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Deeper Learning Competencies
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Critical Thinking & Problem Solving
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- I can test a simple game rule by running a few trials (with dice/cards/spinners), recording results in a table, and comparing what happened to my initial prediction
- I can notice patterns in repeated outcomes and explain what seems to be happening.
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- I can use the structure of the game (rules, scoring, and possible outcomes) to make clearer predictions and plan a fair test by choosing tools and repeats
- I can describe how my results match or differ from expected probability, using evidence from my test log and graphs to support my reasoning.
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- I can find regularity across many trials and revise my predictions or rules when data shows an imbalance
- I can construct a viable argument about whether my game is fair by linking rule choices to expected outcomes, and I can critique alternative explanations by pointing to specific data.
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- I can independently and strategically test multiple design changes, using math reasoning (mean and probability comparisons) to evaluate fairness and identify which specific rule features cause advantage or randomness
- I can communicate a strong, evidence-based fairness claim that uses repeated reasoning, clear graphs/tables, and calculations to defend my final design and predict future results.
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Deeper Learning Competencies
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Collaboration
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- I can work with my teammates to make shared game-design decisions (rules, scoring, and materials) and I can clearly follow the plan we agree on during test rounds.
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- I can collaborate to revise our game based on evidence from repeated trials, using structured feedback (like sticky notes or a test log) to propose specific rule changes and explain why they may improve fairness.
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- I can actively critique and improve my teammates’ reasoning by asking questions, comparing expected vs
- actual outcomes, and proposing data-based fixes to the game’s structure (turns, chances, and scoring) to better balance player experiences.
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- I can independently lead collaboration by organizing roles and test workflows, synthesizing team and community play-tester feedback with mathematical evidence (tables/graphs/probability calculations), and coordinating revisions so our final game is both clear and fair for all players.
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Deeper Learning Competencies
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Effective Communication
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- I can clearly explain the game’s rules and scoring using student-made materials (rule card, examples, and simple directions) so another player can start without asking for help.
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- I can use math vocabulary to communicate predictions and results by referencing my trial data (table/graph) and comparing “expected vs
- actual” with a few sentences about what patterns I notice.
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- I can construct a viable fairness argument by stating a claim about balance, supporting it with sample probability calculations and evidence from repeated trials, and addressing at least one critique from a peer or play-tester.
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- I can refine and communicate a strong, structured fairness defense by linking rule changes to specific data patterns, critiquing others’ reasoning respectfully during play-testing, and presenting my findings so visitors understand both why it’s fair and how to play it.
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Deeper Learning Competencies
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Content Expertise
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- I can use dice, cards, or spinners to run repeated game trials and record clear results in a test log (with counts for outcomes) that match the game I built
- I can calculate expected outcomes for one or two key rules using simple probability ideas (like equally likely outcomes).
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- I can design rules and a scoring system that have consistent structure (e.g., defined outcomes, clear win/loss conditions, and repeatable setup) and I can use appropriate tools (tables, spinners, calculators, or graphing tools) to collect and organize experimental data
- I can calculate expected probabilities for key outcomes, run multiple rounds, compute a mean or comparison, and explain how my expected and actual results relate.
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- I can use patterns from repeated reasoning to identify whether my game is fair and make targeted revisions to improve balance (changing rule values, outcome chances, or scoring) based on evidence from my tables/graphs
- I can support my fairness claims with data-based probability calculations and show a clear comparison between expected vs
- experimental results, including how structure in my rules leads to specific outcomes.
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- I can independently test, analyze, and refine my game using a complete fairness process: predicting probabilities from the rule structure, running sufficient repeated trials, graphing results, and using mean/variation to justify conclusions
- I can construct and critique viable arguments using precise math evidence (including sample probability calculations) to defend which rules make the game fair for different players and describe why changes improved balance and clarity.
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Deeper Learning Competencies
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Self Directed Learning
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- I can follow the game-design process and use teacher check-ins to choose next steps, like revising rules or updating my test log after I notice mismatches between predictions and results.
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- I can independently monitor my progress by tracking what I predicted, what actually happened, and what feedback said, then make clear revisions to my game using structured math evidence (tables/graphs, mean, and probability comparisons).
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- I can use repeated trials to look for patterns, explain what the patterns mean for fairness, and revise my game rules on my own when my data shows regular results that are not balanced for all players.
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- I can independently run and refine a testing plan, use appropriate tools (like spreadsheets/calculators and graphs) to verify fairness, critique my own reasoning with MP3-style checks, and justify final design changes with strong, organized evidence.
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