Incorporating data literacy into undergraduate information literacy programs in the social sciences

Abstract

Purpose -The purpose of this paper is to describe and analyze the confluence of data literacy with information literacy in an experimental one-unit course taught in the UCLA Department of Sociology, and present the literature on, rationale for, and future of integrating these interrelated literacies into social science courses. Design/methodology/approach -The course was co-taught twice by a librarian and a data archivist using a syllabus and assignments that reflect sociological research problems and tools and information literacy competencies in the social sciences. Findings -The need for information and data skills in sociology is well-established, and their integration into a sociology course (rather than in a stand-alone information literacy course) would produce more opportunities for students to apply what they learn and for the instructors to assess learning in the context of doing sociology coursework.Research limitations/implications -The class sizes were too small for full-scale assessment and pre-tests/post-tests were not given. Assessment of student learning was based on work produced in and outside class and on course evaluations. Practical implications -It is suggested that librarians and data archivists work with faculty to innovate curricular approaches based on recommendations and outcomes in key documents on learning sociology from professional library and sociology organizations. Attaching the lab to an existing course and promoting the data literacy modules for faculty to adopt in other courses are also suggested. Originality/value -This paper invites social science librarians to examine the value to students and faculty of collaboration with professional data services staff to teach and merge information and data literacy within the social sciences curricula.

Developing data literacy competencies to enhance faculty collaborations

“…The authors of this study also determined that most researchers in this study thought that offering some type of data literacy was required for their students (Carlson et al, 2013). Other studies have also concluded that academic librarians are well positioned to support data literacy and data competencies (Calzada Prado & Marzal, 2013;Gray, 2004;Schield, 2004;Stephenson & Schifter Caravello, 2007).…”

The Development of Data Science Education in China from the LIS Perspective

“…Regarding computer science education, Wang et al (2017) identified data scientists, big data system engineers, big data algorithm engineers, machine learning engineers and big data algorithm scientists as five distinct data talents, and suggested enhancing the big data abilities of computer science students by developing course architecture with a focus on big data and big data tools. In addition to these two disciplines, with the significant impact of the new science paradigm, educational programs in journalism, economic management, business, publishing science, biological sciences and social science also become more data-centric with more concentration on their students' awareness and abilities to tackle big data problems (Shen et al., 2014;Kirkpatrick, 2015;Bichler et al, 2017;Yu, 2017;Macmillan, 2015, Stephenson & Caravello, 2007.…”

Data Visualization and Information Literacy

“…He argues that both statistical literacy and data literacy need to be taught more widely. Stephenson and Caravello (2007) describe the challenges of implementing a classroom instruction program on data literacy.…”