{"id":635,"date":"2022-02-16T11:21:59","date_gmt":"2022-02-16T16:21:59","guid":{"rendered":"https:\/\/dev4.legawebdesign.com\/?page_id=635"},"modified":"2024-09-23T13:22:35","modified_gmt":"2024-09-23T17:22:35","slug":"cocorahs-jan","status":"publish","type":"page","link":"https:\/\/horizon-research.com\/tl4cs\/cocorahs\/cocorahs-jan\/","title":{"rendered":"CoCoRaHS, 5: January"},"content":{"rendered":"

[et_pb_section fb_built=”1″ custom_padding_last_edited=”on|phone” admin_label=”Header” _builder_version=”4.16″ background_enable_image=”off” background_size=”contain” background_position=”top_right” custom_padding=”40px||||false|false” custom_padding_tablet=”40px|0px|50px|0px|false|false” custom_padding_phone=”30px||||false|false” global_colors_info=”{}”][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.16″ max_width=”1280px” use_custom_width=”on” custom_width_px=”1280px” global_colors_info=”{}”][et_pb_column type=”2_3″ module_class=”engage-inline-button” _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_line_height=”1.6em” header_font=”EB Garamond||||||||” header_font_size=”56px” header_line_height=”1.3em” header_2_font=”|700|||||||” header_2_font_size=”30px” header_3_font=”|600|||||||” header_3_font_size=”26px” header_3_line_height=”1.4em” header_4_font=”|700||on|||||” header_4_text_color=”#89CEA4″ header_4_font_size=”16px” header_4_letter_spacing=”1px” header_4_line_height=”1.8em” max_width=”900px” custom_margin=”||14px|||” text_font_size_tablet=”” text_font_size_phone=”15px” text_font_size_last_edited=”on|desktop” text_line_height_tablet=”” text_line_height_phone=”1.6em” text_line_height_last_edited=”on|phone” header_font_size_tablet=”50px” header_font_size_phone=”32px” header_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n

5: January<\/h2>\n

[\/et_pb_text][et_pb_code _builder_version=”4.16″ _module_preset=”default” width=”40px” module_alignment=”right” custom_margin=”-54px||10px||false|false” global_colors_info=”{}”]

<\/div>[\/et_pb_code][et_pb_text _builder_version=”4.21.0″ text_font=”||||||||” text_line_height=”1.6em” header_font=”EB Garamond||||||||” header_font_size=”56px” header_line_height=”1.3em” header_2_font=”|700|||||||” header_2_font_size=”30px” header_3_font=”|600|||||||” header_3_font_size=”26px” header_3_line_height=”1.4em” header_4_font=”|700||on|||||” header_4_text_color=”#89CEA4″ header_4_font_size=”16px” header_4_letter_spacing=”1px” header_4_line_height=”1.8em” max_width=”900px” custom_margin=”||14px|||” text_font_size_tablet=”” text_font_size_phone=”15px” text_font_size_last_edited=”on|desktop” text_line_height_tablet=”” text_line_height_phone=”1.6em” text_line_height_last_edited=”on|phone” header_font_size_tablet=”50px” header_font_size_phone=”32px” header_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n

Comparing your county\u2019s precipitation to counties in different regions<\/h4>\n

Purpose<\/strong>
Building on last month\u2019s comparisons of data from stations in the same region, this activity has students make sense of how geographic location and features can result in differences in precipitation <\/span>across<\/b> regions by comparing their own station\u2019s fall data (meteorological seasonal total) with that of other stations located in different regions, and exploring differences in these locations. <\/span><\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_line_height=”1.6em” global_colors_info=”{}”]<\/p>\n

\"\"<\/p>\n

Review last month\u2019s graph and data sheets where students compared their monthly <\/span>and fall totals for their school and another station in the same region (mountain, piedmont, or coast). Then, without disclosing the stations\u2019 location, provide students with fall\"\"\u00a0totals for stations in each of the two NC regions outside their own. Ask students to record and compare the decimal values with their data from last month. Students don\u2019t need to graph these values, they may use other strategies (such as number lines and organizing with > or < symbols) to compare decimals. Before reviewing the geographic location of the other stations, ask students to record reasons why their data may differ from the other stations\u2019 data.<\/p>\n

Using a<\/span> map of North Carolina<\/span><\/a>, locate the other stations, noting the different regions. Support students in discussing the geographic features as respective differences between regions based on location (proximity to water), and other geographic features (e.g., elevation, rain shadow). If possible, keep this map with other locations marked on display for your class to reference.<\/span><\/p>\n

