Letting Students Lead: A Tenn. Science Teacher Navigates Curriculum Change

Middle school science in Collierville, Tennessee, looks a lot different this year.

Less time is spent taking notes, reading, and answering question sets, and more time is spent developing models, conducting investigations, and discussing findings. The change comes from a new 5E-based curriculum, in which students construct their own scientific explanations.

By law, the Tennessee Board of Education is required to review core academic standards at least once every eight years. In late 2022, the board approved a revised edition of the Tennessee Academic Standards for Science that was developed to explicitly “integrate disciplinary core ideas with crosscutting concepts and science and engineering practices.” In other words, the standards are intended to help students not just memorize scientific facts but actively use evidence and reasoning to make sense of the world around them.

A list of state-approved curricula aligned with the revised standards was released in 2024, and districts began implementation at the start of this school year.

Navigating the Change
Collierville Teacher Leslie Austin

Collierville Schools, located in a suburb of Memphis, opted to use STEMscopes™ Science TN as the curriculum for their 68 science students. Leslie Austin, a 7th-grade teacher at West Collierville Middle School, is a fan of the 5E instructional approach used by the curriculum. She admits, however, that they were worried at first.

“Turning over the reins to the kids is a shift for a lot of people, and it can be really hard,” said Austin. “But it is really cool to see, if we give them the tools and access to the information, what they can discover.”

STEMscopes™ is one of six state-approved middle school science curricula. Each requires a similar shift: from consuming knowledge to actively building it.

Actively Making Sense of the World 

A central feature of Austin’s classroom is a whiteboard coated with sticky notes. On each note is a student-generated question that links back to a real-world situation students are attempting to explain. 

Austin’s driving question boards from semester 1 (left) and semester 2 (right). Austin has noticed an increase in both the number of questions and in “piggybacking”, or adding to each other’s questions.

As the unit progresses, students plan and conduct their own investigations and analyze others’ research to answer their own questions and, ultimately, construct an explanation of the phenomenon. Austin loves the “a-ha” moments when students make a discovery that connects to the question board.

“The kids get to answer those questions for their classmates. They start getting excited and wanting to know more. They are going to remember that more than me reading them something,” said Austin.

Data’s Role in Sensemaking

Data plays a crucial role in the student sensemaking process. Austin encourages her students to make claims using data from investigations they have conducted themselves or from a reliable study, but doesn’t permit them to just look up an explanation on their cell phone. 

“AI can make up anything, but what backs it up? What makes it true? How do you know this is the boiling point? How do you know the cell works this way? I want them to figure out the answers on their own.”

STEMscopes™, also recognizing data’s importance, bundled Tuva into their curriculum subscriptions for many schools. Tuva has since created a Tuva-STEMscopes™ Science TN Alignment Guide to make it easier for teachers to locate places where Tuva’s data-rich resources and graphing tools can be used to enhance the curriculum.

Tuva: Dual Learning Gains

“Where has this been all my life?”

That’s how Austin described her reaction after using “Boiling Water Wherever You Are“, an activity she found on the Tuva‑STEMscopes™ Science TN Alignment. The activity helped students grasp abstract concepts about how pressure affects boiling point using real-world data, while also embedding vocabulary and building transferable data literacy skills.

Students practiced identifying positive and negative relationships, experimenting with which variables to place on which axis, and choosing the best type of graph to represent their data. Visual learners could see patterns emerge, and kinesthetic learners engaged directly by manipulating the graphs and testing ideas themselves.

Students can choose from multiple graph types in Tuva, allowing them to experiment with variables, axes, and visual patterns.

For Austin, the most exciting part was seeing students take ownership of the data. 

“It was something they had never done before—being able to choose what to put on the axes, change the colors, and see what patterns emerged. They could play around and see what they saw,” she said. 

The Payoff

In Austin’s opinion, the new statewide approach does a much better job of engaging students and preparing them to ask questions, work with data, and defend their ideas.

Though the shift places new demands on teachers, Austin has noticed some advantages for them as well.

“It’s a lot of fun watching teachers like their jobs better.” 

Teaching in Tennessee?

We’ve mapped Tuva resources to the Tennessee Academic Standards for Science. Search by grade level and standard here.

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.

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.