Searching for "data analytics"

big data

big-data-in-education-report

Center for Digital Education (CDE)

real-time impact on curriculum structure, instruction delivery and student learning, permitting change and improvement. It can also provide insight into important trends that affect present and future resource needs.

Big Data: Traditionally described as high-volume, high-velocity and high-variety information.
Learning or Data Analytics: The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Educational Data Mining: The techniques, tools and research designed for automatically extracting meaning from large repositories of data generated by or related to people’s learning activities in educational settings.
Predictive Analytics: Algorithms that help analysts predict behavior or events based on data.
Predictive Modeling: The process of creating, testing and validating a model to best predict the probability of an outcome.

Data analytics, or the measurement, collection, analysis and reporting of data, is driving decisionmaking in many institutions. However, because of the unique nature of each district’s or college’s data needs, many are building their own solutions.

For example, in 2014 the nonprofit company inBloom, Inc., backed by $100 million from the Gates Foundation and the Carnegie Foundation for the Advancement of Teaching, closed its doors amid controversy regarding its plan to store, clean and aggregate a range of student information for states and districts and then make the data available to district-approved third parties to develop tools and dashboards so the data could be used by classroom educators.22

Tips for Student Data Privacy

Know the Laws and Regulations
There are many regulations on the books intended to protect student privacy and safety: the Family Educational Rights and Privacy Act (FERPA), the Protection of Pupil Rights Amendment (PPRA), the Children’s Internet Protection Act (CIPA), the Children’s Online Privacy Protection Act (COPPA) and the Health Insurance Portability and Accountability Act (HIPAA)
— as well as state, district and community laws. Because technology changes so rapidly, it is unlikely laws and regulations will keep pace with new data protection needs. Establish a committee to ascertain your institution’s level of understanding of and compliance with these laws, along with additional safeguard measures.
Make a Checklist Your institution’s privacy policies should cover security, user safety, communications, social media, access, identification rules, and intrusion detection and prevention.
Include Experts
To nail down compliance and stave off liability issues, consider tapping those who protect privacy for a living, such as your school attorney, IT professionals and security assessment vendors. Let them review your campus or district technologies as well as devices brought to campus by students, staff and instructors. Finally, a review of your privacy and security policies, terms of use and contract language is a good idea.
Communicate, Communicate, Communicate
Students, staff, faculty and parents all need to know their rights and responsibilities regarding data privacy. Convey your technology plans, policies and requirements and then assess and re-communicate those throughout each year.

“Anything-as-a-Service” or “X-as-a-Service” solutions can help K-12 and higher education institutions cope with big data by offering storage, analytics capabilities and more. These include:
• Infrastructure-as-a-Service (IaaS): Providers offer cloud-based storage, similar to a campus storage area network (SAN)

• Platform-as-a-Service (PaaS): Opens up application platforms — as opposed to the applications themselves — so others can build their own applications
using underlying operating systems, data models and databases; pre-built application components and interfaces

• Software-as-a-Service (SaaS): The hosting of applications in the cloud

• Big-Data-as-a-Service (BDaaS): Mix all the above together, upscale the amount of data involved by an enormous amount and you’ve got BDaaS

Suggestions:

Use accurate data correctly
Define goals and develop metrics
Eliminate silos, integrate data
Remember, intelligence is the goal
Maintain a robust, supportive enterprise infrastructure.
Prioritize student privacy
Develop bullet-proof data governance guidelines
Create a culture of collaboration and sharing, not compliance.

more on big data in this IMS blog:

https://blog.stcloudstate.edu/ims/?s=big+data&submit=Search

Predictive Analytics

Educational Intelligence and the Student Lifecycle – Leveraging Predictive Analytics for Profit in Higher Education

This presentation will begin on Wednesday, August 12, 2015 at 02:00 PM Eastern Daylight Time.

