Engineers Code: Re-usable, Open Educational Modules for Engineering Undergraduates Lorena A. Barba, George Washington University, USA, labarba@gwu.edu Natalia C. Clementi, George Washington University, USA, ncclementi@gwu.edu JupyterCon, 22–24 August 2018, New York Open—Inspired by open-source culture Think of the impact of open-source development: people collaborate on large, complex projects, creating value together. What is the inﬂuence of this model in Open Education? Teaching and learning resources are made public, under a license permitting reuse, remix, rework, redistributing. We aim to collaborate with like-minded instructors, to peer-review materials, join in shared online platforms, and bring our students together. Motivation—Using computing to learn Modular—Stackable self-contained modules We can transform science and engineering education by making computational thinking infrastructural: embedded, ubiquitous, and contextual. To accomplish this, we need open instructional materials designed to be reusable, and community efforts to share good practices for using them. Our foundation modules are ready to adopt: 1. Get data off the ground: Learn to interact with Python and handle data. 2. Take off with stats: Hands-on data analysis using a computational approach and real-life applications. 3. Fly at changing systems: Tackle the dynamics of change with computational thinking. Coming soon! 4. Land in vector spaces: A graphical introduction to linear algebra. All materials are under BSD-3 clause and CC-BY licenses. Digital —Using the latest technology in digital pedagogy Find it: https://openedx.seas.gwu.edu Course lessons are Jupyter-ﬁrst—created as Jupyter notebooks, displayed in Open edX via the new jupyter-viewer-xblock. Assessments are auto-graded via the jupyter-edx-grader-xblock, nbgrader and Docker, providing instant feedback to the learner. Open edX is the only last-generation, full-featured, open-source platform for online learning. We deployed our site on Amazon AWS, with support from technical partners at IBL Education, NY. We take inspiration in the ideas of Seymour Papert about computational thinking. In particular, we want to design course materials adhering to Papert's Power Principle: What comes ﬁrst, using or understanding? The natural mode of learning is to ﬁrst use, leading slowly to understanding. New ideas are a source of power to do something! Our approach follows research-based design directions for developing computational thinking skills, embedded in science contexts. These skills are: 1. Data practices 2. Modeling and simulation practices 3. Computational problem-solving 4. Systems-thinking practices Ref. – Weintrop, David, et al., “Deﬁning computational thinking for mathematics and science classrooms,” J. Science Ed. and Tech., Vol. 25(1): 127–147 (2016). Design—Key principles 1. Idea of “computable content”—educational content made powerfully interactive, using Jupyter. 2. Idea of open pedagogy—reﬂecting in the teaching practice the ethos of open source. 3. Modularization—break-up the typical course format. 4. Harness the “worked-example effect”—empirically shown as better than problem-solving for novices. 5. Live-coding to structure active-learning in class. Credits—Images and thanks Lolly sticks from Wiki Education CC-BY Fuding by NSF award OCI–1730170 @LorenaABarba @ncclementi Please visit: http://lorenabarba.com This poster is licensed under CC-BY 4.0 https://github.com/engineersCode/EngComp Find it: https://github.com/ibleducation/jupyter-viewer-xblock https://github.com/ibleducation/jupyter-edx-grader-xblock From Module 3, lesson 4: “Bird’s eye view of mechanical vibrations”—top: system diagram; bottom: phase plot. From Module 2, lesson 4: “Life expectancy and wealth”—bubble plot of life expectancy vs. gross domestic product for 142 countries, with bubble size scaled by population.