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Deeper Learning Competencies
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Collaboration
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- I can work with my team by sharing materials and taking on a clearly assigned task (like building one step, recording observations, or checking safety) while keeping others included in what we are doing
- I can follow our team’s simple plan and ask for help when I’m stuck so the group stays on track.
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- I can co-design with my team by contributing ideas and listening to others’ suggestions during planning, prototyping, and testing for our Rube Goldberg machine
- I can communicate specific cause-and-effect observations from our testing log and use them to revise our design together, staying respectful even when our build fails.
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- I can take shared responsibility for the design process by helping the team break our big goal (a safe, reliable machine that runs three times) into smaller problems and roles
- I can lead or support decision-making by comparing options against our criteria/constraints, resolving conflicts using evidence from test results, and documenting who did what and why in our revision notes.
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- I can independently strengthen collaboration by facilitating productive teamwork—making sure every voice is heard, negotiating roles based on need, and addressing disagreements with clear evidence from testing data
- I can help the team refine the system toward reliability by coordinating revisions across steps, supporting safe tool use, and improving our collective plan through feedback from debriefs and the showcase preparation.
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Deeper Learning Competencies
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Critical Thinking & Problem Solving
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- I can state the engineering design problem for our Rube Goldberg machine and list the success criteria and constraints (materials, time, safety, and the required task) using the teacher-provided checklist and lab rules.
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- I can generate and compare more than one possible design idea for each step of the machine and choose a plan that matches the criteria, explaining my thinking with specific evidence from the chain-reaction and build-it break-it trials.
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- I can apply scientific principles (force, motion, gravity, friction, and energy transfer) to design, test, and revise my solution, recording what I changed and how the results affected reliability toward running the machine three times.
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- I can independently refine and troubleshoot the machine as a system by breaking the problem into smaller parts, using prioritized evidence and tradeoffs from testing logs to propose targeted improvements that make my design consistently safe and reliable across repeated runs.
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Deeper Learning Competencies
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Effective Communication
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- I can clearly share my ideas and listen to my teammates during planning and testing by speaking in turn and asking at least one relevant question to understand what they noticed
- I can describe the task my machine completes in simple steps using basic cause-and-effect words (then, next, because).
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- I can present my machine plan and walkthrough to others using organized step-by-step explanations, including what triggers each part and what happens as a result
- I can use evidence from our testing log (what worked/what failed) to explain one change we made and why, using engineering words like force, motion, and energy transfer.
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- I can communicate my design thinking with accurate scientific/engineering language by explaining how forces and simple machines work in each step and how energy moves through the system
- I can share multiple options we considered and compare them based on criteria (safety, reliability, materials/time constraints), then clearly justify the final design choices using our evidence.
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- I can deliver an effective live engineering expo explanation by connecting every step of the chain reaction to forces, motion, gravity/friction (as applicable), and energy transfer in a way that others can follow and predict
- I can refine and improve my communication across rehearsals by responding to feedback from peers/visitors and using prioritized evidence (testing results and reliability runs) to support tradeoffs and next-step improvements.
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Deeper Learning Competencies
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Content Expertise
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- I can use class resources and teacher models to choose appropriate tools/materials and explain basic how-and-why ideas (force, motion, energy transfer, gravity, friction) for each step of my Rube Goldberg machine using correct, age-appropriate engineering vocabulary.
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- I can design and test a machine that meets several given criteria and constraints by selecting simple machines and arranging steps in a logical cause-and-effect sequence, and I can record evidence from testing to explain what scientific ideas worked or did not.
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- I can independently refine my design by comparing multiple approaches (mini-builds/prototypes) and using scientific principles to justify changes that improve reliability and safety, accurately identifying relevant forces, motion, and energy transfers in each labeled step.
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- I can evaluate and optimize a complex design using prioritized criteria and tradeoffs by proposing evidence-based revisions, explaining how energy moves through the full system, and demonstrating that my machine can run safely and reliably three times in a row while documenting the design cycle.
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Deeper Learning Competencies
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Self Directed Learning
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- I can use the project checklist, safety rules, and teacher prompts to plan what I will do next in the design/build cycle and to start documenting evidence in my testing log (what we tried and what happened).
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- I can independently choose and carry out the next step in my team’s design cycle by setting clear, reachable goals (e.g., improve one part or reduce a specific failure) and recording my results with enough detail to track changes over time.
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- I can use prior evidence from testing logs, debrief questions (“What worked
- What failed
- What will we change next?”), and peer feedback to revise my design decisions, justify the change, and update my documentation so it shows my thinking and impact on reliability.
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- I can self-direct my learning by evaluating tradeoffs using prioritized criteria (safety, clear task completion, reset efficiency, and running three times reliably), proposing testable improvements, monitoring progress toward the reliability goal, and revising plans based on data and reflection.
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Deeper Learning Competencies
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Academic Mindset
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- I can name what success looks like for my Rube Goldberg machine and explain (in my own words) the constraints I must follow (like safety rules and materials/time limits) when making design decisions.
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- I can use evidence from testing logs, sticky-note feedback, and peer debriefs to set a short next-step goal for revising my machine, and I can describe why I am choosing that change based on criteria (function, safety, reliability).
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- I can evaluate my design tradeoffs using scientific and engineering ideas (force, motion, gravity, friction, and energy transfer) and refine my plan independently after failures, showing clear cause-and-effect reasoning in my revision notes.
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- I can consistently monitor my progress toward reliability (running three times in a row), troubleshoot proactively, and revise with confidence by selecting among multiple solution options and justifying my final choices with prioritized evidence and constraints.
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