Wednesday, August 12, 2015 02:00 PM EDT

This webinar will provide an overview of the student lifecycle – from lead generation to job placement. You will learn what the components are and how student data can be leveraged for competitive gain through the use of predictive analytics tools. While these technologies have been in use by other industries for many years, especially in the area of assessing consumer demand, higher education is a relatively late adopter. As an example of benefit, colleges and universities can deploy them to determine which students are most at risk for attrition and – armed with deep, historical data – craft segment-specific retention strategies designed to compel them to persist toward degree completion. During this session, Eduventures analysts will provide concrete examples of how predictive analytics has been used within the student lifecycle at a variety of institutions, citing interviews with practitioners, that led to measurable performance improvements. To conclude, we will uncover the benefits of sharing data amongst key stakeholders to the ultimate gain of the institution and its constituents.

Speakers:

Jeff Alderson
Principal Analyst
Max Woolf
Senior Analyst

Audience members may arrive 15 minutes in advance of this time.

 

Twitter Analytics

How to Improve Your Tweets Using Twitter Analytics

http://www.socialmediaexaminer.com/improve-tweets-using-twitter-analytics/

Twitter ads and Twitonomy are helpful and cost-effective. Find time to go through these reports to see what works for you and your competition. The improvement in results from your Twitter marketing will be worth it.

Once you get comfortable with this kind of data review, check back every week, month or quarter to make sure that you are still hitting the optimal mark. The social media world moves fast, and analytics will help you keep pace with the changes.

personalized learning and achievement gap

https://www.edsurge.com/news/2022-03-28-can-personalized-learning-be-scaled-to-ease-teacher-burdens-and-close-achievement-gaps

McGraw Hill Plus, a new tool, Focusing first on math and then expanding to ELA and science, its objective is to make personalized learning scalable.

Smith: The modern classroom sits at the intersection of blended learning, competency-based learning and personalized learning.

reimagine instructional time and use technology to scale personalized learning.

First, pulling data into one place is the key fundamental driver that will change the teacher workflow. Second, we need to manipulate that data into some advanced data visualization tools, so it’s easy for teachers to understand and use. Third, we need to be able to visualize student performance and take action on it.
Using these data analytics, we can drive personalized learning based on student performance. And the last thing is the automation of teacher workflow.

eachers get data visualization from different sources, such as an adaptive software solution like our ALEKS program, our Redbird Mathematics, or our recently acquired Achieve3000 Literacy.

metaverse definition

What the metaverse will (and won’t) be, according to 28 experts

metaverse (hopefully) won’t be the virtual world of ‘Snow Crash,’ or ‘Ready Player One.’ It will likely be something more complex, diverse, and wild.

The metaverse concept clearly means very different things to different people. What exists right now is a series of embryonic digital spaces, such as Facebook’s HorizonEpic Games’ FortniteRoblox‘s digital space for gaming and game creation, and the blockchain-based digital world Decentraland–all of which have clear borders, different rules and objectives, and differing rates of growth.

TIFFANY ROLFE

different layers of realities that we can all be experiencing, even in the same environment or physical space. We’re already doing that with our phones to a certain extent—passively in a physical environment while mentally in a digital one. But we’ll see more experiences beyond your phone, where our whole bodies are fully engaged, and that’s where the metaverse starts to get interesting—we genuinely begin to explore and live in these alternate realities simultaneously.

RONY ABOVITZ, FOUNDER, MAGIC LEAP

Xverse

It will have legacy parts that look and feel like the web today, but it will have new nodes and capabilities that will look and feel like the Ready Player One Oasis (amazing gaming worlds), immersion leaking into our world (like my Magicverse concept), and every imaginable permutation of these. I feel that the Xverse will have gradients of sentience and autonomy, and we will have the emergence of synthetic life (things Sun and Thunder is working on) and a multitude of amazing worlds to explore. Building a world will become something everyone can do (like building a webpage or a blog) and people will be able to share richer parts of their external and inner lives at incredibly high-speed across the planet.

