Standards-based curriculum is the core of the formative assessment process in English Language Arts and Math at the Chippewa Falls Middle School.  Student progress toward proficiency on the established learning targets is monitored using a variety of both formal and informal formative practices. The use of formative practices has helped students and instructors determine progress toward standards attainment and the meeting of student learning goals.Read more about these practices in the module. 

Google Slides Template for the Data Inquiry Journal housed on WISEdash for Districts Make a copy of this template for your own use. 

 Elementary and middle school teachers at Marathon Area Elementary and Venture School work collaboratively on developing learning targets and setting the expectations of high quality work.   Students track their learning progress in a binder that moves with them in each grade. They engage in self-reflection through student-led conferences as they report on data collected and show examples of his/her work.The student explains why each piece of work was important, how it connected to their learning goals, and how that lead to demonstration of proficiency in the standards.  Read more about these practices in the module. 

 Elementary and middle school teachers at Marathon Area Elementary and Venture School work collaboratively on developing learning targets and setting the expectations of high quality work.   Students track their learning progress in a binder that moves with them in each grade. They engage in self-reflection through student-led conferences as they report on data collected and show examples of his/her work.The student explains why each piece of work was important, how it connected to their learning goals, and how that lead to demonstration of proficiency in the standards. Read more about these practices in the module. 

This unit focuses on the diversity of life at Hartje School Forest and centers around NGSS Standards on Ecosystem Interactions, Energy and Dynamics. Field experiences in observing and recording the diversity of life, seed dispersal methods, plant pollination, and plant life cycles will support science disciplinary core ideas, cross-cutting concepts, and hands-on engineering practices.

Lisa Krohn, Director of Teaching and Learning, challenged teachers at Johnson Creek Elementary School to rethink the traditional format of student learning and assessment and, focus on how to create  successful learners. Students are now grouped by academic readiness instead of age. Teachers transitioned from being grade level teachers (e.g. 3rd grade teacher) to “Focus Area Advisors” specializing in math or literacy. The workshop model allows students to be active learners engaging in formative assessment practices including conferring, journaling, and computer adaptive programs to name a few.  Read more about these practices in the module. 

Principal Matt Renwick, principal and author, works with the art teacher and grade 5 teacher to utilize portfolio assessment in classrooms. The teachers are incorporating digital portfolio assessment as a way to better gauge student progress and success for more subjective areas of study, such as literacy and art.  Read more about these practices in the module.

 The Senior Exit Portfolio Project (SEP) at West Salem High School was created to provide evidence that its graduates were career and college ready. The self-directed, active learning experience spans the entire senior year and includes the selection of an area of interest, a research paper, development of a project, maintenance of a portfolio of work and work artifacts, and a presentation of the research and project to a panel of community members.  Each student is assigned a mentor related to the field of study to help link the research to practical application. The mentor connection and the use of a community panel has encouraged deep community participation and investment in the educational process. The SEP also provides an opportunity for full staff collaboration, promoting vertical integration of learning standards across all grade levels and content areas.  

 At Lake Superior Intermediate School, grade level teams meet weekly and quarterly to dig into student data. Each grade level created a data wall to monitor student progress. The data analysis process and discussions drive decision making about student learning and classroom instruction. High quality data is essential to determine which students need more practice and/or intervention/enrichment (PIE) on each priority standard for both ELA and Math. The teams use universal screening data, running records for reading, common formative assessments, and teacher observations to ensure multiple sources of data present a complete picture of student learning and guide instructional adjustments.Read more about these practices in the module. 

Building on a collaborative staff culture, teachers presented their SLOs with colleagues for feedback and to find common themes in instructional practices. Professional Learning Communities (PLCs) shifted their focus from teaching to student learning as a result of increased data literacy skills across staff. It has become a mission of the Grant Elementary staff to continue to address individual student needs to ensure all students are showing growth and achievement.  Read more about these practices in the module. 

In order to integrate Universal Design for Learning (UDL) at all levels, a series of "Try Its" (3-4 week periods to implement UDL strategy) were scheduled throughout the year with cohort groups of teachers to increase student engagement and reach more of their  students.    Following each try it period, the cohort team reviewed the resultant data.  After their first year, cohorts regrouped to discuss UDL practices and how they worked in the classroom. In year two, a second cohort was developed to follow the same path. Cohort 1 trained Cohort 2, and answered questions. Members of the two cohorts were then strategically mixed with other staff members to help them understand the UDL work and process.  Read more about these practices in the module. 

