Crosscutting Concepts Disciplinary Core Ideas Earth & Space Science Is Lesson Plan NGSS Phenomena Science and Engineering Practices Three-Dimensional Learning Elementary Grade 5
Teachers and families across the country are facing a new reality of providing opportunities for students to do science through distance and home learning. The Daily Do is one of the ways NSTA is supporting teachers and families with this endeavor. Each weekday, NSTA will share a sensemaking task teachers and families can use to engage their students in authentic, relevant science learning. We encourage families to make time for family science learning (science is a social process!) and are dedicated to helping students and their families find balance between learning science and the day-to-day responsibilities they have to stay healthy and safe.
Interested in learning about other ways NSTA is supporting teachers and families? Visit the NSTA homepage.
Sensemaking is actively trying to figure out how the world works (science) or how to design solutions to problems (engineering). Students do science and engineering through the science and engineering practices. Engaging in these practices necessitates that students be part of a learning community to be able to share ideas, evaluate competing ideas, give and receive critique, and reach consensus. Whether this community of learners is made up of classmates or family members, students and adults build and refine science and engineering knowledge together.
In today's task, How can we use data to predict the length of a shadow?, students engage in science and engineering practices and use the thinking tools of patterns and cause-and-effect to make sense of the science idea that orbit of the Earth around the Sun and the rotation of Earth on its axis causes changes in the length and direction of shadows.
This Daily Do was inspired by the book Florence The Data Scientist and Her Magical Bookmobile, written by Ryan Kelly and illustrated by Mernie Gallagher-Cole. Check out Reflect on Making Predictions with Data (at the end of this lesson) to learn how to use this story to extend student learning about making predictions using patterns in data and to introduce students to the field of data science.
View the How can we use data to predict the length of a shadow? NGSS table to see the elements of the three dimensions targeted in this lesson.
You might say to your students, "I found this interesting picture of a young person and their shadow. What are some things you notice or wonder about this picture?" Give students a minute or two to individually record observation/questions (Alone Zone), then ask them to share their noticings and wonderings with a partner. Bring the class back together and ask students to share their own noticing or a partner's noticing with the whole group. Students may say (with suggested teacher follow-up questions) the following:
Say to students, "Many of us noticed the shadow is really long - much longer (taller) than the young person. This makes us wonder when our own shadows are long and if the length of our shadows change (daily/weekly/season to season)."
Share the Me and My Shadow formative assessment probe with your students. Ask students to choose the person they most agree with and record their thinking using words, pictures, and/or symbols. Next, ask students to share their thinking with a partner. If you are completing this Daily Do in a distance-learning scenario, consider sharing this Google Docs version of the Me and My Shadow probe with students; digital tools such as Google Slides, Jamboard or Padlet can be used to provide students an opportunity to share ideas with a partner or small group.
Bring the class back together and invite students to share their ideas with the whole class. Consider using these talk moves to support equitable participation and help deepen student thinking about shadows:
Students will likely continue to disagree about which friend they most agree with. This is OK. Welcome all students' ideas and experiences with shadows.
Ask students, "How might we investigate which friend—Jamal, Morrie, Amy, Fabian, or Penelope—best explains how shadow length changes during the day?" Give students time to think independently and record their ideas. Then ask them to share their ideas in a small group. As you move around the room, you might ask student groups some or all of the following questions:
Consider (informally) ranking the groups' investigation plans from least detailed to most detailed to determine the order in which to call on groups when you bring the class back together. This will allow for all groups to genuinely contribute to the class investigation plan. (Do not share rankings or strategy with students).
Bring students back together. Invite groups to share their investigation plan. You might choose to call on the group with the least detailed plan to share first, as described above. Record the "big ideas" in a location easily viewable by all the students in the room (whiteboard, poster, or shared digital space); label these ideas Class Investigation Plan. As each group shares in turn, add to and/or revise the class plan. Consider asking students if they agree (thumbs up) or disagree (thumbs down) with additions/changes to the plan after each group's turn. Investigation plans will vary from class to class, but all plans should include these elements:
You might collaboratively create a data table template, then ask students to draw the table in their science notebooks, or provide each student with a blank data table.
Before bringing students outside, you might consider the following adaptations:
Take students outdoors to collect data. Make sure to follow your school's safety guidelines. Additionally, make sure to continually remind students not to look directly at the sun.
