Find Resources

ModelBEST Module 2 - Catching Butterflies

The 2nd module from Model BEST. Intended for 4th and 5th grade.

Application of the Occupational Analysis of Computational Thinking-Enabled STEM Professionals as a Program Assessment Tool (2012)

Journal of Computational Science Education (2012)

The Greenhouse Effect

A short video on the basics of the greenhouse effect, used in CS in Science, Module 2 (Greenhouse gas model).

Beaker with silver nitrate solution and copper pipe

Silver Nitrate Production video

A short video showing "Silver Production from Silver Nitrate using a Copper Pipe" - used if wet lab activity not available for CS in Science Module 4 (Chemical Reactions)

Pair Programming video

This short video (2:51 minutes) features two middle school girls describing and practicing pair programming. It is a great introduction to this method of learning programming and creating models.

Massachusetts Institute of Technology video - image of birds flocking

Complex Adaptive Systems Modeling Video

This short video (3:13 minutes) introduces complex adaptive systems and agent-based modeling concepts to middle-school students. It shows beautiful footage of birds flocking as an example of a complex adaptive system.

Skill Building Deck

A slide deck of exercises to build CS and decoding skills

Computational Thinking for Youth in Action (2011)

This is an early paper of Computational Thinking that has a lot of traction. It's where we first describe the Use-Modify-Create trajectory.

Assessing Youth's Computational Thinking (2011)

AERA conference proceedings 2011

Integrating Computational Thinking Across the K-8 Curriculum (2014)

ACM InRoads (2014)

Experimental Design for Virtual Instruction

A Google Sheet to use for conducting the Experimental Design activity in a remote learning classroom.c

Reclaiming the roots of CT (2016)

CSTA Voice 2016

Developing a framework for computational thinking from a disciplinary perspective (2018)

Proceedings of the International Conference on Computational Thinking
Education 2018. Hong Kong: The Education University of Hong Kong.

Making Sense of Models one page description

This is a document that describes the Making Sense of Models project.

Module 3 Lesson 3 - Adding a Predator

In this lesson, students will modify the Rabbits and Grass model by adding a predator, a Mountain Lion, to answer a new question: “Does adding a top predator increase or decrease the stability of an ecosystem?” In the second activity, students will design and run experiments to see if adding a predator has an impact on the ecosystem. This activity will reinforce the concepts of energy flow through ecosystems and the often unexpected results of interactions in complex adaptive systems.

Module 3 Lesson 4 - Create Your Own Ecosystem Model

In this lesson, students will design their own ecosystems projects consisting of a question, experimental design and model. In the first activity, students will learn about the computational science cycle and use it to scope their project. This leads to a second activity where they start designing and implementing their model.

Module 3 Lesson 5 - Designing and Running an Experiment and Sharing Your Findings

In this lesson, students will complete their ecosystems models and then design and run experiments using their models as experimental test beds. As an extension, students can prepare a presentation on their model, experimental design and findings.

Locating the Epicenter of an Earthquake Lesson

Identify the location of an earthquake epicenter using a travel time graph and three seismograph tracings. The epicenter is the point on Earth's surface directly above an earthquake. Seismic stations detect earthquakes by the tracings made on seismographs. Tracings made at three separate seismic stations are needed to locate an earthquake epicenter.

ModelBEST Module 1 - Introduction to Modeling and Simulation

The 1st module from Model BEST. Intended for 4th and 5th grade.

Emergence video

A portion of a PBS NOVA video discussing the concept of emergence (or complex adaptive systems) where patterns emerge in nature where agents follow simple rules.

Search for "[keyword]" across the whole site.