Engaging Students in Data Literacy: Lessons Learned from Data Intensive Classrooms

Abstract

This paper offers one approach to teaching relevant data literacy skills, knowledge, and attitudes using authentic assignments and assessments that provide students motivational and real-world data applications in order for them to demonstrate their data literacy skills. Students are expected to demonstrate data literacy skills through: Demonstrating knowledge of relevant analytic methods, and to recognize and apply quantitative algorithms, techniques and interpret results; Demonstrating strategic thinking skills, combined with a solid technical foundation in data and model driven decision making; Developing the ability to apply critical and analytical methods to formulate and solve science, engineering, medical, and business problems; Effectively communicating analytic findings to non-specialists. We discuss some of the pilot projects that we conducted inside the data-intensive courses such as Data Analytics and Data Science and share insights from the lessons learned to provide ideal pedagogical environments to make our students' data literate.