DataLiteracy

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:

Lack of a clearly-stated driving question or claim to investigate
Overwhelming tendency to plot data in bar graphs as a default graph choice.
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:

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

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”).

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!

Coming Soon – Automatic Grading on Tuva!

During the initial design phase of Tuva Activities and Lessons, one critical decision we made was that activities on Tuva would only initially support one item type – open-ended questions.

Why? We felt that activities and lessons based on authentic datasets gave both educators and learners a unique opportunity to look beyond the straightforward multiple-choice and True/False questions.

With actual data and tools at our fingertips, educators can pose a number of interesting, open-ended questions that gave learners an opportunity to:

Practice and gain exposure to the language of data, statistics, and graphing.
Construct arguments and support their reasoning with evidence from the data and their own visuals.
Explain their thinking in words, giving them an opportunity to focus on their grammar, sentence construction, as well as learn the techniques of quantitative reasoning.
Describe in words the various aspects of the data, including:

Any patterns or interesting phenomenon they discover.
Distribution of various attributes, such as its shape, its spread, or its center

Image: New Item Type on Tuva

Over the last twelve months, we have received a variety of feedback from educators across math, science, statistics, and many other subject areas about being able to ask (and immediately grade) straightforward, multiple-choice questions at the beginning of the activity to assess for basic understanding, and then gradually build towards the more difficult, open-ended questions.

I am excited to announce that we will soon be adding these two new features – Multiple Choice Questions and Automatic Grading – to Tuva Activities and Lessons.

For Tuva Basic teachers, you will be able preview these new features via a number of different activities on the 25 Free Tuva Datasets.

For Tuva Premium subscribers, you will see this feature gradually introduced across all of our 400+ activities and lessons around 315+ datasets, as well as many of the new activities and lessons we release moving forward.

Introducing An Easier Way to Filter & Find Datasets on Tuva!

There are 300+ Tuva Datasets in our library covering topics such as the Climate Change, Land & Sea Animals, Presidents of the United States, and many many more.

Today, we are excited to do an initial launch of a more powerful Filter Bar to meet the diverse needs of our educators within the Tuva community.

A More Powerful Filter Bar:

Now, if you wanted to find all the Science or Environment-related Tuva Datasets that are small in size, you can find them very quickly by choosing Science & Environment in the Subject drop-down and 1-40 in the Size drop-down, like this:

As you can see above, you can now filter and find Tuva Datasets based on a number of additional parameters beyond Subject & Grade Level such as:

The Size of the dataset (How many data points are there?)
The NGSS standard (particularly relevant for all our US Science Educators)
The dataset Language (For our non-English speaking educators and learners)

Or, if you wanted to find a dataset that is related to the MS-ESS3-5 (Earth & Human Activity) NGSS Standard, you can find it very quickly by choosing MS-ESS3-5 in the NGSS drop-down, like this:

Send Us Your Favorite Datasets & Win

Do you have a favorite dataset that you have used in your previous lessons or units? Does it come from an authentic source? Is it licensed under Creative Commons? Are you able to link to it?

Send us your favorite datasets to hello@tuvalabs.com over the next two weeks and enter a chance to win a Tuva T-shirt, a Tuva Coffee Mug, and other goodies!

What does NGSS look like in the classroom?

By Stephen Farnum – Middle School Science Teacher, Greenwich Public Schools & Tuva K-12 STEM Content Specialist

When I speak with fellow educators about Next Generation Science Standards, they usually tell me they understand “what” NGSS is, but have concerns about “how?”.

How can I help my students meet these expectations? How does my instruction need to change? How am I going to find resources to help?

Tuva is on a quest to help science educators implement NGSS in their classrooms through our growing library of authentic datasets, interactive graphing tools, and ready-to-use activities and lessons.

One aspect of this is to make it easier for teachers to create their own high-quality, NGSS-aligned lessons.

In support of this, I recently collaborated with them to create Characteristics of an Effective Data-Driven Science Lesson, a checklist for teachers to use while creating or improving data-driven lessons which combine science, math, and problem-solving.

I combined input from the NGSS Science and Engineering Practices as well as what I’ve learned from my students as they have developed their understanding of science and math through data analysis.

Once we completed the checklist, we realized that many teachers would like to see these characteristics of a data-driven science lesson in action.

Exemplar Science Lesson on Tuva

I created a lesson titled “How to Mitigate Hurricane Damage” to show one way of applying these characteristics to create an NGSS-aligned learning activity on Tuva.

I began with NGSS Middle School DCI:

“Analyze and interpret data on natural hazards to forecast future catastrophic events and inform the development of technologies to mitigate their effects” (MS-ESS3-2).

I searched the Tuva Datasets library to find a dataset titled – Hurricane Sandy, Her Brother and Sisters – that was relevant to the DCI. The source of the dataset is NOAA’s National Climate Data Center, and it has 654 Data Points (or cases) and 7 Attributes.

I used Tuva’s graphing tools to explore relationships between different attributes. Noticing correlations between hurricane latitude, frequency, and severity, I designed a task that would guide students to investigate these relationships:

“Create an evidence-based proposal for where a new hurricane mitigation structure should be placed”

You can checkout the finished activity here, and feel free to use it in your classroom during your next Earth Science activity!

Tuva NGSS Resources

The ability to easily find and create open-ended and student-centered learning activities shows Tuva’s potential as a tool for aligning K-12 STEM curricula with NGSS.

If NGSS leaves you wondering “how?”, Tuva’s NGSS resources are a great place to start.