[\/et_pb_text][et_pb_button button_url=”@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF9saW5rX3VybF9wYWdlIiwic2V0dGluZ3MiOnsicG9zdF9pZCI6IjYzMyJ9fQ==@” button_text=”Previous: 4: December” button_alignment=”left” _builder_version=”4.16″ _dynamic_attributes=”button_url” _module_preset=”default” custom_button=”on” button_text_size=”14px” button_border_width=”0px” button_icon=”#||divi||400″ button_icon_placement=”left” button_on_hover=”off” custom_padding=”|20px|||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_button button_url=”@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF9saW5rX3VybF9wYWdlIiwic2V0dGluZ3MiOnsicG9zdF9pZCI6IjYzNyJ9fQ==@” button_text=”Next: 6: February” button_alignment=”right” _builder_version=”4.16″ _dynamic_attributes=”button_url” _module_preset=”default” custom_button=”on” button_text_size=”14px” button_border_width=”0px” button_icon=”$||divi||400″ button_on_hover=”off” custom_padding=”|||20px|false|false” custom_css_main_element=”text-align:right !important;” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_font_size=”14px” text_line_height=”1.3em” header_font=”EB Garamond||||||||” header_font_size=”56px” header_line_height=”1.3em” header_2_font=”|700|||||||” header_2_font_size=”30px” header_3_font=”|600|||||||” header_3_font_size=”26px” header_3_line_height=”1.4em” header_4_font=”|700||on|||||” header_4_text_color=”#89CEA4″ header_4_font_size=”16px” header_4_letter_spacing=”1px” header_4_line_height=”1.8em” custom_margin=”||2px|||” header_font_size_tablet=”50px” header_font_size_phone=”32px” header_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n

Standards + Practices<\/h4>\n

[\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_font_size=”14px” text_line_height=”1.3em” header_font=”EB Garamond||||||||” header_font_size=”56px” header_line_height=”1.3em” header_2_font=”|700|||||||” header_2_font_size=”30px” header_3_font=”|600|||||||” header_3_font_size=”26px” header_3_line_height=”1.4em” header_4_font=”|700||on|||||” header_4_text_color=”#89CEA4″ header_4_font_size=”16px” header_4_letter_spacing=”1px” header_4_line_height=”1.8em” min_height=”109.8px” custom_margin=”||2px|||” header_font_size_tablet=”50px” header_font_size_phone=”32px” header_font_size_last_edited=”on|phone” global_colors_info=”{}”]\"\"[\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font=”||||||||” text_font_size=”13px” text_line_height=”1.4em” custom_margin=”||12px||false|false” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Science Standards: <\/strong><\/p>\n

ESS.5.1.1 <\/strong>Analyze and interpret data to compare daily and seasonal changes in weather conditions and patterns<\/p>\n

ESS.5.1.2 <\/strong>Analyze and interpret weather data to explain current and upcoming weather conditions in a given location<\/p>\n

ESS.5.1.3 <\/strong>Construct an explanation to summarize the ocean\u2019s influences on weather and climate in North Carolina<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font=”||||||||” text_font_size=”13px” text_line_height=”1.4em” custom_margin=”||12px||false|false” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Science Practices
<\/strong>SP4: Analyze and interpret data
SP5: Using mathematics and computational thinking<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font=”||||||||” text_font_size=”13px” text_line_height=”1.4em” custom_margin=”||12px||false|false” global_colors_info=”{}”]<\/p>\n

Math Standards:<\/strong><\/p>\n

NC.5.NBT.3 <\/strong>Read, write, and compare decimals to thousandths.<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” text_font=”||||||||” text_font_size=”13px” text_line_height=”1.4em” custom_margin=”||32px||false|false” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Math Practices
<\/strong>MP2: Reason abstractly and quantitatively
MP4: Model with mathematics
MP6: Attending to precision<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_font_size=”14px” text_line_height=”1.3em” header_font=”EB Garamond||||||||” header_font_size=”56px” header_line_height=”1.3em” header_2_font=”|700|||||||” header_2_font_size=”30px” header_3_font=”|600|||||||” header_3_font_size=”26px” header_3_line_height=”1.4em” header_4_font=”|700||on|||||” header_4_text_color=”#89CEA4″ header_4_font_size=”16px” header_4_letter_spacing=”1px” header_4_line_height=”1.8em” custom_margin=”||2px|||” header_font_size_tablet=”50px” header_font_size_phone=”32px” header_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n

Resources + Supports<\/h4>\n

[\/et_pb_text][et_pb_blurb image=”http:\/\/www.horizon-research.com\/tl4cs\/wp-content\/uploads\/2022\/02\/Asset-18.png” icon_placement=”left” image_icon_width=”100%” _builder_version=”4.16″ _module_preset=”default” body_line_height=”1.4em” custom_margin=”||10px||false|false” global_colors_info=”{}”]<\/p>\n

