In recent years, education leaders and practitioners have contemplated the power of learning with integrated STEM curricula and methods. While there are many thoughts and ideas on such a subject, no one definition of integrated STEM has come to the forefront. AMP-IT-UP project leaders saw many ways to design and implement integrated STEM curriculum, where any one version had its benefits of best practices and its barriers to success. For example, a “single classroom” model where math, science, and engineering are taught in one three our block runs against the realities of school scheduling and qualified teaching professionals to manage such a learning space and structure.
After reviewing multiple models and conducting good diligence of the school system’s structure and needs, AMP-IT-UP project leaders landed on one version that best met the project’s vision: identify the common, universal concepts that students must learn in their each of their middle school science, math, and engineering courses. The team would build learning experiences around those concepts, so that the lessons and skills developed would be iteratively and spirally built throughout grades 6-8. The curriculum would anchor to these three concepts:
1. Planning and Carrying-Out Investigations
2. Analyzing and Interpreting Data
3. Data-and-Evidence-Based Decision Making
In each of the science, math, and engineering courses, students use AMP-IT-UP curricula explicitly designed around these themes. The engineering curriculum is a semester-long series of design challenges. The science and math classes use shorter-term modules to develop understanding of these themes. The modules usually take 4-6 days to complete, and they address a wide array of state and national content and skill standards. The intention of the modules is 1) introduce the concepts, priming the pump for students to return to these concepts in future lessons developed by the teachers, and 2) model for teachers how to teach with these themes using inquiry and real-world settings.
AMP-IT UP provides project teachers professional development sessions, online support, and individual meetings. The intention is to provide teachers with a protocol and “eye” for types of activities to use in their future units. For example, students design procedures as a part of one science module. As students move through the school-year, the teacher will adapt existing, future activities and experiments where students would re-engage in procedural design activity and lessons from the module. In this way, the modules seed the concepts for future development and growth in all three courses.
Data Visualization: Analyzing and Interpreting Data
Analyzing data in 6th through 8th grades build on K-5 experiences and progress to extending quantitative analysis to investigations, distinguishing between various data representations (e.g. spatial vs. temporal, causal, linear vs. non-linear), and basic statistical techniques of data and and error analysis. Focus is on using data visuals (e.g. graphs, maps, pictograms) to analyze change and communicate trends for an audience.
- Math Curriculum:
- Draw diagrams of important features and relationships and graph data.
- Map relationships using such tools as diagrams and two-way tables, graphs flowcharts, and formulas.
- Make sense of quantities and their relationships in problem situations.
- Create a coherent representation of the problem at hand.
Search for regularity or trends and analyze given, constraints, relationships, and goals.
- Science Curriculum:
- Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships.
- Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships.
- Distinguish between causal and correlational relationships in data.
- Analyze and interpret data to provide evidence for phenomena.
- Apply concepts of statistics and probability (including mean, median, mode, and variability) to analyze and characterize data, using digital tools when feasible.
- Consider limitations of data analysis (e.g., measurement error), and/or seek to improve precision and accuracy of data with better technological tools and methods (e.g., multiple trials).
- Analyze and interpret data to determine similarities and differences in findings.
- Analyze data to define an optimal operational range for a proposed object, tool, process or system that best meets criteria for success.
Decision Making: Data and Evidence Based Decision Making
The data-driven decision making modules ask students to analyze data and situations that are intentionally murky, and to make decisions or design solutions based on data, but where there is not a simple solution and instead they need to address various trade-offs. Students then need to communicate and defend their decisions. These modules support the science practices of constructing explanations and designing solutions, engaging in argument from evidence, and communicating information. The modules also support the math standards of analyzing givens, constraints, relationships and goals, and constructing viable arguments. Decision making modules introduce decision matrices as a tool for organizing data to extract meaning and inform decisions.