While building and testing model rockets fueled by antacid tablets, students are introduced to the basic physics concepts on how rockets work. Students revise and improve their initial designs. Note: This activity is similar to the elementary-level film canister rockets activity, but adapted for middle school students.

The "Into the Book" web site is designed to help elementary students practice eight reading comprehension strategies through playful interactive activities. The site focuses on eight research-based strategies: Using Prior Knowledge, Making Connections, Questioning, Visualizing, Inferring, Summarizing, Evaluating and Synthesizing. "Behind the Lesson," the teacher area of the site, provides information, lesson plans and other resources for teachers.

Acting as if they are biomedical engineers, students design and print 3D prototypes of pressure sensors that measure the pressure of the eyes of people diagnosed with glaucoma. After completing the tasks within the associated lesson, students conduct research on pressure gauges, apply their understanding of radio-frequency identification (RFID) technology and its components, iterate their designs to make improvements, and use 3D software to design and print 3D prototypes. After successful 3D printing, teams present their models to their peers. If a 3D printer is not available, use alternate fabrication materials such as modeling clay, or end the activity once the designs are complete.

Spreadsheets Across the Curriculum module. Students build spreadsheets to model the growth in population of a species of birds introduced to an isolated island in New Zealand.

This activity is an introductory activity where students observe the characteristics of ionic compounds and crystals They also work on making observations and writing hypotheses.

In this just-in-time activity, students submit responses before class to demonstrate their ability to use concepts from a textbook reading on money. In-class follow-up activities are recommended.

An elementary introduction to chemistry vocabulary, tools and concepts used in science curriculum.

Data Carpentry lesson to learn how to work with Amazon AWS cloud computing and how to transfer data between your local computer and cloud resources. The cloud is a fancy name for the huge network of computers that host your favorite websites, stream movies, and shop online, but you can also harness all of that computing power for running analyses that would take days, weeks or even years on your local computer. In this lesson, you’ll learn about renting cloud services that fit your analytic needs, and how to interact with one of those services (AWS) via the command line.

This activity is a guided demonstration where students create light up quiz boards to demonstrate electricity vocabulary.

This PowerPoint presentation was delivered by Anthony Tisler from the Michigan Academy for Green Mobility Alliance (MAGMA) at the Utica Community Schools' Professional Development Day for Teachers in the Industrial Arts/Engineering Pathway, held at the Instructional Resource Center in Sterling Heights, MI on April 15, 2014. The presentation provides information on electric vehicle architecture and components. It is a great tool for introducing students at the high school or college level to various battery electric vehicle (BEV) and hybrid electric vehicle (HEV) components, configurations, and operation.

Data Carpentry lesson to understand data structures and common storage and transfer formats for spatial data. The goal of this lesson is to provide an introduction to core geospatial data concepts. It is intended for learners who have no prior experience working with geospatial data, and as a pre-requisite for the R for Raster and Vector Data lesson . This lesson can be taught in approximately 75 minutes and covers the following topics: Introduction to raster and vector data format and attributes Examples of data types commonly stored in raster vs vector format Introduction to categorical vs continuous raster data and multi-layer rasters Introduction to the file types and R packages used in the remainder of this workshop Introduction to coordinate reference systems and the PROJ4 format Overview of commonly used programs and applications for working with geospatial data The Introduction to R for Geospatial Data lesson provides an introduction to the R programming language while the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data. The R for Raster and Vector Data lesson assumes that learners are already familiar with both geospatial data concepts and the core concepts of the R language.

Data Carpentry lesson to open, work with, and plot vector and raster-format spatial data in R. The episodes in this lesson cover how to open, work with, and plot vector and raster-format spatial data in R. Additional topics include working with spatial metadata (extent and coordinate reference systems), reprojecting spatial data, and working with raster time series data.

Students are are introduced to Excel and its capabilities by building some simple spreadsheets.

Students continue their introduction to Excel by building spreadsheets that estimate the risk of a major earthquake along the Cascadia Subduction Zone to Benton County, Oregon.

In this inquiry investigation, students will observe unknown tissue samples and draw them in their science notebooks. They will work in groups to present and defend a classification scheme for the unknown tissues to the class, and then use textbooks as a reference to compare their scheme to the accepted classification system.

This task lets students explore the differences between linear and non-linear functions. By contrasting the two, it reinforces properties of linear functions. The task lends itself to an extended discussion comparing the differences that students have found and relating them back to the equation and the graph of the two functions.

This is a recording of a 45 minute introductory webinar on preprints. With our guest speaker Philip Cohen, we’ll cover what preprints/postprints are, the benefits of preprints, and address some common concerns researcher may have. We’ll show how to determine whether you can post preprints/postprints, and also demonstrate how to use OSF preprints (https://osf.io/preprints/) to share preprints. The OSF is the flagship product of the Center for Open Science, a non-profit technology start-up dedicated to improving the alignment between scientific values and scientific practices. Learn more at cos.io and osf.io, or email contact@cos.io.

The microscopic world is full of phenomena very different from what we see in everyday life. Some of those phenomena can only be explained using quantum mechanics. This activity introduces basic quantum mechanics concepts about electrons that are essential to understanding modern and future technology, especially nanotechnology. Start by exploring probability distribution, then discover the behavior of electrons with a series of simulations.

In this webinar, Doctors David Mellor (Center for Open Science) and Stavroula Kousta (Nature Human Behavior) discuss the Registered Reports publishing workflow and the benefits it may bring to funders of research. Dr. Mellor details the workflow and what it is intended to do, and Dr. Kousta discusses the lessons learned at Nature Human Behavior from their efforts to implement Registered Reports as a journal.

The goal of this lesson is to provide an introduction to R for learners working with geospatial data. It is intended as a pre-requisite for the R for Raster and Vector Data lesson for learners who have no prior experience using R. This lesson can be taught in approximately 4 hours and covers the following topics: Working with R in the RStudio GUI Project management and file organization Importing data into R Introduction to R’s core data types and data structures Manipulation of data frames (tabular data) in R Introduction to visualization Writing data to a file The the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data.