Ms. Clark Uncovers Students\u2019 Thinking About Differences in Precipitation<\/a><\/p>\n

[\/et_pb_blurb][et_pb_blurb image=”http:\/\/www.horizon-research.com\/tl4cs\/wp-content\/uploads\/2022\/02\/Asset-25.png” icon_placement=”left” image_icon_width=”100%” _builder_version=”4.16″ _module_preset=”default” body_line_height=”1.4em” custom_margin=”||10px||false|false” global_colors_info=”{}”]<\/p>\n

Analyzing and Interpreting Data<\/a><\/p>\n

[\/et_pb_blurb][et_pb_blurb image=”http:\/\/www.horizon-research.com\/tl4cs\/wp-content\/uploads\/2022\/02\/Asset-26.png” icon_placement=”left” image_icon_width=”100%” _builder_version=”4.16″ _module_preset=”default” body_line_height=”1.4em” custom_margin=”||10px||false|false” global_colors_info=”{}”]<\/p>\n

Making Sense of Trends in Weather Data<\/a><\/p>\n

[\/et_pb_blurb][et_pb_blurb image=”http:\/\/www.horizon-research.com\/tl4cs\/wp-content\/uploads\/2022\/02\/Asset-20.png” icon_placement=”left” image_icon_width=”100%” _builder_version=”4.16″ _module_preset=”default” body_line_height=”1.4em” custom_margin=”||10px||false|false” global_colors_info=”{}”]<\/p>\n

Considering Geographic Features<\/a><\/p>\n

[\/et_pb_blurb][et_pb_blurb image=”http:\/\/www.horizon-research.com\/tl4cs\/wp-content\/uploads\/2022\/02\/Asset-28.png” icon_placement=”left” image_icon_width=”100%” _builder_version=”4.16″ _module_preset=”default” body_line_height=”1.4em” global_colors_info=”{}”]<\/p>\n

February Preview<\/a><\/p>\n

[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Objectives” module_id=”narrative” _builder_version=”4.16″ background_enable_color=”off” custom_padding=”35px|||||” border_width_top=”8px” border_color_top=”#89CEA4″ global_colors_info=”{}”][et_pb_row column_structure=”1_4,3_4″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” admin_label=”Module Heading with Icon” _builder_version=”4.16″ custom_padding=”||1px|||” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” custom_css_main_element=”width: 18%!important;” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”http:\/\/www.horizon-research.com\/tl4cs\/wp-content\/uploads\/2022\/02\/Asset-18.png” title_text=”Asset 18″ show_bottom_space=”off” align=”center” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ _module_preset=”default” header_font=”EB Garamond||||||||” header_text_align=”left” header_text_color=”#454545″ header_font_size=”42px” header_line_height=”2.2em” header_3_font=”|700|||||||” header_3_text_color=”#454545″ header_3_font_size=”26px” header_line_height_tablet=”1.2em” header_line_height_phone=”1.2em” header_line_height_last_edited=”on|tablet” global_colors_info=”{}”]<\/p>\n

Ms. Clark Uncovers Students\u2019 Thinking About Differences in Precipitation<\/b><\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Ms. Clark’s students have found success identifying patterns in precipitation, but she wonders how they think about the factors that affect those patterns. She is aware of many strategies for uncovering students’ thinking, but in this case, she decides to spend a class period having students make and discuss scientific claims about where in the state (and the related geographic features) the other stations are located based on their precipitation totals. Using information gathered through class discussion, Ms. Clark will select resources that will help students further their understanding the following day.\u00a0<\/span><\/p>\n

Prior to disclosing the location of the station (i.e., in the mountains or coastal region), Ms. Clark reviews the data reports from each station with her students (e.g., to check for missing data). Once they determine they are working with complete data sets, Ms. Clark shares the total precipitation recorded by the two stations outside their region. Ms. Clark writes the two totals on the board: <\/span>Station A: 22.24 inches<\/span><\/i> and <\/span>Station B: 18.20 inches<\/span><\/i> and asks students what they notice about the two values.\u00a0<\/span><\/p>\n

One student replies, \u201cBoth are more than ours! We measured less than 17 inches in the fall.\u201d Another student adds, \u201cOne is only about an inch more, and the other is about 5 inches more!\u201d Ms. Clark asks the students to write the two totals down and then record a claim about where in the state they think each station is located, providing a reason for why they think that. <\/span><\/p>\n

After students share their ideas with a partner, Ms. Clark asks students to share with the class, and she makes a list of student ideas about each station. One student shared, \u201cI think that the station that recorded 18.20 inches is close to us, because the total was similar to what our station recorded.\u201d Under Station B, Ms. Clark adds \u201cclose to us\u201d but then asks the class what should count as \u201cclose.\u201d After discussion, the class decides on \u201c50 miles or less.\u201d\u00a0<\/span><\/p>\n

