Stagnant Student Performance and Widening Achievement Gaps
a vociferous debate over what to blame, from subpar reading instruction to poverty to uneven implementation of the Common Core
A Crisis in Elite College Admissions
Declining Trust in Higher Education
a survey from the Pew Research Center found that 59 percent of Republicans and those who lean Republican believe colleges have a negative effect on the country.
Betsy DeVos, continued to draw criticism for rolling back oversight of for-profit colleges and weakening protections for bilked students.
The Democratic Party Backed Away From Charter Schools
Charters in cities like New York and Boston have shown promising achievement gains. But the sector has come under increasing fire on the left for harsh discipline practices, contributing to school segregation and serving fewer students with special needs. Teachers unions tend to oppose the schools’ expansion, since most of them are not unionized.
I will be in D.C. on January 29 at Politics & Prose, 7 p.m. SAVE THE DATE!
Diane Ravitch – Slaying Goliath: The Passionate Resistance to Privatization and the Fight to Save America’s Public Schools | Politics and Prose Bookstore https://t.co/9QyYE76gAh
for the past 21 years its organizer, the Oakland, Calif.-based nonprofit known as NewSchools Venture Fund, has also put millions of dollars into novel schools in public districts
Charter schools operate with public funding, and sometimes philanthropic support, but are managed by an outside organization that is independent from local district oversight. In California, they are run by nonprofit organizations with self-elected boards. (For-profit charters are outlawed.)
Their supporters and operators—who make up the vast majority of the 1,300-plus attendees at this year’s Summit—say the model offers the flexibility needed to introduce, test and adopt new curriculum, tools and pedagogical approaches that could better serve students, particularly in low-income and minority communities.
Rocketship Education was an early showcase for blended learning, where students rotate between working on computers and in small groups with teachers. Summit Public Schools, a network of charters that now claims a nationwide footprint, promotes project-based learning assisted by an online learning platform.
But charters have also attracted an increasingly vocal opposition, who charge them with funneling students, teachers and funds from traditional district schools. Aside from raising teacher salaries, a sticking point in the recent California teachers’ strikes in Los Angeles and Oakland has been stopping the growth of charter schools.
Detractors can point to fully-virtual charters, run by for-profit companies, that have been fined for misleading claims and graduating students at rates far below those at traditional schools. At the same time, research suggests that students attending charter schools in urban regions outperform their peers in traditional school settings.
While the first decade of this century saw double-digit percentage increase in the number of such schools, it has almost entirely plateaued (at 1 percent growth) in the 2017-2018 school year, according to data from the National Alliance for Public Charter Schools.
But the rationale that I find most disturbing—despite, or perhaps because of, the fact that it’s rarely made explicit—is the idea that technology will increase our efficiency…at teaching the same way that children have been taught for a very long time. Perhaps it hasn’t escaped your notice that ed tech is passionately embraced by very traditional schools: Their institutional pulse quickens over whatever is cutting-edge: instruction that’s blended, flipped, digitally personalized.
We can’t answer the question “Is tech useful in schools?” until we’ve grappled with a deeper question: “What kinds of learning should be taking place in those schools?”
Tarting up a lecture with a SmartBoard, loading a textbook on an iPad, looking up facts online, rehearsing skills with an “adaptive learning system,” writing answers to the teacher’s (or workbook’s) questions and uploading them to Google Docs—these are examples of how technology may make the process a bit more efficient or less dreary but does nothing to challenge the outdated pedagogy. To the contrary: These are shiny things that distract us from rethinking our approach to learning and reassure us that we’re already being innovative.
putting grades online (thereby increasing their salience and their damaging effects), using computers to administer tests and score essays, and setting up “embedded” assessment that’s marketed as “competency-based.” (If your instinct is to ask “What sort of competency? Isn’t that just warmed-over behaviorism?”
But as I argued not long ago, we shouldn’t confuse personalized learning with personal learning. The first involves adjusting the difficulty level of prefabricated skills-based exercises based on students’ test scores, and it requires the purchase of software. The second involves working with each student to create projects of intellectual discovery that reflect his or her unique needs and interests, and it requires the presence of a caring teacher who knows each child well.a recent review found that studies of tech-based personalized instruction “show mixed results ranging from modest impacts to no impact” – despite the fact that it’s remarkably expensive.
an article in Education Week, “a host of national and regional surveys suggest that teachers are far more likely to use tech to make their own jobs easier and to supplement traditional instructional strategies than to put students in control of their own learning.”
OECD reportednegative outcomes when students spent a lot of time using computers, while Stanford University’s Center for Research on Education Outcomes (CREDO) concluded that online charter schools were basically a disaster.
