School of One Mastery-Based Model
In 2011, my school was awarded a grant that brought the School of One mastery-based blended learning model to MS88. School of One allows students to learn at their own pace in a totally redesigned, open classroom that can fit well over 100 students in different centers of the room. Students are assessed at the beginning of the year and given a “learning trajectory” for the entire year. Every day, each student is assigned new individualized lessons in different parts of the classroom in one of seven different learning modalities: virtual instruction/reinforcement, independent practice, small group/peer-to-peer collaboration, live investigation, and task projects. At the end of each class, we use an “exit slip” to evaluate and regroup students based on their progress. They are required to demonstrate mastery in each skill or concept before they can move onto new skills and concepts.
Number of Students: ~300 students
Number of Adults: six teachers; one Operations Technology Associate; SPED teacher(s) and/or paraprofessionals (as needed)
Length of Class Period/Learning Time: 92 minutes (divided into two 36 minute sessions)
Digital Content/Ed Tech Tools Used on a Regular Basis: proprietary web-based software; IXL, LearnZillion, VirtualNerd, Khan Academy, MangaHigh, Math XL, TenMarks, I Can Learn, Khan Academy, Engrade, Educreations, Padlet, Remind, Weebly, Google Apps for Education
Hardware Used on a Regular Basis: student laptops (1:1), iPads for teachers, SMARTboards
Key Features: competency-based; student agency;individualized learning paths; project-based; innovative use of time; innovative use of talent; co-teaching
A blended teacher’s personal mindsets shape his decisions as an educator. These mindsets influence general pedagogies, instructional approaches, and short-term decision making, alike. Check out how Aaron’s mindsets have helped to shape his blended instruction.
The grouping algorithm employed by School of One assigns students a new lesson every day based on the student's most current learning needs. The algorithm actually learns the students' needs from the previous day's exit ticket. One of the learning styles or lesson types, Live Investigation, assigns students to me who are ready for whatever the assigned skill is. However, within that group, there are still varying levels of ability. I can see all of this on my data report and then I can group within my group. I call this micro-grouping.
At the end of every class, my students must take a computer-based exit slip. This is an essential part of my blended program because these exit slips tell me whether or not my students are ready to move on to the next skill. If a student gets 4/5 or 5/5, he or she can move on. If not, he or she will be assigned a different type of lesson on that skill the next day.