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Using Strong Data Culture to Create Effective COVID-19 Instruction

As districts consider at-home remote learning, socially-distanced schools, or hybrid models, school leaders and educators can use this opportunity to intentionally integrate technology into their regular instruction by utilizing principles of blended learning.

Recent research predicts student learning gains from the 2019-20 school year will suffer significant losses as a result of COVID school closures in the spring. Researchers project students will return in fall 2020 with roughly 70% of the learning gains in reading and less than 50% of the learning gains in mathematics relative to a typical school year.1 Others estimate students could lose three to four months of learning if they receive average remote instruction, seven to 11 months with lower-quality remote instruction, and 12 to 14 months if they do not receive any instruction at all.2 

Given these daunting projections, educators know it is vital to quickly identify where learners are in their progression towards mastery of essential skills and respond accordingly. (Consider reading our related resource, 3 Blended Learning Fundamentals That Can Help Educators in the 2020-21 school year Prioritize Data, Relationships and Rigor in every Student Experience Next Year”.)

Educators Should Focus on 3 Blended Learning Fundamentals in 2020-21

Campuses with strong data cultures are best positioned to identify and address COVID learning loss for all students. A strong data culture begins with a well-developed data vision within a district’s teaching and learning departments and campus instructional leadership teams. Educators working in a district with strong data culture, and consequently, with more robust data systems and assessment routines, can also feel more empowered and equipped to align instructional responses based on their data, further improving their ability to personalize student instruction.

Raising Blended Learners Provides Insight about How Data Cultures Best Support Personalized Learning

Raising Blended Learners (RBL) districts found a powerful connection between a strong data culture focused on individual student data and an increased ability to personalize instruction for students. Their early work also revealed areas where existing data practices needed to be adapted to effectively support teaching in a blended learning context. These lessons apply directly to educators facing the challenge of offering increasingly individualized instruction in socially distanced, hybrid and remote learning environments.

Open-Source Professional Development Modules: “Building a Data Culture”

To support educators in shifting their practices and mindsets around using data to facilitate personalized learning, the Raising Blended Learners initiative has developed a series of open-source professional development modules. The objective of the module series is to enhance campus data cultures to support effective instruction in a variety of learning contexts and to better address COVID learning losses through personalized, rigorous instruction and data-informed learning activities. While the audience and activities for this module are designed for school campuses, we also recommend district leaders participate to deepen the support and connection for a consistent data culture.

Building a strong data culture rooted in well-established systems tracking individual student progress is critical for teaching effectively across a variety of learning contexts, and it can help identify and eliminate COVID learning losses. We invite educators to explore these open-source modules and determine how they might help support a rapid cycle of meaningful assessment and individualized instructional response essential for teaching during this unprecedented time. 

Provided below are additional details about each RBL Data Culture learning module, suggested uses, and several examples of learning activities within the content.

Data Culture Modules: Objectives and Descriptions

Specific objectives and a basic description of the content of each module are presented in the table below.  

ModuleLearning ObjectivesContent Description
Module 1. Why Data Culture?- Get clear on why building a data culture is vital for our current context and start building an understanding of what a data culture is and isn't.

- Identify data culture shifts needed to meet current conditions and prepare for blended learning.
Participants build awareness of the value of a data culture and reflect on mindset and practice shifts needed to better support data-informed teaching.
Module 2: Foundations of a Data Culture- Self-assess and reflect on the current state of a participant’s campus data culture, including the conditions that uphold them.

- Learn from an example of an RBL district, which built a strong data culture.
Participants use a data culture reflection tool to assess their campus data culture and study an example of an RBL district, which built a strong data culture to support blended learning.
Module 3: Sources of Data- Recognize the difference between the unit of data analysis at the classroom level and the student level and why this distinction matters to improving and personalizing learning.

