This project is addressing a crisis throughout engineering education, and cyberinfrastructure education regarding decreasing enrollments, under preparation and disengagement of students, and the pressure of faculty to cover increasing volumes of material. Curricula have become stove-piped and disconnected, in spite of research indicating that science and engineering education best resonates with women and underrepresented minority students and when clear connections are drawn to transformative applications and other fields of study. Industry routinely lobbies for better engineering graduates that are at ease when collaborating on teams, and eager to attack hands-on design challenges.
The academic/industrial/professional society partnership between Rice University, Georgia Institute of Technology, Rose-Hulman Institute of Technology, the University of Texas at El Paso, National Instruments, Texas Instruments, Hewlett-Packard, and the Institute for Electrical and Electronics Engineers Signal Processing Society directly attacks these issues by aiming to revolutionize the way they teach and learn about cyberinfrastructure. They are guided by a common vision: to prepare the cyberinfrastructure leaders of tomorrow, to break away from the traditional textbook, lecture, homework-based approach to education, and to build a new framework where a vibrant network of educators, students, and field practitioners continually interact, collaborate, connect, and explore interactive content.
The innovative aspects and scientific merits of this collaborative project lie in their new approach to building and sustaining virtual educational communities around interactive content and applying the results to the full spectrum of engineering education venues: university undergraduate and graduate courses, industrial training and continuing education, just-in-time on the job learning, and high-school laboratories. Their research focuses on one strategic discipline in engineering, signal processing, and involves and balances education, community development, technology development, marketing and business planning, and impact assessment. The partnership is: 1. Implementing a light-weight Technology Framework that enables faculty and student users to exploit and expand upon the existing signal processing education content; 2. Building a signal processing Education Network of champions from faculty, students, and industry leaders nationwide that continually expands, improves, and diversifies the materials and that promotes the use of the framework both at network member institutions and at institutions in the wider engineering education community; 3. Assessing the effectiveness of the framework and network for accelerating adoption and use as well as the value of the mentoring and support provided by the network of champions; 4. Widely Disseminating the results and lessons learned.
Broader impacts of this research include the development of people-resources and technologies that will substantially increase the performance and capabilities of engineering educators, effectively opening up engineering education for motivated self-learners in all parts of the nation as well as the world. In particular, education in digital signal processing and related technologies is critical in sustaining many high-tech industries. Finally, digital signal processing educators, practitioners and students will be brought together to form dynamic knowledge sharing communities that greatly impact education not only on their home campuses but around the world.
Under the NSF Cyberinfrastructure Training, Education, Advancement and Mentorship (CI-TEAM) program, Rice University, Georgia Institute of Technology, Rose-Hulman Institute of Technology, and the University of Texas at El Paso worked together to form the Signal Processing Education Network (SPEN). SPEN was formed to advance signal processing education and create a community between members of the signal processing education network. The members defined three primary goals for SPEN: SPEN will serve as a forum to exchange best practices and tools for developing new signal processing education content; SPEN will develop an inventory and wish list for teaching a range of signal processing courses; SPEN will develop a globe-spanning network that will work to strengthen and unify signal processing education through interactive content. A clear challenge was presented: "In order for this project to be successful, SPEN must create a signal processing curriculum solution that is ready to use out of the box." The culmination of this work is implementable tools and strategies to achieve wider dissemination of Signal Processing educational content and strategies. OpenStax Tutor OpenStax Tutor (OST, https://openstaxtutor.org) is a study resource, homework, and test delivery system that uses powerful, advanced techniques to improve student learning and instructor understanding. OpenStax Tutor melds new cyber and social infrastructures to put powerful learning tools in the hands of educators and learners. OpenStax Tutor is an ambitious effort that combines high-quality, public ready software with advanced research pursuits. OST is currently in beta and some features are still being developed. OST provides signal processing educators with a comprehensive environment tailored to facilitate teaching technical content and analyze student learning. Careful attention has been given to the learner user experience, especially as it pertains to input for mathematical concepts essential for signal processing. Students have multiple work input options: students can type inside text or LaTeX editors or they can sketch answers using an embedded graphics editor, upload any image or PDF document via their personal computer, or email photos of pen-and-pencil work using the innovative Smart Device Upload feature. These features simplify student submission of free-form work. Students can also view assignments and their due dates, check assignment grades, and access feedback and Analytics from one place. The student Analytics feature empowers each student to track his or her individual competencies and outcomes by topic. OST has already been adopted by educators at Rice, Georgia Tech, UT-El Paso, Rose Hulman, Arizona State, UMass, Ecole Polytechnique Federale de Lausanne, Claremont McKenna, the University of North Carolina, and others. Beginning in fall 2011, OST has hosted 12 distinct courses. Of those, 3 courses have been reprised during multiple semesters for a total of 20 courses hosted on OST. All courses hosted so far are STEM courses. Over half are core signal processing courses or prerequisites. The course catalog can be viewed at https://openstaxtutor.org/catalog. A finding of note from two experiments at UT El Paso showed that, in comparison to immediate feedback, delayed feedback promoted long term retention and application of engineering concepts. Interestingly, students strongly preferred immediate feedback even though they learned more with delayed feedback – a nice example of how student preferences do not always align with their best learning. Massive Open Online Courses Massive Open Online Courses (MOOCs) are online courses offered free of charge to any student wishing to enroll. PI-Baraniuk and Co-PI Johnson offered their signature undergraduate courses via MOOCs to expand opportunity to learn electrical engineering and signal processing fundamentals in a structured class environment. Johnson taught Fundamentals of Electrical Engineering on Coursera (www.coursera.org) in Spring 2013 and Spring 2014. ‘Fundamentals’ is a 14-week course duplicating the course of the same name taught to all Rice University ECE majors, typically during the sophomore year. Also offered in Spring 2014 was the complementary lab course, Fundamentals of Electrical Engineering Laboratory. This course allowed online students to experience a true hands-on lab. National Instruments partnered with this course to provide a customized breadboard and virtual instruments (oscilloscope, oscillator, multi-meter) that students used to layout experiments and measure data. Baraniuk taught Discrete Time Signals and Systems on edX (https://www.edx.org) in Spring 2014. The course is the discrete time portion of his Rice University course Signals and Systems, typically taken by all ECE juniors and a follow-up of Johnson’s courses above. The course used extensive Matlab simulation and programming assignments and with sponsorship from the MathWorks. Further Expansion and Development. Based on the success of the infrastructure research conducted as part of this CI TEAM grant, OpenStax has been encouraged by several private foundations to expand this approach beyond Signal Processing. As a result it has been actively developing the next iteration of OpenStax Tutor geared towards serving students in high school, advanced placement, and also intro level general education classes. Pilots of the next iteration will begin in Fall of 2015.
