Data-Informed Planning for Personalized Learning
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Microcredential ID : 2681
Personalized Learning
0.5 USBE Credit

This microcredential represents educators' strategic use of data to plan personalized learning lessons and units. Data should be gathered regularly and systematically from multiple sources, analyzed carefully, and used to tailor learning experiences that allow the learner to have voice and choice in time, path, pace, and place of learning.
How To Earn This Microcredential
To earn this 0.5 USBE Credit microcredential you will submit two evidence items to demonstrate your use of data to personalize instruction for learners. You will also submit a reflection. Click the Earn This Microcredential button for more information.
If you submit this microcredential for review, you will be assessed an administrative fee of $20.00.
Data-informed planning for personalized learning is using multiple formal and informal sources of data (triangulation) to drive personalized planning / instruction and learning. It is NOT using a single piece of data to inform instruction for all learners.
Important Terms
Personalized Learning: Tailoring instruction for each learner's strengths, needs and interests, including enabling learner voice and choice in time, path, pace, and place of learning–to provide flexibility and supports to ensure mastery of the highest standards possible.

Data-Informed Instruction: Using data (e.g., reports, student work) to identify misconceptions and reteach individual students, small groups, and/or whole classes.

Data: Facts, statistics, and other artifacts of learning collected together for reference or analysis.

Formal Assessment: Standardized method for testing how well a student has learned the material that has been taught. Formal assessments create statistical models that can be used to compute the performance of each student.

Informal Assessment: Informal assessment involves observing the learners as they learn and evaluating them from the data gathered. It can be compared to formal assessment, which involves evaluating a learner's level of language in a formal way, such as through an exam or structured continuous assessment.

Summative Assessment: The goal of summative assessment is to evaluate student learning at the end of an instructional unit by comparing it against some standard or benchmark.

Multiple Data Sources: Use data that comes from several different places—formal assessments, informal observations, summative assessments, etc.
Background Scenario / How This Will Help You
Before starting instruction in her persuasive writing unit, Ms. Smith gives her students a pre-assessment with the goal that by the summative assessment, her students will be proficient in introducing claims, incorporating evidence to support claims, and using effective elaboration to analyze the evidence.
After the pre-assessment, Ms. Smith notices that about 80% of her students are proficient with their claims, 53% of her students were still working towards proficiency in incorporating evidence to support their claims, and 43% of her students are working towards proficiency with their elaboration. She decides to plan a day to help students understand their own data and set goals and make plans to increase their proficiency. Ms. Smith also decides that a station rotation day might help students achieve their own goals they have set for themselves.
The next day, Ms. Smith goes over the persuasive essay rubric with her students and shows them some sample essays. The students then look over their own essays and on the learning tracker Ms. Smith provided for them, they track their learning for each rubric score and set goals for improvement. Based on their goals, Ms. Smith has her students choose which station(s) they need to go to in order to improve their writing: claims, incorporating evidence, and using effective elaboration.
As the students are rotating between stations, Ms. Smith walks around the classroom gathering informal data: observing students, talking with them, and helping them. She notices that after going to the evidence station, most students feel more confident in using evidence. However, even after going to the elaboration station, students still cannot quite explain how to elaborate. She makes a note to add more resources to their Canvas course and decides to plan a day using some direct instruction to help students understand and use elaboration better.
Later, Ms. Smith gives her students another formal assessment. She now has 93% of her students proficient in claims, 80% proficient in incorporating evidence, and 67% proficient in using effective elaboration. She makes notes of students who are proficient in all areas and makes plans to extend learning for those students and ask them to be coaches for their peers. In order to help struggling students, she finds more resources and activities to help those still working towards proficiency. She plans another formal assessment to continue gathering data as students keep progressing in their learning.
Evidence Options
Category: Preparation and Planning
Select ONE of the evidence options below to demonstrate your preparation and planning to use data to inform personalized learning.
Lesson Plan: Lesson Plan: Submit two lesson plans, one showing the original plan to be used in your instruction and one that shows how the personalized learning was changed due to formal assessment or individual assessments. The lesson plans should show how your instruction provides learner choice in path, place, pace, or time; and supports an environment that allows learners to be self-directed. In a separate section of the lesson plan, include citations for research supporting your instructional approach. (See the resources section for examples to cite.)
Category: Implementation
Select ONE of the evidence options below to demonstrate your implementation of data-informed decisions to support personalized learning.
Video: Submit a 4-6 min video of your instruction showing data collection that drives the personalized learning in your classroom, including changes in routines and procedures of the lesson. This video should demonstrate how your instruction changes using multiple data sources to increase learner’s understanding of the topic being addressed. The video should also show how data supports an environment that allows learners to be self-directed. Video should follow FERPA and your district or charter guidelines for student privacy.
Student Performance Data: Submit data showing how learners’ performance improved as a result of the feedback in your data that was collected in the classroom. Data should include pre and post scores for three different assignments. Include a written description of the data and explanation of how learner growth was linked to your culture of feedback. Be sure to follow your district/charter guidelines to protect learner privacy.
Observation Results: Submit written or video-recorded testimonials from 2-4 colleagues or administrators in which they describe how your instruction changes based on individual or formative assessments and is personalized, student-centered, allows for choice, and is engaging. Video should follow FERPA and your district or charter guidelines for student privacy.