The module Instructional Materials & Professional Learning based on Instructional Partners Curriclum Support Guide Framework is designed to guide district teams as they Understand and explain why instructional materials matterBuild understanding of the Curriculum Support Guide FrameworkBegin to develop the district plan and establish the Review Committee

"Students connect polynomial arithmetic to computations with whole numbers and integers.  Students learn that the arithmetic of rational expressions is governed by the same rules as the arithmetic of rational numbers.  This unit helps students see connections between solutions to polynomial equations, zeros of polynomials, and graphs of polynomial functions.  Polynomial equations are solved over the set of complex numbers, leading to a beginning understanding of the fundamental theorem of algebra.  Application and modeling problems connect multiple representations and include both real world and purely mathematical situations.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

Module 2 builds on students' previous work with units and with functions from Algebra I, and with trigonometric ratios and circles from high school Geometry. The heart of the module is the study of precise definitions of sine and cosine (as well as tangent and the co-functions) using transformational geometry from high school Geometry. This precision leads to a discussion of a mathematically natural unit of rotational measure, a radian, and students begin to build fluency with the values of the trigonometric functions in terms of radians. Students graph sinusoidal and other trigonometric functions, and use the graphs to help in modeling and discovering properties of trigonometric functions. The study of the properties culminates in the proof of the Pythagorean identity and other trigonometric identities.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

"In this module, students synthesize and generalize what they have learned about a variety of function families.  They extend the domain of exponential functions to the entire real line (N-RN.A.1) and then extend their work with these functions to include solving exponential equations with logarithms (F-LE.A.4).  They explore (with appropriate tools) the effects of transformations on graphs of exponential and logarithmic functions.  They notice that the transformations on a graph of a logarithmic function relate to the logarithmic properties (F-BF.B.3).  Students identify appropriate types of functions to model a situation.  They adjust parameters to improve the model, and they compare models by analyzing appropriateness of fit and making judgments about the domain over which a model is a good fit.  The description of modeling as, “the process of choosing and using mathematics and statistics to analyze empirical situations, to understand them better, and to make decisions,” is at the heart of this module.  In particular, through repeated opportunities in working through the modeling cycle (see page 61 of the CCLS), students acquire the insight that the same mathematical or statistical structure can sometimes model seemingly different situations.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics."

Students build a formal understanding of probability, considering complex events such as unions, intersections, and complements as well as the concept of independence and conditional probability.  The idea of using a smooth curve to model a data distribution is introduced along with using tables and technology to find areas under a normal curve.  Students make inferences and justify conclusions from sample surveys, experiments, and observational studies.  Data is used from random samples to estimate a population mean or proportion.  Students calculate margin of error and interpret it in context.  Given data from a statistical experiment, students use simulation to create a randomization distribution and use it to determine if there is a significant difference between two treatments.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

(Nota: Esta es una traducción de un recurso educativo abierto creado por el Departamento de Educación del Estado de Nueva York (NYSED) como parte del proyecto "EngageNY" en 2013. Aunque el recurso real fue traducido por personas, la siguiente descripción se tradujo del inglés original usando Google Translate para ayudar a los usuarios potenciales a decidir si se adapta a sus necesidades y puede contener errores gramaticales o lingüísticos. La descripción original en inglés también se proporciona a continuación.)

"Los estudiantes conectan la aritmética polinomial con los cálculos con números enteros e enteros. Los estudiantes aprenden que la aritmética de las expresiones racionales se rige por las mismas reglas que la aritmética de los números racionales. Esta unidad ayuda a los estudiantes a ver conexiones entre soluciones a ecuaciones polinomiales, ceros de polinomiales,, y gráficos de funciones polinómicas. Las ecuaciones polinomiales se resuelven sobre el conjunto de números complejos, lo que lleva a una comprensión inicial del teorema fundamental del álgebra. Los problemas de aplicación y modelado conectan múltiples representaciones e incluyen situaciones de mundo real y puramente matemáticas.

Encuentre el resto de los recursos matemáticos de Engageny en https://archive.org/details/engageny-mathematics.

English Description:
"Students connect polynomial arithmetic to computations with whole numbers and integers.  Students learn that the arithmetic of rational expressions is governed by the same rules as the arithmetic of rational numbers.  This unit helps students see connections between solutions to polynomial equations, zeros of polynomials, and graphs of polynomial functions.  Polynomial equations are solved over the set of complex numbers, leading to a beginning understanding of the fundamental theorem of algebra.  Application and modeling problems connect multiple representations and include both real world and purely mathematical situations.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

(Nota: Esta es una traducción de un recurso educativo abierto creado por el Departamento de Educación del Estado de Nueva York (NYSED) como parte del proyecto "EngageNY" en 2013. Aunque el recurso real fue traducido por personas, la siguiente descripción se tradujo del inglés original usando Google Translate para ayudar a los usuarios potenciales a decidir si se adapta a sus necesidades y puede contener errores gramaticales o lingüísticos. La descripción original en inglés también se proporciona a continuación.)