Larry Cuban, Sherry Turkle, Gary Stager, and Will Richardson.
Emily Talmage points out, uncannily aligned with the wish list of the Digital Learning Council, a group consisting largely of conservative advocacy groups and foundations, and corporations with a financial interest in promoting ed tech.
the digital age only exacerbates the need for a school librarian, which he describes as a position that far exceeds “book manager.”
A bill sponsored by Sen. Becky Harris in the 2017 session proposed changing that. SB143 would have required public schools, including charters, to maintain a school library and staff a certified librarian, except in specific circumstances.
“Students are technology literate when they come to Miller,” he said. “But they are informationally illiterate.”
The American Association of School Librarians is in the beginning stages of studying state policies regarding school libraries and librarians, said the organization’s president, Steven Yates. He doesn’t think eliminating school libraries or the position is a widespread movement in K-12 education, but he acknowledged that’s happening in more places than just Southern Nevada.
Over the past 10 years, new learning management systems (LMSs) have sprung on the scene to rival the Blackboards and Moodles of old. On the EdSurge Product Index alone, 56 products self-identify and fall into the LMS category. And with certain established companies like Pearson pulling out of the LMS ranks, where do you start?
As University of Central Florida’s Associate Vice President of Distributed Learning, Tom Cavanagh, wrote in an article for EDUCAUSE, “every institute has a unique set of instructional and infrastructure circumstances to consider when deciding on an LMS,” but at the same time, “all institutions face certain common requirements”—whether a small charter school, a private university or a large public school district.
The LMS Checklist
#1: Is the platform straightforward and user-friendly?
#2: Who do we want to have access to this platform, and can we adjust what they can see?
#3: Can the instructor and student(s) talk to and communicate with each other easily?
“Students and faculty live a significant portion of their daily lives online in social media spaces,” writes University of Central Florida’s Tom Cavanagh in his article on the LMS selection process. “Are your students and faculty interested in these sorts of interplatform connections?”
#5: Does this platform plug in with all of the other platforms we have?
“Given the pace of change and the plethora of options with educational technology, it’s very difficult for any LMS vendor to keep up with stand-alone tools that will always outperform built-in tools,” explains Michael Truong, executive director of innovative teaching and technology at Azusa Pacific University. According to Truong, “no LMS will be able to compete directly with tools like Piazza (discussion forum), Socrative (quizzing), EdPuzzle (video annotation), etc.”
As a result, Truong says, “The best way to ‘prepare’ for future technological changes is to go with an LMS that plays well with external tools.”
U.S. Education Secretary spared no words in her critique of education reform efforts during the Bush and Obama administrations. “I don’t think there is much we can hold onto, from a federal level, that we can say was a real success,”
This is not the first time DeVos has praised personalized learning. The education secretary visited Thomas Russell Middle School in Milpitas, Calif
Her vision of personalized learning has plenty of detractors. Educators and administrators have already begun to voice their reservations about personalized learning in schools. At a gathering of educators in Oakland last October speakers decried what they described as the privatization of public education through the introduction of technology initiatives such as personalized learning. More recently, former AltSchool educator Paul Emerich wrote a blog post titled, “Why I Left Silicon Valley, EdTech, and ‘Personalized’ Learning,” where he offered critiques of the personalized learning movement in his school. The post touched on concerns about his workload and interactions with students.
Parents are raising pressure too. In at least two states, their concerns over screen-time and digital content used in online educational platform has forced districts to suspend the implementation of technology-enabled personalized learning programs such as Summit Learning.
De Vos pointed to previous federal-led education funding programs as a “carrot” that made little or no impact. Her critique is not unfounded: A report published last year by the Education Department’s research division found that the $7 billion School Improvement Grants program made “no significant impacts” on test scores, high school graduation rates or college enrollment.
Common Core is currently adopted in 36 states, according to EdWeek’s Common Core Tracker, last updated September 2017.
++++++++++++++++
DeVos: ‘Common Core Is Dead’; A Large Online Charter School Is Shut Down
One of the largest online charter schools in the country closed this week amid a financial and legal dispute with the state of Ohio.
Education Secretary Betsy DeVos in a keynote address this week to the American Enterprise Institute.
She also cited a survey by the American Federation of Teachers that 60 percent of its teachers reported having moderate to no influence over the content and skills taught in their own classrooms. That same survey also noted that 86 percent of teachers said they do not feel respected by DeVos.
Victor Pickard, associate professor of communication at the University Pennsylvania’s Annenberg School, whose research focuses on internet policy and the political economy of media.
with each new victory for the American telecommunications oligopoly, that digital optimism fades further from view.