Review Criteria
Criterion 1: Evidence demonstrates that data from multiple sources is gathered and analyzed.

Criterion 2: Evidence demonstrates that instruction and planning are adjusted based on multiple sources of data.

Criterion 3: Evidence demonstrates choice over pace, path, place, or time, with learners identifying their goals and tracking their progress.
Reflection Prompts
Describe how collecting and using data helps you to personalize learning.
Explain how your students benefit from the data you collect. Give a specific example.
Describe how you plan to strengthen the personalization of your instruction based on data collection in the future.

Review Criteria
Criterion 1: The reflection indicates the teacher uses data from multiple sources for instruction and planning.

Criterion 2: The reflection indicates the teacher understands the benefits of using data for instruction and has a plan to continue using data to strengthen personalized learning.
Bold School: Old School Wisdom + New School Technologies = Blended Learning That Works by Weston Kieschnick
Technology is awesome. Teachers are better. Blending new technologies into instruction is a non-negotiable if we are to help our students gain these skills they will need to thrive in careers. And so too is educators' old school wisdom in planning intentional blended learning that works: Bold school thinkers embrace Blended pedagogies and Old school wisdom. In Bold School blended learning is demystified and distilled into the powerful, yet simple Bold School Framework for Strategic Blended Learning to help you enhance your instruction and learning.

The PL Toolbox: Assessment and Data—Phase 1
This website can help you self-assess where you are in terms of using data to inform instruction.

3 Ways Student Data Can Inform Your Teaching
Gather and use valuable student data to inform your classroom practice. The number one job of a teacher is to be faithful to authentic student learning. Unfortunately, our profession is overly fixated on results from one test, from one day, given near the end of the school year. Yes, that standardized testing data can be useful; however, we teachers spend the entire year collecting all sorts of immediate and valuable information about students that informs and influences how we teach, as well as where and what we review, readjust, and reteach.

TEDxCincy - Jeff Edmondson - The Key to Educational Improvement: Data and How We Use It
TED Talk available on YouTube, to use data to drive the success of each and every child.

The Core Four of Personalized Learning: The Elements You Need to Succeed
A good overview of the key elements of personalized learning. If you want to just read about data driven decisions, start on page. 19. This resource will help you identify where you are in terms of using data to inform instruction and specific things you can do to increase your competency in this area.

The five building blocks of data-informed instruction
Article that includes ideas on how to acquire data for informed instruction with links to other articles that also teach about data gathering and usage.

Driven by Data 2.0: A Practical Guide to Improve Instruction, 2nd Edition, by Paul Bambrick-Santoyo
Data-driven instruction is the philosophy that schools should focus on two simple questions: how do you know if are students learning? And when they are not, what do you do about it?

Teaching Channel - Using Data Effectively
A professional development toolkit for teachers and leadership teams, designed collaboratively. Videos and modules to help both teachers and leadership to understand how to use data effectively.
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