While moving from group to group, help students connect their current thinking with the class' initial observations of the young person's long shadow (in the photograph). You might ask these questions:
Encourage students to record observations of their shadows (in addition to length) and the location of the sun compared to the direction/location (and length) of their shadow.
After the investigation is completed, ask students to independently record any patterns they observe in their data. Move students into small groups to share these patterns and identify similarities and differences among the patterns shared by their group members. As you move around the room, encourage students to look for patterns in all of their data (qualitative), not just shadow length measurements (quantitative). Consider using one or more of the following prompts:
As ________ changed, ________ changed, too.
As ________ [describe change here*], ________ [describe change here].
*longer/shorter, higher/lower, moved east/moved west, etc.
Bring the students back together. Ask groups to share the patterns they identified; record the groups' shares. Students will likely identify the following patterns (with suggested teacher follow-up responses):
Acknowledge with students, "Wow! That is a lot of patterns! And some of the patterns we noticed about shadows have other patterns in them! (For example, the sun moving from low to high to low in the sky during the day.)"
Refer again to the formative assessment probe Me and My Shadow. Remind students, "We planned our investigation to find out which friend best explains how shadow length changes during the day." Read each person's response (Jamal, Morrie, Amy, Fabian, and Penelope), then say, "When I count to three, say out loud the name of the person you most agree with." Count to three. Students will mostly likely agree that Fabian has the best description.
Note: Some students may say "Morrie" has the best description. If so, ask students to return to their small groups and use evidence from their data to make a claim about whose response, Morrie's or Fabian's, best explains how shadow length changes throughout the day. Bring students back together and poll the class again.
Next, ask students, "How could we use our data to predict how long our shadows would be at [insert time halfway between two measurements]?" Ask students to turn to a partner and share their ideas. Then take three sharings from the class. Students will likely say the length will be halfway between two measurements. Next, ask students, "What if I wanted to predict how long our shadows would be at [insert time before first measurement] or [insert time after last measurement]? Could we still use our data to make a prediction?" Accept all ideas.
Say to students, "We represented our shadow-length data in a table. What is another way we might represent our data?" Students will likely say graphs (they may give examples of different types of graphs, like pie charts, bar graphs, and line graphs). Say to students, "Let's try representing our data on a graph and see if that helps us answer our question about using the data we collected to predict shadow lengths before [insert time of first measurement] and after [insert time of last measurement]."
You might consider these different options for constructing graphs with your students:
Paper and pencil. If you don't want to invest a lot of instructional time but want to give students an opportunity to develop their graphing skills, consider providing them a graph template with numbered x- and y-axes.
Google Sheets. With assistance, upper-elementary students can use Google Sheets to create graphs. Google Sheets recognizes "clock" time when times are denoted AM and PM. Consider sharing the shadow length Google Sheets template with students; each student will need to make their own copy before entering their data. Watch Graph Shadow Lengths in Google Sheets to learn how to use this program with students.
Independent of the graphing method you choose to use, the x-axis minimum value should be sunrise and the maximum value, sunset. Ask students why they think this is the case.
Provide students time to graph their data (whichever method you choose).
Project your own graph or a student's graph in a place easily viewable by all students. Ask students, "How might we use our graphs to predict our shadow lengths at [time in between data points on graph]?" Ask students to share their ideas with a partner. Bring the students back together and ask for a volunteer to show on the projected-image how they would use the graph to predict shadow length at the given time. Quickly survey the students whether they agree (thumbs up) or disagree (thumbs down) with the student's idea. Invite one of the students who disagree to show their thinking using the projection. Continue in this manner until students all agree that shadow length can be predicted by (1) locating the time of interest on the x axis, (2) moving their finger vertically until it intercepts the line, then (3) tracing their finger horizontally until it meets the y-axis. This location on the y-axis is the predicted shadow length.
Return to the question that created the need (from students' perspective) to represent shadow length data in a graph. Ask students, "Now that we have represented our data on graphs, can we use our data to predict how long our shadows would be [insert time before first measurement] and [insert time after last measurement]? Give students time to turn and share their thinking with a partner. Bring the class back together. Ask for a student to volunteer to show on the projected-image how they would use the graph to predict shadow length at the given time (choose a time before the first or after the last measurement). The volunteer will most likely use the same method the class previously agreed on.
