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! 

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. 

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:

  1. Practice and gain exposure to the language of data, statistics, and graphing.
  2. Construct arguments and support their reasoning with evidence from the data and their own visuals.
  3. 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.
  4. Describe in words the various aspects of the data, including:
    1. Any patterns or interesting phenomenon they discover.
    2. 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.