Eaton, M. E. (2017). Seeing Seeing Library Data: A Prototype Data Visualization Application for Librarians. Journal of Web Librarianship, 11(1), 69–78. Retrieved from http://academicworks.cuny.edu/kb_pubs
Visualization can increase the power of data, by showing the “patterns, trends and exceptions”
Librarians can benefit when they visually leverage data in support of library projects.
Nathan Yau suggests that exploratory learning is a significant benefit of data visualization initiatives (2013). We can learn about our libraries by tinkering with data. In addition, handling data can also challenge librarians to improve their technical skills. Visualization projects allow librarians to not only learn about their libraries, but to also learn programming and data science skills.
The classic voice on data visualization theory is Edward Tufte. In Envisioning Information, Tufte unequivocally advocates for multi-dimensionality in visualizations. He praises some incredibly complex paper-based visualizations (1990). This discussion suggests that the principles of data visualization are strongly contested. Although Yau’s even-handed approach and Cairo’s willingness to find common ground are laudable, their positions are not authoritative or the only approach to data visualization.
a web application that visualizes the library’s holdings of books and e-books according to certain facets and keywords. Users can visualize whatever topics they want, by selecting keywords and facets that interest them.
To give SeeCollections a unified visual theme, I have used Bootstrap. Bootstrap is most commonly used to make webpages responsive to different devices
D3.js facilitates the binding of data to the content of a web page, which allows manipulation of the web content based on the underlying data.
Free Webinar: Driving Decisions With Data
with Analytics On Demand, you can add value to your library’s existing data and unlock key insights about your community.
Monday, July 24, 2017 12 p.m. Central
Tune in to this free 60-minute webcast Joining us for this webinar are:
- Jason Kucsma, deputy director, Toledo Lucas County (Ohio) Public Library
- Liz Bondie, education sales consultant, Gale, a Cengage company
more on data analytics in this IMS blog
New Report Examines Use of Big Data in Ed
By Dian Schaffhauser 05/17/17
new report from the National Academy of Education “Big Data in Education,” summarizes the findings of a recent workshop held by the academy
three federal laws: Family Educational Rights and Privacy Act (FERPA), the Children’s Online Privacy Protection Act (COPPA) and the Protection of Pupil Rights Amendment (PPRA).
over the last four years, 49 states and the District of Columbia have introduced 410 bills related to student data privacy, and 36 states have passed 85 new education data privacy laws. Also, since 2014, 19 states have passed laws that in some way address the work done by researchers.
researchers need to get better at communicating about their projects, especially with non-researchers.
One approach to follow in gaining trust “from parents, advocates and teachers” uses the acronym CUPS:
- Collection: What data is collected by whom and from whom;
- Use: How the data will be used and what the purpose of the research is;
- Protection: What forms of data security protection are in place and how access will be limited; and
- Sharing: How and with whom the results of the data work will be shared.
Second, researchers must pin down how to share data without making it vulnerable to theft.
Third, researchers should build partnerships of trust and “mutual interest” pertaining to their work with data. Those alliances may involve education technology developers, education agencies both local and state, and data privacy stakeholders.
Along with the summary report, the results of the workshop are being maintained on a page within the Academy’s website here.
more on big data in education in this IMS blog
Researchers use an app to predict GPA based on smartphone use
Dartmouth College and the University of Texas at Austin have developed an app that tracks smartphone activity to compute a grade point average that’s within 0.17 of a point.
More on Big Data in education in this blog: