This microcredential focuses on the 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.
To earn this 0.25 USBE Credit microcredential you will submit one evidence item 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.
You will be charged $20 by the badge provider. You'll be charged at the point you submit your badge for final review.
Data-informed planning for personalized learning is using multiple formal and informal sources of data to drive personalized planning / instruction and learning. It is NOT using a singular piece of data to inform instruction for all learners.
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.
Informed Instruction: Use reports and 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.
Before starting instruction in her persuasive writing unit, Mrs. 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, Mrs. 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. Mrs. Smith also decides that a station rotation day might help students achieve their own goals they have set for themselves.
The next day, Mrs. 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 Mrs. Smith provided for them, they track their learning for each rubric score and set goals for improvement. Based on their goals, Mrs. 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, Mrs. 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, Mrs. 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.
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.
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.
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.
Testimonial: Submit video-taped testimonials from 2-4 learners that include them discussing how your classroom culture of personalized learning changes based on individual or formative assessments, is student-centered, and is allowed to change based on the understanding of learner feedback. Video should follow FERPA and your district or charter guidelines for student privacy.
Observation Results: Submit video-taped testimonials from 2-4 colleagues or administrators that include them discussing how your classroom culture of personalized learning changes based on individual or formative assessments and is student-centered, allows for choice, and is engaging. Video should follow FERPA and your district or charter guidelines for student privacy.
Candidate's Choice: Submit another type of evidence demonstrating how you use data to personalize instruction for learners effectively and consistently.
Candidates are required to make 1 evidence submission(s).
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.
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.
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.
3 Ways Student Data Can Inform Your Teaching https://www.edutopia.org/blog/using-student-data-inform-teaching-rebecca-alber 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.
Bold School: Old School Wisdom + New School Technologies = Blended Learning That Works by Weston Kieschnick Find it on Amazon.com 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.
Driven by Data 2.0: A Practical Guide to Improve Instruction, 2nd Edition, by Paul Bambrick-Santoyo Find it on Amazon.com 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?
TEDxCincy - Jeff Edmondson - The Key to Educational Improvement: Data and How We Use It https://www.youtube.com/watch?v=FLqc_9VxfCE 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 https://www.edelements.com/hubfs/Core_Four/Education_Elements_Core_Four_White_Paper.pdf 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.