YAT SIU, COFOUNDER AND EXECUTIVE CHAIRMAN OF GAMING AND BLOCKCHAIN COMPANY ANIMOCA BRANDS

Reality will exist on a spectrum ranging from physical to virtual (VR), but a significant chunk of our time will be spent somewhere between those extremes, in some form of augmented reality (AR). Augmented reality will be a normal part of daily life. Virtual companions will provide information, commentary, updates and advice on matters relevant to you at that point in time, including your assets and activities, in both virtual and real spaces.

TIMONI WEST, VP OF AUGMENTED AND VIRTUAL REALITY, UNITY:

I think we can all agree our initial dreams of a fully immersive, separate digital world is not only unrealistic, but maybe not what we actually want. So I’ve started defining the metaverse differently to capture the zeitgeist: we’re entering an era where every computer we interact with, big or small, is increasingly world-aware. They can recognize faces, voices, hands, relative and absolute position, velocity, and they can react to this data in a useful way. These contextually aware computers are the path to unlocking ambient computing: where computers fade from the foreground to the background of everyday, useful tools. The metaverse is less of a ‘thing’ and more of a computing era. Contextual computing enables a multitude of new types of interactions and apps: VR sculpting tools and social hangouts, self-driving cars, robotics, smart homes.

SAM HAMILTON, HEAD OF COMMUNITY & EVENTS FOR BLOCKCHAIN-BASED METAVERSE CREATOR THE DECENTRALAND FOUNDATION

NITZAN MEKEL-BOBROV, CHIEF AI OFFICER, EBAY

as carbon is to the organic world, AI will be both the matrix that provides the necessary structural support and the material from which digital representation will be made. Of all the ways in which AI will shape the form of the metaverse, perhaps most essential is the role it will play in the physical-digital interface. Translating human actions into digital input–language, eye movement, hand gestures, locomotion–these are all actions which AI companies and researchers have already made tremendous progress on.

HUGO SWART, VICE PRESIDENT AND GM OF XR, QUALCOMM

Qualcomm views the metaverse as an ever-present spatial internet complete with personalized digital experiences that spans the physical and virtual worlds, where everything and everyone can communicate and interact seamlessly.

IBRAHIM BAGGILI, FOUNDING DIRECTOR, CONNECTICUT INSTITUTE OF TECHNOLOGY AT UNIVERSITY OF NEW HAVEN

As an active researcher in the security and forensics of VR systems, should the metaverse come into existence, we should explore and hypothesize the ways it will be misused.

CHITRA RAGAVAN, CHIEF STRATEGY OFFICER AT BLOCKCHAIN DATA ANALYTICS COMPANY ELEMENTUS 

I picture [the metaverse] almost like The Truman Show. Only, instead of walking into a television set, you walk into the internet and can explore any number of different realities

JOHN HANKE, CEO OF POKÉMON GO CREATOR NIANTIC

We imagine the metaverse as reality made better, a world infused with magic, stories, and functionality at the intersection of the digital and physical worlds.

CAROLINA ARGUELLES NAVAS, GLOBAL PRODUCT MARKETING, AUGMENTED REALITY, SNAP

Rather than building the “metaverse,” a separate and fully virtual reality that is disconnected from the physical world, we are focused on augmenting reality, not replacing it. We believe AR–or computing overlaid on the world around us–has a smoother path to mass adoption, but will also be better for the world than a fully virtual world.

URHO KONTTORI, COFOUNDER AND CTO OF AR/VR HEADSET MAKER VARJO

In the reality-based metaverse, we will be able to more effectively design products of the future, meet and collaborate with our colleagues far away, and experience any remote place in real-time.

ATHERINE ALLEN, CEO OF IMMERSIVE TECH RESEARCH CONSULTANCY LIMINA IMMERSIVE

I prefer to think of the metaverse as simply bringing our bodies into the internet.

BRANDS IN THE METAVERSE

VISHAL SHAH, VP OF METAVERSE, FACEBOOK

The metaverse isn’t just VR! Those spaces will connect to AR glasses and to 2D spaces like Instagram. And most importantly, there will be a real sense of continuity where the things you buy are always available to you.

