Introducing the Second Tuva Science Collection — Atmosphere

“Have lights when other men are blind

As pigs are said to see the wind”

Aside from Ralph Waldo Emerson’s claim (paraphrasing William Butler), of all Earth systems, the atmosphere can be one of the hardest for (non- pig) students to perceive.

Although we breathe in atmosphere every minute, its chemistry, layers, air masses, and patterns of movement occur at large scales and are often invisible.

We can feel the wind, but how can we discern its large-scale geographical and temporal patterns? Through data, of course!

Following up from the release of our first Tuva Science Collection on Earth in Space,  today we are excited to introduce Tuva’s second science collection – Atmosphere.

Typhoon Halong Seen from the International Space Station
Typhoon Halong Seen from the International Space Station

The Atmosphere Collection includes datasets and activities that explore differences among vertical layers in the atmosphere, geographic variability in atmospheric pollutants, and temporal changes in components of the atmosphere such as the stratospheric ozone layer.

Some of the activities also explore weather and climate data to make sense of how air masses move.

Key ideas supported by datasets and activities in Tuva’s Atmosphere Collection include:

  • The size of the Antarctic ozone hole changes seasonally and is correlated with atmospheric concentrations of CFCs.
  • Atmospheric concentrations of CO2 and other pollutants have increased during recent decades, as has human population.
  • Relationships between different atmospheric parameters can be modeled and predicted mathematically.
  • Air masses can transport pollutants from source areas to distant non-source areas.  
  • Human actions to mitigate air pollution can improve air quality.

The activities support NGSS performance expectations, such as exploring evidence for how motions and interactions of air masses result in changes in weather conditions (MS-ESS2-5), or analyzing geoscience data to forecast of the rate of change in the ozone hole (HS-ESS3-5).

These activities also support a number of CCSS-Math standards such as modeling relationships with linear equations.

Don’t forget to give your students opportunity to explore data on their own to make their own discoveries. Help your students learn to “see the wind” — through data!

The Earth in Space and Atmosphere collections are available for Tuva Premium Subscribers.

Introducing the first Tuva Science Collection — Earth in Space

earth-rising

Here a star, and there a star,

Some lose their way.

Here a mist, and there a mist,

Afterwards — day!

Few have conveyed the beauty of dawn with more inspiration than Emily Dickinson. As teachers of Earth science, we aspire to convey to students appreciation and understanding of what it means to be a planet moving through space and how our position and movement in space predictably affects our everyday experience of, well — day!

The magnitude of spatial and temporal scale differences covered by the topic of “Earth in Space” makes it challenging to find datasets about stars, planets, Sun and Moon, orbits,  seasons, tides, and day-lengths that are of a scope that students can explore to find meaning and discover patterns and relationships.

At Tuva, we are gathering datasets into collections by topic to help you quickly find data and activities that support a unit you are teaching. Our first Tuva Science Collection is Earth in Space.

Tuva’s Earth in Space collection provides opportunities for students to analyze and interpret data and model systems (see NGSS ESS1.A and ESS1.B) to support understanding of key ideas such as:

  • Stars range in size, type, and distance from our Solar System.
  • Planets have different properties (density, gravitational pull, orbital period, temperature…)
  • Seasons occur at opposite times of year in northern and southern hemispheres.
  • The timing of tide cycles can be explained (and predicted) by the phase (and position) of the Moon.
  • How much day length changes through the year depends on latitude.

Each dataset has at least one activity, and you can add or adapt activities to fit your teaching goals.

Help your students discover and appreciate the music of the spheres — through data!

We will be adding many other science collections in coming weeks and months.  If there is a collection you are especially interested in, please let us know via Tuva Support or share with the community on Tuva Discussions.

Graph Choice Chart

This blog post is written by Molly Schauffler. Dr. Schauffler is an Assistant Research Professor at the University of Maine School of Earth and Climate Sciences.

So, your students have some data. Now what?

One teaching challenge is how to guide students to apply math skills when they “analyze and interpret” data in the context of learning content in other subjects – science, or history, or social science. 

Many educators see students stumbling to apply basic data literacy skills such as organizing data, graphing, fitting lines and thinking statistically in general, when working outside of math class with real (often “messy”) data about the real world.

In our work in the Maine Data Literacy Project over the last six years, we observed, over and over again, three glaring weak spots in students’ data literacy:

  1. Lack of a clearly-stated driving question or claim to investigate
  2. Overwhelming tendency to plot data in bar graphs as a default graph choice.
  3. Absence of discussion about variability in data.