Another student offers a claim about Station A, \u201cI think that station is near the coast because they recorded over 22 inches, and that was probably because of hurricane season.\u201d\u00a0<\/span><\/p>\n

Ms. Clark\u2019s class has generated and recorded several claims as the class period is coming to an end. Reviewing the lists of student ideas, Ms. Clark organizes these student ideas about factors that might explain the differences in rainfall as a record of what students know and understand, at that point, about the factors that account for differences in rainfall. She will use this information to begin instruction the next day. Knowing that Station A is actually located in the mountains (not on the coast as many students thought), Ms. Clark expects this revelation will surprise students. She gathers some topographic maps and resources related to \u201crain shadows\u201d to help students understand how the other station\u2019s location (east of the mountains) affects its precipitation. Additionally, to help students understand that being in close proximity doesn\u2019t always mean similar precipitation, Ms. Clark downloads a small data set from a station very close to their school to emphasize that \u201crain doesn\u2019t fall the same on all,\u201d even if you are just a few miles apart. <\/span><\/p>\n

After the students have examined the additional data, and after considering the factors that are believed to account for the differences in those data, the students and teacher compile a revised set of factors that students know and understand. The teacher can then call students’ attention to how this is similar to the way that science works: hypotheses are formed based on current scientific knowledge and that knowledge is revised and grown as new scientific evidence is acquired.<\/span><\/p>\n

Additional information on using topographic maps can be found in the content resources.<\/a> <\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Objectives” module_id=”graphingtips” _builder_version=”4.16″ background_enable_color=”off” custom_padding=”35px|||||” border_width_top=”8px” border_color_top=”#89CEA4″ global_colors_info=”{}”][et_pb_row column_structure=”1_4,3_4″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” admin_label=”Module Heading with Icon” _builder_version=”4.16″ custom_padding=”||1px|||” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” custom_css_main_element=”width: 10%!important;” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”http:\/\/www.horizon-research.com\/tl4cs\/wp-content\/uploads\/2022\/02\/Asset-25.png” title_text=”Asset 25″ show_bottom_space=”off” align=”center” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ _module_preset=”default” header_font=”EB Garamond||||||||” header_text_align=”left” header_text_color=”#454545″ header_font_size=”42px” header_line_height=”2.2em” header_3_font=”|700|||||||” header_3_text_color=”#454545″ header_3_font_size=”26px” header_line_height_tablet=”1.2em” header_line_height_phone=”1.2em” header_line_height_last_edited=”on|tablet” global_colors_info=”{}”]<\/p>\n

Analyzing and Interpreting Data<\/b><\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

As students compare their seasonal data with other stations\u2019 data in different regions, there will be a strong focus on making sense of the data within the graphs. Refer to the <\/span>Practice Overview<\/b> in December<\/a> for tips on Analyzing and Interpreting Data. <\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Objectives” module_id=”takingnotes” _builder_version=”4.16″ background_enable_color=”off” custom_padding=”35px|||||” border_width_top=”8px” border_color_top=”#89CEA4″ global_colors_info=”{}”][et_pb_row column_structure=”1_4,3_4″ use_custom_gutter=”on” gutter_width=”1″ make_equal=”on” admin_label=”Module Heading with Icon” _builder_version=”4.16″ custom_padding=”||1px|||” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ custom_padding=”|||” custom_css_main_element=”width: 10%!important;” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”http:\/\/www.horizon-research.com\/tl4cs\/wp-content\/uploads\/2022\/02\/Asset-26.png” title_text=”Asset 26″ show_bottom_space=”off” align=”center” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ _module_preset=”default” header_font=”EB Garamond||||||||” header_text_align=”left” header_text_color=”#454545″ header_font_size=”42px” header_line_height=”2.2em” header_3_font=”|700|||||||” header_3_text_color=”#454545″ header_3_font_size=”26px” header_line_height_tablet=”1.2em” header_line_height_phone=”1.2em” header_line_height_last_edited=”on|tablet” global_colors_info=”{}”]<\/p>\n

Making Sense of Trends in Weather Data<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Why is it important to make sense of trends in weather data?<\/b>\u00a0<\/span><\/p>\n

The Next Generation Science Standards (NGSS Lead States, 2013) highlight crosscutting concepts (CCC) that identify connections across areas of science including patterns, cause and effect, and systems. As students begin to examine more data (e.g., more months, more regions, more seasons), they may start noticing trends in the data. Scientists use these trends to make predictions about water levels, amounts of rainfall\/snowfall, drought conditions, and more.\u00a0<\/span><\/p>\n