Using Actionable Data to Personalize Learning
Actionable data is an essential prerequisite for supporting personalized learning. These data come in many forms, from teacher observations to formative assessment to interim or benchmark assessments.
With all this data – the term to focus on to support your personalized learning is actionable. Getting focused on using data to support personalized learning may be new for some, unexplored for others and an instilled habit for veteran staff. One major key to making this use of data most effective is that the data are actionable.
What Makes Data Actionable?
Actionable data has three major characteristics.
- It is timely, meaning we don’t wait from the test in March to the end of July for the results. Ideally, it is available instantaneously or at least within 24 hours.
- This data is easy to understand, so much so that both teachers and learners can make sense of the data.
- The data are immediately actionable for both students and teachers. Students can adapt their learning tactics and teachers can adjust their instruction.
We need to remember that students generate most of the data that is accessible in school. We must teach them to be consumers and users of the data they create. Educators need to make the data actionable for all.
Actionable data is the first key to solving the puzzle for supporting personalized learning. Three guiding questions form the second key – Who? What? How much? Who needs what support (or learning) and how much do they need or want? Those are the questions that guide the actions educators can take to personalize the learning. And actionable data can provide the answers to all three questions.
Why Use Data to Personalize Learning?
The 2019 NCES Forum Guide says, “Personalized learning aims to tailor instruction to the needs, talents, and skills of each individual.” Using data effectively means that educators (and learners), when they are taught how, develop a better understanding of the learners, their needs, and different approaches to consider. Making instructional decisions means we are using a variety of data to identify both common and unique needs, looking across a grade or class. Translating data, using the information to support instructional decisions can be challenging, particularly when students have multiple areas of growth to focus on. Being strategic with the use of data, teachers can identify patterns, trends and actions at multiple levels, whole-group, small-group, and individual. The primary reasons to personalize learning are to meet learners where they are and empower them to keep wanting to learn.
These ideas aren’t new. Two findings from the 2015 RAND report, Continued Progress: Promising Evidence on Personalized Learning | RAND were that schools with the highest achievement gains 1) used data to drive the implementation of student grouping and 2) helped students understand and discuss their data during class. When we consistently collect data from a variety of assessments, we can watch the development of our learners over time, and they can support their own development by learning to use their data.
Three Strategies for Using Data
Flexible grouping is one way to begin using data to support personalized learning. Which students struggled with the same skill or concept? Which students can apply the skill to a more challenging task? Figuring out what types of groups learners would benefit from working in can be done using various methods.
Using learning stations is another strategy that can be based on data and supports personalized learning. Having specific stations address the identified needs of your learners provides the opportunity for students to focus on the right tasks using the right resources.
Building student ownership of data is critical. Students generate the bulk of the data. They need to have access to their data and an understanding of how to use the data to set and work toward meaningful learning goals. Helping learners develop data literacy empowers them in new ways. Not only can they set goals relevant for themselves, learners can also reflect on their challenges and successes and use both to support their choices in how and what they learn.
One way teachers build this ownership is through data notebooks. Often organized by standards, learners can see where they are in their attainment of a standard. Conversations are supported by data so learners can discuss what they know and don’t know and make their plans for what they will do to get where they want and need to be. These notebooks also allow learners to track what strategies work best for them
Teaching students to own their data and make it actionable to support their learning has several benefits. Setting their own learning goals helps develop motivation. Self-assessment builds that ability and contributes to self-regulation. Determining their own learning activities and identifying the strategies that work best for them helps develop their self-advocacy skills.
Technology in Personalized Learning
The use of technology has saved educators and learners time – time for educators to answer the who, what and how much questions and plan the personalization. A digital infrastructure can provide adaptive learning paths, automate content and assessment, and help teachers (and students) track progress.
Depending on the software, there are opportunities to differentiate for a variety of learner needs with text-to-speech, reading, guided reading (text revealed after answering a question correctly), and other tools. Technology can help teachers assess students’ strengths, needs, track mastery, and select and deliver the curriculum.
Steps to Increase Personalized Learning
Building personalized learning opportunities using data involves seven steps.
- Train – Educating teachers is the first step to using data to support personalized learning. When teachers fully understand the assessments, data and technology, then personalized learning can be supported by actionable data.
- Assess – To know where you want to go, you need to know where you are. What is critical here is 1) that teachers understand the data and know how to act on it and 2) teachers teach learners how to understand and use their own data.
- Identify – This is where standards come in. Checks for understanding help answer who and what during instruction, while benchmark, interim or mastery checks occur at specific times along the way.
- Modify – Use during-instruction data about the learners’ specific knowledge to modify or customize the learning plan.
- Empower – When learners are given a voice in their learning experience, they develop essential skills such as self-advocacy. When given the chance to participate in goal setting, students are more motivated to reach those goals. Empower learners to own their data.
- Monitor – Whether during learning (formative assessment) or at scheduled intervals, rgularly check the status of learning to collect additional data points for both the learner and teacher to track progress and adjust. Knowing that a learner is struggling today guides personalization tomorrow.
- Review, Reflect, Revise – Continually seek to improve the learner’s experience and mastery as part of the process of using data as a support for learning. The learner’s and teacher’s self-assessment of what’s working and what’s not provide ongoing opportunities to make adjustments that contribute to increased academic outcomes and growth.
Personalizing learning has a huge impact on learning outcomes and learner engagement. Customizing the learning experience based on the learner’s data means needs are better met and each learner gets the kind of education they need to be as successful as possible.
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This article was written by Kathy S Dyer.
Kathy Dyer is an innovative educator who has served as a teacher, principal, district assessment coordinator, and adjunct professor. She has a passion for learner-centered learning—opportunities for learners of all ages to learn with, from, and for one another. Believing that all learners can learn more and grow more, Kathy is passionate about helping schools and educators get better at what they do.
Kathy combines a deep understanding of adult learning with a passion for collaborative problem solving to help school systems improve student outcomes. Her work has been featured on eSchool News, Education Dive, Ed Circuit, Teach. Learn. Grow. blog, and Getting Smart.