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. Lin is Professor of Learning Technologies at the University of North Texas. Currently, she also serves as the Director for the Texas Center for Educational Technology (TCET, https://tcet.unt.edu/), and the Development Editor-in-Chief of the journal Educational Technology Research and Development (ETR&D, https://www.springer.com/journal/11423).
Dr. Lin’s research looks into intersections of mind, brain, technology and learning. Specifically, she has published in areas such as creativity, human-computer interaction, virtual reality, media multitasking, multimedia design, and learning in digital and hybrid spaces.
Lin Lin, PhD
Professor
Seyedahmad Rahimi, Ph.D., is an assistant professor of Educational Technology in the School of Teaching and Learning at the University of Florida. Dr. Rahimi’s research focuses on assessing and fostering students’ 21st-century skills (focusing on creativity) and STEM-related knowledge acquisition (focusing on physics understanding). Toward that end, Dr. Rahimi designs, develops, and evaluates immersive learning environments (e.g., educational games) equipped with stealth assessment and educational data mining and learning analytics models. These learning environments can diagnostically assess students’ various competency levels, predict different outcomes, and act accordingly in real-time (e.g., adapt the game challenges to students’ level of competency or support students’ learning by triggering the appropriate learning supports).
Dr. Rahimi is also actively researching various aspects of educational games (e.g., game mechanics, game difficulty, cognitive and affective supports, dashboard design, and incentive systems) and how they affect students’ motivation, performance, and learning.
Seyedahmad Rahimi, PhD
Assistant Professor
Key Outcomes
