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Learning Goals & Products

Learning Goals

1

Students will be able to measure and record local weather data using simple tools to identify patterns in temperature, wind, cloud cover, and rainfall.

2

Students will be able to analyze class graphs, tables, and picture charts to explain weather patterns and make a simple prediction about what may happen next.

3

Students will be able to collaborate to test weather tools, compare results, and revise a forecast based on partner feedback and evidence.

Products

individual

Weather Observation Log with Picture Graph and Simple Prediction

Each student creates a personal weather research artifact using tool observations, a picture graph, and one written or drawn prediction. It shows how the student used firsthand evidence to understand one weather pattern affecting the local environment.

team

Community Garden Weather Report with Shared Data Display and Forecast

Small teams produce a shared weather report that combines their class data, a clear problem statement about local weather needs, and a low-tech forecast or presentation for the community garden. The team explains how observations and feedback shaped their final prediction and tool choices.

Rubric
Competency Progression Rubric Competency-first rubric
Category
Learning Goal
Stage 1
Stage 2
Stage 3
Stage 4
Deeper Learning Competencies
Critical Thinking & Problem Solving
  • I can use one weather tool to make a careful observation and record it in my picture chart or log, and I can name what it tells me about today’s weather
  • I can follow the class process to notice one simple pattern in the data we collect together.
  • I can use weather data from multiple tools (temperature, wind, clouds, or rainfall) to compare what is happening and explain what pattern I notice using my graph or picture chart
  • I can use that evidence to make a simple prediction about what the local environment might be like next.
  • I can analyze my team’s recorded weather measurements over time to describe how one weather variable changes (or stays the same) and why I think it matters
  • I can support my prediction with more than one piece of data and revise my thinking when new observations don’t match.
  • I can independently solve a weather problem by choosing which data to use and forming a clear explanation of the strongest pattern I see across variables
  • I can make a weather forecast that connects evidence from the graphs/tables to a specific, testable prediction for what may happen next in our community garden, and I can justify my reasoning confidently.
Deeper Learning Competencies
Effective Communication
  • I can share one weather fact I notice (like temperature, wind, clouds, or rain) using a simple sentence and point to a picture chart or class graph.
  • I can explain what my weather tool measurements show and connect them to a weather pattern I notice, using evidence from my team’s logs (with labels, numbers, or picture clues).
  • I can clearly communicate a short weather report by describing a pattern I found, showing the data I used, and making a simple prediction about what may happen next in our local environment.
  • I can present my weather findings confidently and with evidence by using graphs/tables/picture charts to justify both my pattern and prediction, answering a question from the audience and adjusting my explanation as needed.
Deeper Learning Competencies
Collaboration
  • I can work with my team to use one weather tool safely and record an observation (like temperature, wind, clouds, or rainfall) in our class weather chart or picture graph.
  • I can share tasks with my team to test tools and compare results, and I can explain what I notice using our data (for example, “We saw more clouds today”).
  • I can work with my team to make a simple plan for our weather report, take turns leading, and use partner feedback to improve our weather observation logs and graphs.
  • I can collaborate to solve problems during our weather station work, help teammates reach shared decisions, and clearly support my group’s prediction by pointing to patterns from our team’s data displays.