Definition:
Net neutrality protections are essentially safeguards that prevent internet service providers (ISPs) from interfering with the internet. Net neutrality gives the FCC the regulatory authority to prevent ISPs like Comcast and Verizon from slowing down or blocking certain types of content. It also prevents them from offering what’s known as paid prioritization, where an ISP could let particular websites or content creators pay more for faster streaming and download times. With paid prioritization an ISP could shake down a company like Netflix or an individual website owner, coercing them to pay more in order to be in the fast lane.
Net neutrality often gets treated as a sort of technocratic squabble over ownership and control of internet pipes. But in fact it speaks to a core social contract between government, corporations, and the public. What it really comes down to is, how can members of the public obtain information and services, and express ourselves creatively and politically, without interference from massive corporations?
Should we think of the internet as a good, a service, an infrastructure, or something else?
It’s all of the above.
The internet has been radically privatized. It wasn’t inevitable, but through policy decisions over the years, the internet has become increasingly commodified. Meanwhile it’s really difficult to imagine living in modern society without fast internet services — it’s no longer a luxury but a necessity for everything ranging from education to health to livelihood. The “digital divide” is a phrase that sounds like it’s from the 1990s, but it’s still very relevant. Somewhere around one fifth of American households don’t have access to wireline broadband services. It’s a social problem. We should be thinking about the internet as a public service and subsidizing it to make sure that everyone has access.
In your recent book on media democracy, you discuss the rise of what you call “corporate libertarianism.” What is corporate libertarianism and how does it relate to net neutrality?
Corporate libertarianism is an ideological project that has origins at a core moment in the 1940s. It sees corporations as having individual freedoms, like those in the First Amendment, which they can use to shield themselves from public interest oversight and regulation. It’s also often connected to this assumption that the government should never intervene in markets, and media markets in particular. (My note: Milton Friedman)
Of course, this is a libertarian mythology — the government is always involved. The question ought to be how it should be involved. Under corporate libertarianism it’s assumed that the government should only be involved in ways that enhance profit maximization for communication oligopolies.
There are clear dangers associated with vertical integration, where the company that owns the pipes is able to control the dissemination of information, and able to set the terms by which we access that information.
There have been cases like this already. In 2005, the company Telus, which is the second largest telecommunications company in Canada, began blocking access to a server that hosted a website that supported a labor strike against Telus.
Net neutrality is just one part of the story. What other regulations, policies and interventions could resist corporate control of the internet?
Roughly half of Americans live in communities that have access to only one ISP. My note: Ha Ha Ha, “pick me, pick me,” as Dori from “Finding Nemo” will say… Charter, whatever they will rename themselves again, is the crass example in Central MN.
Strategies to contain and confront monopolies:
break them up, and to prevent monopolies and oligopolies from happening in the first place by blocking mergers and acquisitions.
if we’re not going to outright nationalize them then we want to heavily regulate them, and enforce some kind of social contract where they’re compelled to provide a public service in exchange for the right to operate.
create public alternatives, like municipal wireless networks that can circumvent and compete with corporate monopolies. There’s a growing number of these publicly owned and governed internet infrastructures, and building more is crucial.
The announcement by Silicon Valley personalized learning startup AltSchool that it will close two of its lab school campuses in Palo Alto and New York City has some educators concerned that the company is putting profits over efforts to improve education, EdSurge reports.
Butler Middle School (PA) Principal Jason Huffman also told the publication that he sees the company’s struggles as parallel to those of other education reform models that didn’t live up to their promises, adding that organizations like Future Ready have made similar models and platforms for personalization available at a lower cost to schools and districts.
Education historian Diane Ravitch, a former assistant secretary of education under President George H.W. Bush, has been among the chief critics of these increasingly close ties, especially as it pertains to charter schools and voucher programs, decrying efforts to “turn us from citizens into consumers.” Public schools, she has said, should be focused around “building a sense of community, having a sense of democracy at the local level, having people from different backgrounds coming together to solve problems and learn how to be citizens.”
But AltSchool’s intentions with its lab schools and approach to developing its platform have seemed noble enough, with the company partnering with schools of varying sizes to learn how to best scale up successful approaches to personalized learning for traditional public schools. Its lab schools have been noted for eschewing traditional grade level structures and curriculum, attracting funding from the likes of Mark Zuckerberg.
Earlier this year, we visited one of the company’s partner schools — Berthold Academy, a Montessori in Reston, VA — to find out more about what AltSchool was looking to model and how its partnerships worked, finding a promising approach in action.
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Title:
Learning to Harness Big Data in an Academic Library
Abstract (200)
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
Method
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Sampling design
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Project Schedule
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
References:
Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.
Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.
Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.
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