- Identify and prioritize sources of data in light of COVID and to prepare for teaching and learning in multiple learning environments.
Participants work through a series of activities to clarify existing data sources, how they use them, and where gaps or overlaps occur. They also prioritize sources of most value for addressing COVID learning loss and to support personalized learning across multiple learning environments.
Module 4: Systems & Routines- Consider what teachers think about data use as a way to better inform instruction and which systems and routines best support teacher and student data use. Participants get specific about the systems and routines which comprise a data culture, assess their own data culture, and identify specific areas for improvement and adjustment to meet current challenges and future goals.
Module 5: Data Culture Prototyping- Determine the next steps for moving toward a more effective data culture on your campus with an emphasis on supporting data-informed teaching across multiple learning environments.Participants develop a campus-level data cycle prototype to address what is working/ not working, capture refinements they plan to make, and map the data they plan to prioritize across multiple learning environments.

Flexible Options for Using the Content

The professional learning content is designed to accommodate participants at a range of readiness in data literacy in alignment with Universal Design for Learning principles.3 It is flexibly designed to allow educators to leverage in multiple formats which might include: 1) synchronous sessions with a team of educators, led by a district or campus leader to leverage the content and support collaborative learning;  2) asynchronous content which allows participants to engage online at their own pace; or,  3) a combination of synchronous and asynchronous learning according to a set timeframe. 

Examples of Learning Activities

The following are descriptions of several activities from the modules to help educators quickly assess whether the content might be relevant at this time.

The objective of this module is to self-assess and reflect on the current state of a participant’s campus data culture and identify specific areas to target for learning and growth. After listening to a recorded mini-lesson about six focus areas that uphold a data culture, participants are invited to utilize a Data Culture Reflection Tool to rate the strength of their campus for each focus area.

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Ratings range from zero representing “no implementation/not present” to four indicating “exemplary implementation.” Subsequent to completing individual reflections, participants are encouraged to come together as a team to share reflections about where they are strongest and weakest, discuss differences of opinion and develop a consensus for each condition and rate them accordingly on a campus-level reflection tool.

Data Reflection Tool ➞

Guiding questions to support their thinking include:  Which focus area(s) and conditions stand out as areas of strength? Which stand out to you as areas for growth? Which are the areas of discrepancy between how your individual team members ranked the 6Ss and the corresponding components? 

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One objective of this module is to help participants recognize the difference between analyzing data at the classroom unit of analysis compared to the student level of analysis; an important distinction for those seeking to make learning more personalized. After working through content to meet this aim, participants engage in a data inventory activity to identify and prioritize sources of data according to which are likely to be most useful to address COVID learning losses and prepare for in-person, hybrid and remote learning options. Described below is the four-step activity.

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Step 1:  Participants listen to a recorded mini-lesson about building a data inventory, and exploring the Sources of Data portion of the Establish a Blended Data Culture Playlist.

Step 2: Participants are provided examples of district and campus level data inventories for study and reflection.  They are then provided a blank data inventory template and asked to develop a data inventory for their campus.  At this stage, educators consider basic questions including: Which data sources do we currently have? How are we using the data source? How could we be using this data?

Data Inventory Template ➞

Step 3:  Once data sources are listed in the inventory template, participants are asked to identify high priority data sources most helpful for personalizing teaching and learning in the current context. Guiding questions to support their thinking include: Which data sources provide the best information about student learning gaps? Which data sources provide sufficiently granular, student level data to allow teachers to personalize instruction on an ongoing basis? Are we missing certain data sources which will better enable us to personalize instruction (e.g., proficiency based growth assessments)? Do data sources overlap which we might streamline?

Step 4:  The final step of this activity is to conduct a “pressure test” which takes participants through a checklist of questions to allow them to make adjustments to their plans for using various sources of data in light of COVID. The pressure test includes these types of questions:  Will you have sufficient data if students are not on campus to complete assessments?  Which assessments allow you to modify the assessment windows?  Can data be viewed at an individual student level (e.g., instructional level by student, standards by student)?  Are assessment cycles for certain assessments sufficiently short and reliable to enable teachers and students to make weekly instructional decisions?

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RBL COVID-19 Resources

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The Teacher Perspective: Why Does Blended Learning Work?

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Educators Should Focus on 3 Blended Learning Fundamentals in 2020-21

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A Blended Learning Coaching Tool

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Using Strong Data Culture to Create Effective COVID-19 Instruction