Middle School Curriculum Overview
The AMP-IT-UP middle school curriculum includes three major components. All emphasize the integration of science, mathematics and engineering within an inquiry and problem-based challenge. The components, described in more detail below, are:
- 18-week STEM-Innovation and Design (STEM-ID) courses for 6th, 7th and 8th grade.
- 1-week mathematics modules—three for each middle school grade.
- 1-week science modules—three for each middle school grade.
The STEM-ID courses are 18-week integrated connections courses that can be taught alongside core math and science classes, either as Engineering and Technology classes or as general STEM classes. The curriculum requires that students use the engineering design process within a problem-based learning context, and that they actively practice foundational mathematics skills and NGSS-aligned scientific practices to solve engaging challenges.
Each course is divided into a series of four challenges entitled the Data Challenge, Systems Challenge, Visualization Challenge, and Design Challenge. The first three build different skills, and the fourth is a multi-week design challenge that pulls the experience together. All courses in the multi-year sequence follow a similar trajectory and incorporate many of the same skills, but within different contexts and with increasingly more challenging technological manipulatives. By the 8th grade, students are rendering designs in 3D-modeling software, using a 3D printer to create prototypes, testing their product, and iterating on the design.
Mathematics and Science Modules
The math and science modules emphasize the integration of mathematics and science practices with grade-level specific disciplinary content. Practices are grouped together into themes that relate to the collection, visualization, interpretation and communication of data. The three themes are 1) Experimental Design, 2) Data Visualization, and 3) Data-Driven Decision Making. Each module concentrates on one of these themes as students collect and manipulate data in order to answer a challenge
The Experimental Design theme emphasizes concepts included in NGSS Practice #3: Planning and Carrying Out Investigations, Standards of Mathematical Practice (SMP) #1: Make Sense of Problems (e.g. plan a solution pathway), and SMP #5: Use Appropriate Tools Strategically. When engaging in these practices, students identify and control variables, create procedures, conduct experiments, use data-collection tools, and collect and analyze data.
The Data Visualization theme includes concepts from NGSS Practice #4: Analyzing and Interpreting Data (e.g. making and using graphical displays), SMP #1: Make Sense of Problems (e.g. graph data and search for regularity or trends) and SMP #4: Model with Mathematics (e.g. map relationships using diagrams, two-way tables, graphs). The emphasis of this theme is that data can be represented in multiple ways, that different types of visualizations enable people to extract different meaning from the evidence, and that the best data visualization is the representation that most effectively illustrates the concept that the user wants to communicate.
The Data-Driven Decision-Making theme asks students to make decisions or design solutions based on data in circumstances where there isn’t a simple solution and where trade-offs may exist. The modules introduce decision matrices as a tool for organizing data to extract meaning and inform decisions, and students then communicate and defend their decisions. This theme incorporates NGSS Practice #6: Constructing Explanations and Designing Solutions, as well as NGSS Practice #7: Engaging in Argument from Evidence, and the communication component of NGSS Practice #8: Obtaining, Evaluating and Communicating Information. It also supports the math standards of SMP #1: Make Sense of Problems (e.g. analyze givens, constraints, relationships and goals), and SMP #3: Construct viable arguments
Advanced Manufacturing and Prototyping Integrated To Unlock Potential (AMP-IT-UP)
is made possible by a grant from the National Science Foundation (Award Number: 1238089)