Project Description
With the advent and increased use of the internet, social media has become an integral part of people’s lives. Platforms such as Facebook, Twitter, and TikTok generate a large volume of data that can be analyzed for a range of insights. This underscores the need for educational opportunities in which students can explore big data approaches to extract, visualize, and critically analyze complex algorithms and data structures. This demonstration project will develop a big data curriculum that uses cutting-edge social media data mining techniques via Twitter and a culturally relevant design to engage students from underrepresented groups in the West Texas/El Paso region. The curriculum will be co-designed by a team of teachers and students, and then piloted in El Paso high schools, which have a large population of students who are underrepresented. The outcomes of this project have the potential to transform models of computing and data literacy in which students access their own personal interests to participate in the creation of computational artifacts and navigate the products of others.
This BPC Demonstration Project aims to provide evidence-based insights on “Big Data”-centric computer and data science teaching and learning with underrepresented pre-college student populations. The team will iteratively develop and pilot a culturally relevant data mining and analytics curricular unit with groups of teachers and students who, respectively, serve or come from underrepresented groups. The team will leverage mixed-methodological approaches to examine learning outcomes for CS education and the learning sciences. This research is guided by two research questions: (1) What critical learning and instructional resources are needed to productively sustain a CS curricular intervention that emphasizes culturally relevant data mining and analytics?, (2) What learning experiences and outcomes result when implementing a CS education program that emphasizes culturally relevant data mining and analytics?
Key Collaborators
Dr. David Espalin is an Assistant Professor at the Department of Mechanical Engineering within The University of Texas at El Paso. He also serves as the Director of Research at the W.M. Keck Center for 3D Innovation – a multi-disciplinary center focused on the advancement and adoption of additive manufacturing (or more commonly known as 3D Printing) through activities in education, research, outreach, technology development and commercialization, and industrial partnerships. His current research is in the area of hybrid additive manufacturing, large area additive manufacturing, 3D electronics fabrication, and design software development. Through the development of custom machines at UTEP,
Dr. Espalin has enabled multi-technology manufacturing that allows not only depositing thermoplastic materials, but also the use of wire embedding, machining, foil application, and robotic component placement to achieve the fabrication of multi-functional devices. This type of fabrication has also been extended to metals 3D printing. These combined manufacturing capabilities provide a unique research space where design software, materials and processes, and robotics influence the final fabricated product. As an educator, Dr. Espalin is interested in research related to increasing the participation of minorities and disadvantaged students in engineering. Dr. Espalin has published over 20 refereed manuscripts in technical journals and filed over 10 patents. His work has been published, for example, in Journal of Mechanisms and Robotics, IEEE Sensors, Rapid Prototyping Journal, Journal of Manufacturing Science and Engineering, and International Journal of Advanced Manufacturing Technology.
David Espalin, PhD
Assistant Professor
Dr. Wicker’s areas of expertise include additive manufacturing processes, applications and materials. Additive Manufacturing (AM -- more popularly known as 3D Printing) fabricates a 3D structure with complex geometries directly from a file defined by computer-aided design. The Keck Center focuses on research to enhance these technologies to include the ability to print multi-functional structures with electronics, sensors, antennas, fluidics, and thermal management to name just a few. Fundamental and applied research is required specifically in AM processes, materials and applications.
Ryan Wicker, PhD
Professor
Key Outcomes
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Graduate Student Research Assistants Mentored
