach and now as a classroom teacher is a process to teach kids how to look at their own data.
More specifically, the process helps kids employ SEL strategies to look at their NWEA growth data. NWEA is an assessment that measures growth over time and norms scores to 10.2 million students- giving both growth and achievement data. Our district’s students learned to explore and reflect on their own results and growth in order to make informed decisions about next steps. In the initial year of goal-setting the district saw the number of students meeting their math goals increase by 33 percent and reading increase by 39 percent, with all grade levels increasing in national percentile ranking in both math and reading.
There are five critical elements to effectively combining SEL and data analysis.
1. Use data to build a community of continuous improvement
It’s critical to set a tone where the focus is not on achievement, but on using data to uncover successes and failures. Helping students recognize that they won’t always see positive growth helps to normalize the ups and downs of their academic journey. For adults and students alike, continuous improvement requires that we take risks and reflect on learning.
A concrete way to develop this kind of community is to include students in the process of developing norms and expectations for data analysis. The How Learning Happens Fact Suite developed by the Aspen Institute’s Commission on Social, Emotional, and Academic Development is a great resource to support this work. For example, teaching students that brain science tells us that the most effective “learning happens in safe and supportive environments” gives students permission to ask for what they need to make looking at their data a positive and safe experience. Examples of student-driven expectations and norms include respecting others’ privacy, having a growth-oriented mindset, listening, and asking questions. Using this process with more than 5,000 students–with all of them honoring the norms–has validated this process as an effective way to build the requisite community for continuous improvement.
2. Focus on mindsets and learning sciences
Building on the above example, reflecting on
learning data is a prime opportunity to teach students about brain science. Direct
instruction about neuroplasticity confirms for students that our abilities
(cognitive, social, and emotional) are not fixed. Exposing our students to this
science changes the conversation and unlocks new possibilities. What would happen to our learning
communities if all students learned to “say kind words to themselves” or
“challenge themselves to pay more attention.” In one second-grade
classroom when discussing the impact of how we talk to ourselves (our inner
dialogue) a student raised her hand and asked me, “You mean we shouldn’t
The takeaway is that knowing how their brains work enabled an openness to looking at data. Today’s generation lives in a world plagued with ‘feeds’ brimming with the real-time ‘likes’ and ‘dislikes’ from their peers. Perhaps the ‘digital natives’ are now ‘data natives’- a generation where mass amounts of information is normalized in their daily world.
3. Develop a strengths-based approach to data analysis by breaking down big data
In order to have a strengths-based lens, break down the data into meaningful, bite-sized pieces. Typically, educators talk about reading scores, but composite scores aren’t really helpful for actionable continuous improvement. NWEA Growth scores overall reading and offers instructional area scores in literature, informational text, and vocabulary. State tests have isolated scores that often get overlooked. By focusing on the components of reading and math, we can discuss self-awareness and help develop accurate self-perceptions. When analyzing math scores based on instructional areas, one student drew muscles to support the numerical meaning, drawing an arm with four large muscles in his area of strength and descending number of muscles down to a mini-muscle in his area of growth.
4. Intentionally ask about feelings related to academic growth data
Ask students how they feel about their data, specifically about their strengths and areas of growth. Once feelings have been explored, layer in the social and emotional skills and strategies they can use to set goals and stay motivated. For example, before summer vacation I analyzed three year’s worth of summer scores with fifth graders, discussing the normalized summer dip. Then, I asked questions related to self-awareness, as defined by CASEL, and their own reading data. How are you feeling about summer reading? What are your summer reading goals? How will you stay motivated to read?
The Mood Meter, developed by the Yale Center for Emotional Intelligence, is a good tool to build student’s vocabulary when identifying emotional responses to data. Thanks to expanding vocabulary about emotions, one student wrote, “I feel secure, chill, and grateful for summer reading.” These kinds of tools help educators support students to recognize the role their emotions play in learning.
5. Use a process to identify social, emotional, and academic supports and barriers to learning
The SEAD acronym helps guide reflecting on how our emotions, social context, and academic resources influence our outcomes. In each lesson, classes brainstormed social supports and barriers, emotional supports and barriers, and academic supports and barriers. The brainstorming process then lead to students individualizing next steps in their learning- be it social, emotional, or academic.
Students reported supports to their learning
like persevering, correcting mistakes, taking your time, focusing, maintaining
high expectations, listening to yourself, and more. Common barriers reported
were being tired, busy schedules, distractions, not trying, not interested, not
liking to ask for help, and screen time. View a full list on my website
as well as goal-setting tools for students and educators.
If we are serious about using data in our schools and classrooms, we must acknowledge that it’s not just about the numbers, it’s also about the reflection along the way. How can we ensure our youth are getting practice at this kind of analysis and how do we foster a “new normal” around student access to their own data?
This year I’ve returned to teaching to further explore goal-setting. My class recently met their winter reading growth goals by 220 percent, putting the class in the 84th median conditional growth percentile. We’ve focused on using data to set goals based on SEL embedded in the California standards. I’m also collecting data on student SEL through Kelvin Education. My goal is to continue exploring how to give students agency over their own learning through data-driven process that build social and emotional competence. Follow the journey at annechilders.com.
Disclaimer: The Assessment Work Group is committed to enabling a rich dialogue on key issues in the field and seeking out diverse perspectives. The views and opinions expressed in this blog are those of the authors and do not necessarily reflect the official policy or position of the Assessment Work Group, CASEL or any of the organizations involved with the work group.