SAYON DEB, MANAGER, MARKET RESEARCH, CONSUMER TECHNOLOGY ASSOCIATION

At its core will be a self-contained economy that allows individuals and businesses to create, own or invest in a range of activities and experiences.

NANDI NOBELL, SENIOR ASSOCIATE AT GLOBAL ARCHITECTURE AND DESIGN FIRM CALLISONRTKL

the metaverse experience can be altered from the individual’s point of view and shaped or curated by any number of agents—whether human or A.I. In that sense, the metaverse does not have an objective look beyond its backend. In essence, the metaverse, together with our physical locations, forms a spatial continuum.

NICK CHERUKURI, CEO AND FOUNDER OF MIXED REALITY GLASSES MAKER THIRDEYE

The AR applications of the metaverse are limitless and it really can become the next great version of the internet.

SAM TABAR, CHIEF STRATEGY OFFICER, BITCOIN MINING COMPANY BIT DIGITAL

It seems fair to predict that the actual aesthetic of any given metaverse will be determined by user demand. If users want to exist in a gamified world populated by outrageous avatars and fantastic landscapes then the metaverse will respond to that demand. Like all things in this world the metaverse will be market driven

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More on meta-verse in this blog
https://blog.stcloudstate.edu/ims?s=metaverse

Amazon Health Care

https://newrepublic.com/article/162553/amazon-care-pharmacy-big-tech-universal-healthcare

Microsoft has a data initiative with Providence St. Joseph Health, which operates dozens of hospitals in the United States. In 2019, Google signed a deal with the Mayo Clinic to manage and parse health records for “insights,” explaining that cloud computing and data analytics would provide better performance. Google also reached an agreement this week with HCA Healthcare, a large hospital chain

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more on data privacy in this IMS blog
https://blog.stcloudstate.edu/ims?s=data+privacy

influential tools for online learning

Online Learning’s ‘Greatest Hits’

Robert Ubell (Columnist)     Feb 20, 2019

https://www.edsurge.com/news/2019-02-20-online-learning-s-greatest-hits

dean of web-based distance learning

Learning Management Systems

Neck and neck for the top spot in the LMS academic vendor race are Blackboard—the early entry and once-dominant player—and coming-up quickly from behind, the relatively new contender, Canvas, each serving about 6.5 million students . The LMS market today is valued at $9.2 billion.

Digital Authoring Systems

Faced with increasingly complex communication technologies—voice, video, multimedia, animation—university faculty, expert in their own disciplines, find themselves technically perplexed, largely unprepared to build digital courses.

instructional designers, long employed by industry, joined online academic teams, working closely with faculty to upload and integrate interactive and engaging content.

nstructional designers, as part of their skillset, turned to digital authoring systems, software introduced to stimulate engagement, encouraging virtual students to interface actively with digital materials, often by tapping at a keyboard or touching the screen as in a video game. Most authoring software also integrates assessment tools, testing learning outcomes.

With authoring software, instructional designers can steer online students through a mixtape of digital content—videos, graphs, weblinks, PDFs, drag-and-drop activities, PowerPoint slides, quizzes, survey tools and so on. Some of the systems also offer video editing, recording and screen downloading options

Adaptive Learning

As with a pinwheel set in motion, insights from many disciplines—artificial intelligence, cognitive science, linguistics, educational psychology and data analytics—have come together to form a relatively new field known as learning science, propelling advances in a new personalized practice—adaptive learning.

MOOCs

Of the top providers, Coursera, the Wall Street-financed company that grew out of the Stanford breakthrough, is the champion with 37 million learners, followed by edX, an MIT-Harvard joint venture, with 18 million. Launched in 2013, XuetangX, the Chinese platform in third place, claims 18 million.