"En este módulo, los estudiantes sintetizan y generalizan lo que han aprendido sobre una variedad de familias de funciones. Extienden el dominio de las funciones exponenciales a toda la línea real (n-rn.a.1) y luego extienden su trabajo con estas funciones a incluir la resolución de ecuaciones exponenciales con logaritmos (F-le.a.4). Exploran (con herramientas apropiadas) los efectos de las transformaciones en gráficos de funciones exponenciales y logarítmicas. Notan que las transformaciones en un gráfico de una función logarítmica se relacionan con el Propiedades logarítmicas (F-BF.B.3). Los estudiantes identifican tipos apropiados de funciones para modelar una situación. Ajustan los parámetros para mejorar el modelo y comparan los modelos analizando la idoneidad del ajuste y las juicios sobre el dominio sobre el cual un modelo es un buen ajuste. La descripción del modelado como, el proceso de elegir y usar matemáticas y estadísticas para analizar situaciones empíricas, comprenderlas mejor y tomar decisiones, está en el corazón de este módulo. En particular, a través de oportunidades repetidas para trabajar a través del ciclo de modelado (consulte la página 61 del CCLS), los estudiantes adquieren la idea de que la misma estructura matemática o estadística a veces puede modelar situaciones aparentemente diferentes.

Encuentre el resto de los recursos matemáticos de Engageny en https://archive.org/details/engageny-mathematics ".

English Description:
"In this module, students synthesize and generalize what they have learned about a variety of function families.  They extend the domain of exponential functions to the entire real line (N-RN.A.1) and then extend their work with these functions to include solving exponential equations with logarithms (F-LE.A.4).  They explore (with appropriate tools) the effects of transformations on graphs of exponential and logarithmic functions.  They notice that the transformations on a graph of a logarithmic function relate to the logarithmic properties (F-BF.B.3).  Students identify appropriate types of functions to model a situation.  They adjust parameters to improve the model, and they compare models by analyzing appropriateness of fit and making judgments about the domain over which a model is a good fit.  The description of modeling as, “the process of choosing and using mathematics and statistics to analyze empirical situations, to understand them better, and to make decisions,” is at the heart of this module.  In particular, through repeated opportunities in working through the modeling cycle (see page 61 of the CCLS), students acquire the insight that the same mathematical or statistical structure can sometimes model seemingly different situations.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics."

(Nota: Esta es una traducción de un recurso educativo abierto creado por el Departamento de Educación del Estado de Nueva York (NYSED) como parte del proyecto "EngageNY" en 2013. Aunque el recurso real fue traducido por personas, la siguiente descripción se tradujo del inglés original usando Google Translate para ayudar a los usuarios potenciales a decidir si se adapta a sus necesidades y puede contener errores gramaticales o lingüísticos. La descripción original en inglés también se proporciona a continuación.)

Los estudiantes crean una comprensión formal de la probabilidad, considerando eventos complejos como sindicatos, intersecciones y complementos, así como el concepto de independencia y probabilidad condicional. La idea de usar una curva suave para modelar una distribución de datos se introduce junto con el uso de tablas y tecnología para encontrar áreas bajo una curva normal. Los estudiantes hacen inferencias y justifican conclusiones de encuestas de muestra, experimentos y estudios de observación. Los datos se usan de muestras aleatorias para estimar una media o proporción de población. Los estudiantes calculan el margen de error y lo interpretan en contexto. Dados los datos de un experimento estadístico, los estudiantes usan la simulación para crear una distribución de aleatorización y lo usan para determinar si hay una diferencia significativa entre dos tratamientos.

Encuentre el resto de los recursos matemáticos de Engageny en https://archive.org/details/engageny-mathematics.

English Description:
Students build a formal understanding of probability, considering complex events such as unions, intersections, and complements as well as the concept of independence and conditional probability.  The idea of using a smooth curve to model a data distribution is introduced along with using tables and technology to find areas under a normal curve.  Students make inferences and justify conclusions from sample surveys, experiments, and observational studies.  Data is used from random samples to estimate a population mean or proportion.  Students calculate margin of error and interpret it in context.  Given data from a statistical experiment, students use simulation to create a randomization distribution and use it to determine if there is a significant difference between two treatments.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.