Data information literacy instruction in Business and Public Health

Macy, Katharine; Coates, Heather L.

doi:10.1177/0340035216673382

Cited by 19 publications

(14 citation statements)

References 17 publications

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“…Over time, the educational objectives have become more specific, either by discipline (e.g. agriculture (Carlson and Bracke 2015), engineering (Holles and Schmidt 2018;Johnston and Jeffryes 2014a), meteorology (Frank and Pharo 2016), business (Macy and Coates 2016), climate sciences (Thielen et al 2017), GIS (Widener and Reese 2016), architecture, history, and social work (Addison, Aaron and Moore, Jennifer 2015), or by skill set (visualization (Konkiel, Marshall, and Polley 2013), personal health data (Johnson 2017), and personal digital archiving (Mannheimer and Banta 2018). Active learning is often an integral part of this instruction to provide students a hands-on experience.…”

Section: Introductionmentioning

confidence: 99%

Integrating Data Science Tools into a Graduate Level Data Management Course

Pascuzzi¹,

Nelson²

2018

JeSLIB

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Section: Introductionmentioning

confidence: 99%

Integrating Data Science Tools into a Graduate Level Data Management Course

Pascuzzi¹,

Nelson²

2018

JeSLIB

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“…These frameworks have helped librarians and other researchers determine DL needs in general education. Several studies have used Prado and Marzal's DL framework, including a syllabi project identifying DL skills in research assignments (Brodsky 2017) and a project promoting the use of curriculum integrated data information literacy (Macy and Coates 2016). Other studies have also used Carlson et al's framework, including to pilot a DL program in an agriculture course (Carlson and Bracke 2015), to develop a new competency matrix for data management skills (Nelson 2017), and to create a data credibility checklist for STEM undergraduate students (Zilinski and Nelson 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Curriculum Data Deep Dive: Identifying Data Literacies in the Disciplines

Klenke

Schultz

Tokarz

et al. 2020

JeSLIB

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Objective: Evaluate and examine Data Literacy (DL) in the supported disciplines of four liaison librarians at a large research university. Methods: Using a framework developed by Prado and Marzal (2013), the study analyzed 378 syllabi from a two-year period across six departments-Criminal Justice, Geography, Geology, Journalism, Political Science, and Sociology-to see which classes included DLs. Results: The study was able to determine which classes hit on specific DLs and where those classes might need more support in other DLs. The most common DLs being taught in courses are Reading, Interpreting, and Evaluating Data, and Using Data. The least commonly taught are Understanding Data and Managing Data skills. Conclusions: While all disciplines touched on data in some way, there is clear room for librarians to support DLs in the areas of Understanding Data and Managing Data.

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“…Early library involvement in data management education offered non-discipline-specific sessions in order to maximize their audience (Adamick, Reznik-Zellen, & Sheridan, 2012;Clement, Blau, Abbaspour, & Gandour-Rood, 2017;Johnston & Jeffryes, 2014b;Kvale & Stangeland, 2017;Muilenburg, Lebow, & Rich, 2014;Whitmire, 2015;Wiljes & Cimiano, 2019). Other libraries designed their education to focus on a particular project such as a water quality field station (Carlson & Stowell-Bracke, 2013), or to address a specific discipline such as engineering (Johnston & Jeffryes, 2014a), natural resources and ecology (Wright & Andrews, 2015), business and public health (Macy & Coates, 2016), meteorology (Frank & Pharo, 2016), or social sciences (Thielen & Hess, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Centering Graduate Students’ Research Projects in Data Management Education: A Pilot Program

Griffin¹

2020

Journal of Librarianship and Scholarly Communication

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INTRODUCTION Data management education has been part of library service models for almost 2 decades. This paper describes a pilot graduate student education program whose framework shows interdependencies between data management practices, uses a flipped classroom model to allow maximum time for implementation, and whose primary activities are entirely student research based. LITERATURE REVIEW Education in data management encompasses many different formats (in-person, online, synchronous, asynchronous). Within this instruction, Data Information Literacy competencies help define student-learning objectives for data management tasks. Currently data management education is a combination of theory and active learning, with students asking for more hands-on practice. PROGRAM DESCRIPTION This program is an 8-week, inperson, flipped classroom series that addresses all data life cycle stages and aligns with many Data Information Literacy competencies. It is entirely student research data focused in that activities require that they use their projects, with significant time allocated to implement these practices while in the classroom. NEXT STEPS With a 69% retention rate and student improvement in seven foundational data management concepts, this program is considered a success. Future work involves converting this program to a credit-bearing course.

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Data information literacy instruction in Business and Public Health

Cited by 19 publications

References 17 publications

Integrating Data Science Tools into a Graduate Level Data Management Course

Integrating Data Science Tools into a Graduate Level Data Management Course

Curriculum Data Deep Dive: Identifying Data Literacies in the Disciplines

Centering Graduate Students’ Research Projects in Data Management Education: A Pilot Program

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