This professional development program for high school teachers was developed by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers University as part of its Teacher Professional Development for Embedding Computational Thinking in Mathematics and Science High School Classes (PDCT) project.
This instructional program is led by DIMACS in partnership with the Neag School of Education at the University of Connecticut.
The course is developed around ten modules created by DIMACS to integrate computational thinking concepts with math, science and other disciplines. These modules are intended to be used by teachers in high school classrooms. Each module contains standalone material and provides roughly 4 to 6 days of classroom activities.
Following are four examples of the ten modules:
- Privacy introduces privacy issues that are created, worsened, or solved by computer technology and the collection of data. Through case studies based on well-known websites, students explore uses of data that are intrinsic to the value that users gain from a social network site.
- It’s an Electrifying Idea! explores whether it’s time to switch to an electric car. It uses simple spreadsheets to compare the cost of leasing an electric car to the purchase of a traditional gas-powered car.
- Heart Transplant explores how to decide who among a group of potential recipients should receive a heart when one becomes available.
- Weather Generators explores questions relating to patterns that can arise due to the statistical persistence of weather.
This project is conducted with support from the National Science Foundation to design and test materials that engage high school teachers from a variety of science, mathematics and other disciplines to bring computational thinking (CT) into their classrooms.
Mandatory Online Workshops
Teachers accepted into the program must attend a two-day, online workshop before they can begin the 4-8 week online instructional program. These workshops will introduce teachers to computational thinking, the online instructional platform (CANVAS), and to each other.
Here are the currently scheduled workshops and subsequent online courses:
July 13-14, 2020, Groton School, Groton, MA: Following a mandatory online workshop from 9-11 a.m. on July 13 and 14, the online course will follow immediately from July 15 – August 11, 2020. For more information, contact Jon Choate at Jchoate6678@gmail.com or Gary Benson at firstname.lastname@example.org. Download Groton program flyer.
July 7-8, 2020, Utah State University, Logan, Utah: Following a mandatory online workshop from 9-11 a.m. on July 7 and 8, the online course will follow immediately from July 10 – August 6, 2020. For more information, contact Carl Anderberg at email@example.com or Helen Bosch at firstname.lastname@example.org. Download Utah program flyer.
Other workshop locations will be announced as they become available.
If you wish to seek admission to one of the listed courses, please complete our online application form.
Courses already have been held in the following locations:
- Albany, NY, Nov.-Dec. 2019/Jan. 2020
- Helena, MT, Sept. 9-Nov. 1, 2019
- Oklahoma City, OK, July 15-Aug. 14, 2019
- Pittstown, PA, July 8-Aug. 4, 2019
- Nevada, MO, April 2019
This three-year project will occur in three stages:
- Design and development of instructional materials online instruction;
- Delivery of these materials to teachers around the country through in-person workshops followed by the online course; and
- Research to test how well teachers were able to learn the material and incorporate it into their classroom instruction.
The research conducted in the third state will contribute to the general understanding of professional development in computational thinking and its impact on teachers and their students. It will also illuminate how existing professional-development design principles and theories of teacher learning can be applied to help support teachers integrate computational thinking into their classroom instruction.
This project is being conducted with support from the National Science Foundation under grant DRL-1812982.