|
Deeper Learning Competencies
|
Critical Thinking & Problem Solving
|
- I can use digital tools to collect baseline data from my mica-radish controlled-environment trials (e.g., light, moisture, temperature, pH, and plant size) and organize it in a spreadsheet with clear labels so my team can compare results.
|
- I can analyze my collected radish data by using spreadsheet tools (sorting, filtering, and formulas) to break information into patterns and explain how specific variables may affect growth using evidence from graphs or summary statistics.
|
- I can pose testable questions about which controlled factors matter most and use my data analysis to justify claims, including identifying trends, outliers, and what additional evidence would strengthen my decision.
|
- I can apply a data-informed problem-solving process to evaluate and improve my system by connecting multiple variables, critiquing the quality of my evidence, and using technology-assisted or algorithmic routines to support an evidence-based conclusion about which environment produces the most successful radishes.
|
|
Deeper Learning Competencies
|
Self Directed Learning
|
- I can use a checklist and feedback prompts to gather baseline and growth data with digital tools (photos and measurements) and enter it into shared spreadsheets so my team can track results over time.
|
- I can set specific learning goals for the radish trials, use peer/teacher feedback to revise how I collect or organize data, and apply spreadsheet tools (sorting, formulas, basic charts) to identify patterns that help explain what is happening.
|
- I can independently break down my data into key information, choose which variables and measurements matter most, and use technology-assisted methods to support my analysis and refine my testable claims about which controlled environment is working.
|
- I can monitor my progress toward improving the investigation, use multiple rounds of feedback and reflection to troubleshoot and strengthen my automated monitoring/testing routine, and justify evidence-based decisions about what “success” looks like using clear, well-prepared data for presentation.
|
|
Deeper Learning Competencies
|
Collaboration
|
- I can work with my team to share roles for the radish controlled-environment challenge and consistently contribute to collecting and organizing shared evidence (photos, baseline measurements, and spreadsheet entries) in the correct locations.
|
- I can collaborate by using shared digital tools and clear protocols to coordinate data collection and reduce errors (e.g., naming conventions, input checks, and version control), and I can use peer/teacher feedback to improve our measurements and organization.
|
- I can co-design and refine team strategies by breaking down our dataset to identify key patterns and questions, then I can propose changes to our trial procedures or data dashboard based on what the data suggests.
|
- I can lead collaborative decision-making by proposing and testing improvements to our automated monitoring or testing routines (timers/sensors/formsulas), coordinating with teammates to implement them, and using evidence-based analysis to justify our team’s next steps and claims.
|
|
Deeper Learning Competencies
|
Content Expertise
|
- I can use digital tools to collect baseline radish-growth data (e.g., light, moisture, temperature, pH, and plant size) during my mica-radish farm trials and enter it into shared spreadsheets so the information is accurate and easy to find.
|
- I can use digital tools and simple analysis (sorting, basic calculations, and visualization) to break down my dataset and identify key patterns in how controlled variables relate to radish growth, preparing my findings for presentation with clear evidence.
|
- I can use technology-assisted methods to organize, clean, and compare data across multiple growing systems, and I can explain which measurements best support my testable claims using data trends, comparisons, and justified interpretation.
|
- I can independently design and refine an automated monitoring or testing routine (using sensors/timers/formulas and algorithmic thinking) that supports my investigation, and I can analyze results to make evidence-based decisions about which controlled environment most reliably leads to success.
|
|
Deeper Learning Competencies
|
Academic Mindset
|
- I can use digital tools to collect baseline radish-growing data (light, moisture, temperature, pH, and plant size) and record it in organized spreadsheets with teacher-provided categories so I can track what is happening in my trials.
|
- I can use feedback and my own reflections to improve how I collect and organize data by checking for missing variables, correcting recording issues, and refining spreadsheets so key information becomes easier to compare across growing systems.
|
- I can independently identify patterns or questions in my data, break the data into key categories using technology-assisted methods, and revise my controlled-environment approach to better test what could drive radish success.
|
- I can set personal learning goals, monitor my progress through data and reflection, and use digital tools and algorithmic thinking to guide decisions by proposing and testing automated monitoring/testing routines that strengthen evidence about which conditions lead to the strongest results.
|
|
Deeper Learning Competencies
|
Effective Communication
|
- I can use digital tools (photos, measurements, and logs) to collect and organize radish growth data in shared spreadsheets so my team can clearly present what we observed during the controlled-environment trials
- I can label variables and units and record baseline measurements accurately enough that others can understand my data.
|
- I can use technology-assisted tools (such as spreadsheet functions, filters, and simple breakdowns) to identify key information in our data and help decide what patterns might relate to radish growth
- I can communicate my findings with clear visuals (tables/graphs) and explain how the data supports my team’s next steps.
|
- I can translate my analysis into a clear, evidence-based explanation by connecting specific data trends to controlled-environment variables (light, moisture, temperature, pH, size) in a presentation or poster
- I can incorporate peer/OSU Extension feedback to revise my visuals, wording, and data organization so my claims are more precise and easier to evaluate.
|
- I can independently craft a polished, audience-ready communication that integrates data collection, key insights, and limitations from multiple trials to answer our community-relevant question about farming decisions
- I can also describe how our simple automated monitoring/testing routine supports our conclusions and present confidently, using feedback to strengthen clarity, accuracy, and impact for different community audiences.
|