Former Yale President Rick Levin, who served as Coursera’s CEO for a few years, speaking by phone last week, was optimistic about the role MOOCs will play in the digital economy. “The biggest surprise,” Levin argued, “is how strongly MOOCs have been accepted in the corporate world to up-skill employees, especially as the workforce is being transformed by job displacement. It’s the right time for MOOCs to play a major role.”

In virtual education, pedagogy, not technology, drives the metamorphosis from absence to presence, illusion into reality. Skilled online instruction that introduces peer-to-peer learning, virtual teamwork and other pedagogical innovations stimulate active learning. Online learning is not just another edtech product, but an innovative teaching practice. It’s a mistake to think of digital education merely as a device you switch on and off like a garage door.

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more on online learning in this IMS blog
https://blog.stcloudstate.edu/ims?s=online+learning

philosophy technology

McMullan, T. (2018, April 26). How Technology Got Under Our Skin – Featured Stories. Retrieved April 2, 2019, from Medium website: https://medium.com/s/story/how-technology-got-under-our-skin-cee8a71b241b

anthropocene

Like the circle-bound symmetry of Leonardo Da Vinci’s Vitruvian Man, the meat and bones of the human race are the same in 2018 as they were in 1490. And yet, we are different.

Michael Patrick Lynch, writer and professor of philosophy at the University of Connecticut.
“The digital revolution is more like the revolution brought on by the written word. Just as the written word allowed us to time-travel — to record our thoughts for others, including ourselves, to read in the future — so the internet has allowed for a kind of tele-transportation , breaking down barriers of space and physical limitation and connecting us across the globe in ways we now take for granted, as we do the written word.”

In the book Self-Tracking, authors Gina Neff, a sociology professor at Oxford University, and Dawn Nafus, a research scientist at Intel, describe this phenomenon as a shuffling between physical signs and observed recordings: “The data becomes a ‘prosthetic of feeling,’Advocates of this “prosthetic of feeling” argue that self-tracking can train people to recognize their own body signals, tuning the senses to allow for a greater grasp of biological rhythms.but what if the body-as-data is exploited by the state, or by an insurance company that can predict when you’ll get diabetes, or a data analytics firm that can use it to help sway elections? The Chinese government is going so far as to plan a social credit score for its citizens by 2020, giving each of the country’s 1.3 billion residents a reputation number based on economic and social status. What is particularly subtle about all this is that, like a scientific épistémè, our way of thinking is perhaps unconsciously guided by the configurations of knowledge these new technologies allow. We don’t question it.

Hannah Knox. Computational machines are “shaping what we expect it means to be a human”, Knox wrote for the Corsham Institute’s Observatory for a Connected Society.

Facebook goads us to remember past moments on a daily basis, the stacked boxes of tape in Beckett’s play replaced with stacks of servers in remote data centers in northern Sweden.“There is reasonable evidence that [the internet] has reduced our internal memory ability,” says Phil Reed, a professor of psychology at Swansea University.

Moderate tech use correlated with positive mental health, according to a paper published in Psychological Science by Andrew Przybylski of Oxford and Netta Weinstein at Cardiff University, who surveyed 120,000 British 15-year-olds.Again, the crucial question is one of control. If our way of thinking is changed by our intimacy with these technologies, then is this process being directed by individuals, or the ledgers of private companies, or governments keen on surveilling their citizens? If we conceive of these systems as extensions of our own brains, what happens if they collapse?

Brain-machine interfaces (BMI) are coming in leaps and bounds, with companies like Neuralink and CTRL-Labs in the United States exploring both surgical and noninvasive processes that allow computers to be controlled directly by signals from the brain. It’s a field that involves fundamentally changing the relationship between our minds, bodies, and machines.Kevin Warwick, emeritus professor at Coventry University and a pioneer in implant technology

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more on philosophy in this IMS blog
https://blog.stcloudstate.edu/ims?s=philosophy