We wondered: how could we help students frame clear, statistical questions to drive their inquiry? How could we help them make reasoned decisions about how to visualize data as evidence? How could we help them begin to see, describe, and make sense of variability in data?

And so, the Graph Choice Chart was born. The Graph Choice Chart (GCC) proposes five types of questions that students are likely to investigate:

  1. Questions about variability within a group
  2. Questions about comparing groups
  3. Questions about Correlations
  4. Questions about change through time (a special kind of correlation)
  5. Questions about how a group is proportioned into sub-groups.
image

                                       Screenshot of the Graph Choice Chart 

Students are prompted to clearly state (write out!) a complete question in one of these forms, and then to follow a decision tree for choosing – based on the nature of their question –  what kind graph would make sense to use for developing their evidence.  

In addition to helping students clarify the purpose of their analysis, the GCC maps a framework for building statistical thinking and language, informally at first.  

Students make deliberate decisions within the framework about what kind of question to ask, what kind of data are needed (categorical or quantitative), and what kind of graph makes sense for visualizing evidence. 

It prompts them to adopt language for visualizing and describing variability in data, an underpinning of reasoning about data. Once they master these skills, they are ready to move beyond the GCC to more complex kinds of questions and more quantitative analyses.

Tuva is a perfect environment for putting the Graph Choice Chart to work, whether students are collecting their own data, or working with Tuva Datasets

In future blogs, we’ll talk more about how early informal focus on variability supports the Common Core Math Standards and is a key underpinning to data literacy.

Download the Graph Choice Chart by logging into your Tuva Dashboard or the Tuva Resources section, and share it with your students and colleagues today. 

Introducing Data Literacy 101 course on Tuva

This blog post is written by Molly Schauffler. Dr. Schauffler is an Assistant Research Professor at the University of Maine School of Earth and Climate Sciences. 

Raise your hand if the idea of teaching your students data analysis and statistical thinking is a little unsettling for you.

For many of us who teach social studies, history, language arts, and (yes) even science, it has been a very long time since we took a course in statistics. For many, statistics is a scary label.

Even fewer of us have ever had training in how to incorporate statistical thinking into curriculum for young students. Yet assessments and standards expect our students to analyze and interpret data and make compelling arguments from evidence. Yikes.

The good news is that statistics educators and education researchers, and Tuva, promote an initial exploratory approach to learning how to think statistically about data. Research finds that students have lots to say about data once they have tools, opportunity, and guidance to explore data informally and reason about the stories they find.

Quantitative, or “Confirmatory” statistical analysis comes later, once students grasp challenges of making informal inferences about groups and attributes that are variable. 

In Exploratory Data Analysis, students first learn to recognize and talk about variability, and how variability and certainty are related. They learn to explore data, make informal claims, and develop language for describing their data.

If you feel hesitant about guiding students in data analysis, you are not alone. Many of you have asked for some kind of “orientation” for students starting out with data on Tuva. 

Today, we are excited to launch Data Literacy 101, a course with modules and lessons designed to establish fundamental exploratory data analysis skills (“Level I”).  

image

In the coming weeks, we will add lessons that scaffold students in more quantitative analyses (“Level II”), that then launch them into a grounded and integrated approach to analyzing data as evidence (“Level III”).

Data Literacy 101 lessons incorporate pre- and post-assessment questions to help identify gaps and gains in learning. Lessons are tagged to identify relevant data literacy standards. Follow-on activities will give students practice in skills just learned in a lesson using datasets tagged by content area. 

In addition to building students’ skills, Data Literacy 101 is a useful reference if you want to refresh your own familiarity with basic statistical concepts and tools and language.  

Data Literacy 101 can help you and your students experience the fun of playing with data and telling the stories they find. Let’s get started! 

More Updates to the Filter Bar

We have made additional updates to the Filter Bar on Tuva Datasets, following up from our announcement a few weeks ago regarding a more powerful Filter Bar. 

Now, you can filter for Tuva Datasets and activities by a specific Common Core Math Standard, Domain, or Topic.

The CCSS-related Filter categories include: Comparing Groups, Correlation, Comparing Data, Linear Equations, Modeling, Quantitative Relationships, and many topics and standards.

In addition, we are continuing to extend our coverage of the Next Generation Science Standards, and have curated a number of fantastic new datasets covering additional NGSS Physical Science, Life Science, and Earth & Space Science standards. To learn more, explore all the datasets in our Tuva Datasets Library

Remember, you can always reach out to us if you are unable to find a dataset for your needs.