Ask students, "How might we test our prediction?" Students will suggest going outside at that time and measuring the length of their shadow. Next, ask students, "Would testing our prediction one time make you confident we can use our graphs to accurately predict shadow lengths at different times of the day? Why do you say so?" Students will likely say they would not be confident, although their reasons why may vary. Collaboratively arrive at the number of predictions to make, then the following day, go outside and test their predictions.
Students' excitement will likely grow throughout the day as they gain confidence in their ability to accurately predict the length of their shadow at different times of the day using their graphs.
Say to students, "Remember our question is this: Can we use the data we collected to predict shadow lengths before [insert time of first measurement] and after [insert time of last measurement]? Let’s stay focused on this question and see what we think we have figured out." Facilitate a building understanding discussion with students. Consider using the following prompts:
Students should conclude that their data, when represented in graphs, can be used to predict shadow lengths between data points (interpolate) and predict shadow lengths before our first measurement and after our last measurement (extrapolate).
You might ask students, "Do you think our shadow lengths change from season to season? Do you think the pattern changes (our shadows getting shorter until noon, then getting longer again) from season to season?" Accept all answers. You might say, "Let's investigate!" Tell students you will save the class investigation plan and the patterns they identified until the next season.
Reflect with students on what they've accomplished. They identified patterns in shadow length measurements collected at different times during the day to make predictions about the lengths of shadows between data points (interpolate) and beyond data points (extrapolate). They noticed other patterns as well: (1) The sun rises higher in the sky until solar noon, then begins to descend; and (2) the sun and their shadow are always on "opposite sides" of the student in the sun-student-shadow system. Share with students they have just experienced thinking, acting, and talking like data scientists.
A data scientist is someone who collects data, analyzes it, and makes decisions using large amounts of data, or information.
You might ask students, "Who might need knowledge of these patterns we identified [point to the class list of patterns] to make predictions and/or solve problems?" Give students independent thinking time, then move them into small groups to share their ideas. Bring students back together and ask students to share their idea or an idea shared by a group member. Students' own school, family, or community experience might include the following:
Say to students, "Let's put our data science knowledge and skills to the test in a different way." Introduce students to the book Florence The Data Scientist and Her Magical Bookmobile. Share with students that this story is about two data scientists: Florence, the owner of the magical bookmobile, and Beatrice, a young girl who wants the book Florence chooses for her to produce a dragon (it is a magical bookmobile, after all). Florence chooses books for children based on their answers to three questions. If Beatrice can identify which answer to give for each of the three questions using the answers other children in the neighborhood gave who received a "dragon" book (data), she, too, will have her very own dragon.
Consider challenging your students to gather and organize data from the story in a way that will enable them to identify the pattern that produces dragons. If students struggle to organize the data, consider giving them a copy of the companion student activity sheet. Did everyone who identified the dragon book pattern organize their data in the same way?
You might invite students to research what data scientists do. Can they find things in common between their own work identifying patterns in shadows and children's answers in Florence The Data Scientist and Her Magical Bookmobile and the work of data scientists?
Data science is one of the fastest-growing, in-demand professions today. Domino Data Lab is the Enterprise MLOps platform that more than 20% of the Fortune 100 chooses to unleash data science teams and the IT organizations that support them. Model-driven companies including Allstate, Dell Technologies, and Bayer use Domino to accelerate model velocity for breakthrough research, increase collaboration, and rapidly deliver high-impact models. Domino is backed by Sequoia Capital, Coatue, Bloomberg Beta, Dell Technologies Capital, Highland Capital Partners, and Zetta Venture Partners.
NSTA has created a How can we use data to predict the length of a shadow? collection of resources to support teachers and families using this task. If you're an NSTA member, you can add this collection to your library by clicking +Add to Library, located near the top of the page.
The NSTA Daily Do is an open educational resource (OER) and can be used by educators and families providing students distance and home science learning. Access the entire collection of NSTA Daily Dos.
Web SeminarScience Update: The Science of Oil Spill Response and Cleanup, September 28, 2023
Join us on Thursday, September 28, 2023, from 7:00 PM to 8:00 PM ET, for an edition of NSTA’s Science Update. Major oil spills are rare, b...