SCSU at 2018 LITA Library Technology Forum

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On behalf of the 2018 LITA Library Technology Forum Committee, I am pleased to notify you that your proposal, “Virtual Reality (VR) and Augmented Reality (AR) for Library Orientation: A Scalable Approach to Implementing VR/AR/MR in Education”, has been accepted for presentation at the 2018 LITA Library Technology Forum in Minneapolis, Minnesota (November 8-10).
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Mark Gill and Plamen Miltenoff will participate in a round table discussion Friday. November 9, 3:30PM at Haytt Regency, Minneapolis, MN. We will stream live on Facebook: https://www.facebook.com/InforMediaServices/

SCSU Augmented Reality Library Tour from Plamen Miltenoff

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Notes from the Forum

Risk and Reward: Public Interest and the Public Good at the Intersection of Law, Tech, and Libraries

https://thatandromeda.github.io/forum18_schedule/

Blog: Copyright Librarian; Twitter: @CopyrightLibn

U of MN has a person, whose entire job is to read and negotiate contracts with vendors. No resources, not comfortable to negotiate contracts and vendors use this.

If you can’t open it, you don’t own it. if it is not ours… we don’t get what we don’t ask for.

libraries are now developing plenty, but if something is brought in, so stop analytics over people. Google Analytics collects data, which is very valuable for students. bring coherent rink of services around students and show money saving. it is not possible to make a number of copyright savings. collecting such data must be in the library, not outside. Data that is collected, will be put to use. Data that is collected, will be put to uses that challenge library values. Data puts people at risk. anonymized data is not anonymous. rethink our relationship to data. data sensitivity is contextual.

stop requiring MLSs for a lot of position. not PhDs in English, but people with specific skills.

perspective taking does not help you understand what others want.  connection to tech. user testing – personas (imagining one’s perspective). we need to ask, better employ the people we want to understand. in regard of this, our profession is worse then other professions.

pay more is important to restore value of the profession.

https://docs.google.com/document/d/1lLHP2TZnmrRodSdulPPOruEeF20iwF5zw6h5aOV8ogg/edit

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Library System Migrations: Issues and Solutions 

https://drive.google.com/open?id=109w_NU3zki_A6Fukpa50zzGJdgazbVSKqf7zAoYaKsc

from Sierra to Alma. SFX. number of challenges

Stanford – Folio, Cornell, Duke and several others. https://www.folio.org/ Alma too locked up for Stanford.

Easy Proxy for Alma Primo

Voyager to OCLC. Archive space from in-house to vendor. Migration

Polaris, payments, scheduling, PC sign up.  Symphony, but discussing migration to Polaris to share ILS. COntent Diem. EasyProxy, from Millenium no Discovery Layer to Koha and EDS. ILL.

WMS to Alma. Illinois State – CARLY – from Voyager to Alma Primo. COntent Diem, Dynex to Koha.

Princeton: Voyager, migrating Alma and FOlio. Ex Libris. Finances migrate to PeopleSoft. SFX. Intota

RFPs – Request for Proposals stage. cloud and self-hosted bid.

Data Preparation. all data is standard, consistent. divorce package for vendors (preparing data to be exported (~10K). the less to migrate, the better, so prioritize chunks of data (clean up the data)

Data. overwhelming for the non-tech services. so a story is welcome. Design and Admin background, not librarian background, big picture, being not a librarian helps not stuck with the manusha (particular records)

teams and committees – how to compile a great team. who makes the decision. ORCHID integration. Blog or OneNote place to share information. touch base with everyone before they come to the meeting. the preplanning makes large meetings more productive.

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Using Design Thinking — Do we really want a makerspace? 

makerbot replicator 3d printer

one touch studio 4 ready record studio. data analytics + several rooms to schedule.

lighting turned on when USB drive inserted.

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Article Shortcuts 

2:30 – 2:50

Talk To the Phone (Because the Human Is Overwhelmed) 

Google physical web beacons, NFC lables, QR codes, Augmented Reality. magnetic position. nearby navigations

 

IRDL proposal

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.

 

 

Research Literature

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”
– Dan Ariely, 2013  https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/

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:

 

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