8th November 28 -- December 2

Monday:

Test day.  We will be testing student devices.  While students are testing they will be graphing and writing their CERs.

Tuesday:

We will take a deeper dive into the science behind why some devices worked well and others did not.  This will provide students a better opportunity to edit/adjust/add the science into their CERs.  CERs are due Wednesday.

Wednesday:

Mi-Star Weather Unit Lesson 1

We will be kicking off our next Mi-star unit which will be focused on weather.  In lesson one, we will answer the question: "What causes snowfall patterns in Michigan?"

Anchoring Experience - Students will interact with a snowfall calculator.

Sharing - Students will watch a video that revisits weather vs climate.Connect - Students will be put into Unit Challenge Scenario Teams and will be introduced to the Unit Challenge.

Thursday:

We will revisit the unit challenge scenario and as a class create a classroom bubble.

1. Each student writes 5 of their own questions

2. Table groups share their questions and then put them in categories

3. Groups share out their categories and we put them on the board

4. Groups select best questions to put onto the board

Friday:

Mi-Star Weather Unit Lesson 2

1. Social Media Posts

2. Intro weather event video.

3. Student groups engage in an investigation of the November, 2014 Buffalo NY snowstorm. Students use Storm Resource Cards to organize weather data from Lake Erie and 5 land-based locations which experienced different impacts from that storm. This information is used to construct a land-lake-atmosphere model and an initial explanation of the Buffalo snowstorm phenomenon.  Student groups compare their models and initial explanations of how lake effect snow occurs and discuss similarities and differences among the different versions. The class comes to consensus about their explanations as a formal way to explain how large bodies of water, such as the Great Lakes, can create snowfall patterns on land. Students revisit their Uncover models and add components/processes to improve their models.