Searching for "surveillance education"

surveillance technology and education

https://www.edsurge.com/news/2019-06-10-is-school-surveillance-going-too-far-privacy-leaders-urge-a-slow-down

New York’s Lockport City School District, which is using public funds from a Smart Schools bond to help pay for a reported $3.8 million security system that uses facial recognition technology to identify individuals who don’t belong on campus

The Lockport case has drawn the attention of national media, ire of many parents and criticism from the New York Civil Liberties Union, among other privacy groups.

the Future of Privacy Forum (FPF), a nonprofit think tank based in Washington, D.C., published an animated video that illustrates the possible harm that surveillance technology can cause to children and the steps schools should take before making any decisions, such as identifying specific goals for the technology and establishing who will have access to the data and for how long.

A few days later, the nonprofit Center for Democracy and Technology, in partnership with New York University’s Brennan Center for Justice, released a brief examining the same topic.

My note: same considerations were relayed to the SCSU SOE dean in regard of the purchase of Premethean and its installation in SOE building without discussion with faculty, who work with technology. This information was also shared with the dean: http://blog.stcloudstate.edu/ims/2018/10/31/students-data-privacy/

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more on surveillance in education in this IMS blog
http://blog.stcloudstate.edu/ims?s=surveillance+education

Facial Recognition Technology in schools

With Safety in Mind, Schools Turn to Facial Recognition Technology. But at What Cost?

By Emily Tate     Jan 31, 2019

https://www.edsurge.com/news/2019-01-31-with-safety-in-mind-schools-turn-to-facial-recognition-technology-but-at-what-cost

SAFR (Secure, Accurate Facial Recognition)

violent deaths in schools have stayed relatively constant over the last 30 years, according to data from the National Center for Education Statistics. But then there’s the emotive reality, which is that every time another event like Sandy Hook or Parkland occurs, many educators and students feel they are in peril when they go to school.

RealNetworks, a Seattle-based software company that was popular in the 1990s for its audio and video streaming services but has since expanded to offer other tools, including SAFR (Secure, Accurate Facial Recognition), its AI-supported facial recognition software.

After installing new security cameras, purchasing a few Apple devices and upgrading the school’s Wi-Fi, St. Therese was looking at a $24,000 technology tab.

The software is programmed to allow authorized users into the building with a smile.

“Facial recognition isn’t a panacea. It is just a tool,” says Collins, who focuses on education privacy issues.

Another part of the problem with tools like SAFR, is it provides a false sense of security.

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

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

media literacy backfire

Did Media Literacy Backfire?

Jan 5, 2017danah boyd

https://points.datasociety.net/did-media-literacy-backfire-7418c084d88d

Understanding what sources to trust is a basic tenet of media literacy education.

Think about how this might play out in communities where the “liberal media” is viewed with disdain as an untrustworthy source of information…or in those where science is seen as contradicting the knowledge of religious people…or where degrees are viewed as a weapon of the elite to justify oppression of working people. Needless to say, not everyone agrees on what makes a trusted source.

Students are also encouraged to reflect on economic and political incentives that might bias reporting. Follow the money, they are told. Now watch what happens when they are given a list of names of major power players in the East Coast news media whose names are all clearly Jewish. Welcome to an opening for anti-Semitic ideology.

In the United States, we believe that worthy people lift themselves up by their bootstraps. This is our idea of freedom. To take away the power of individuals to control their own destiny is viewed as anti-American by so much of this country. You are your own master.

Children are indoctrinated into this cultural logic early, even as their parents restrict their mobility and limit their access to social situations. But when it comes to information, they are taught that they are the sole proprietors of knowledge. All they have to do is “do the research” for themselves and they will know better than anyone what is real.

Combine this with a deep distrust of media sources.

Many marginalized groups are justifiably angry about the ways in which their stories have been dismissed by mainstream media for decades.It took five days for major news outlets to cover Ferguson. It took months and a lot of celebrities for journalists to start discussing the Dakota Pipeline. But feeling marginalized from news media isn’t just about people of color.

Keep in mind that anti-vaxxers aren’t arguing that vaccinations definitively cause autism. They are arguing that we don’t know. They are arguing that experts are forcing children to be vaccinated against their will, which sounds like oppression. What they want is choice — the choice to not vaccinate. And they want information about the risks of vaccination, which they feel are not being given to them. In essence, they are doing what we taught them to do: questioning information sources and raising doubts about the incentives of those who are pushing a single message. Doubt has become tool.

Addressing so-called fake news is going to require a lot more than labeling. It’s going to require a cultural change about how we make sense of information, whom we trust, and how we understand our own role in grappling with information. Quick and easy solutions may make the controversy go away, but they won’t address the underlying problems.

In the United States, we’re moving towards tribalism (see Fukuyama), and we’re undoing the social fabric of our country through polarization, distrust, and self-segregation.

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boyd, danah. (2014). It’s Complicated: The Social Lives of Networked Teens (1 edition). New Haven: Yale University Press.
p. 8 networked publics are publics that are reconstructed by networked technologies. they are both space and imagined community.
p. 11 affordances: persistence, visibility, spreadability, searchability.
p. technological determinism both utopian and dystopian
p. 30 adults misinterpret teens online self-expression.
p. 31 taken out of context. Joshua Meyrowitz about Stokely Charmichael.
p. 43 as teens have embraced a plethora of social environment and helped co-create the norms that underpin them, a wide range of practices has emerged. teens have grown sophisticated with how they manage contexts and present themselves in order to be read by their intended audience.
p. 54 privacy. p. 59 Privacy is a complex concept without a clear definition. Supreme Court Justice Brandeis: the right to be let alone, but also ‘measure of th access others have to you through information, attention, and physical proximity.’
control over access and visibility
p. 65 social steganography. hiding messages in plain sight
p. 69 subtweeting. encoding content
p. 70 living with surveillance . Foucault Discipline and Punish
p. 77 addition. what makes teens obsessed w social media.
p. 81 Ivan Goldberg coined the term internet addiction disorder. jokingly
p. 89 the decision to introduce programmed activities and limit unstructured time is not unwarranted; research has shown a correlation between boredom and deviance.
My interview with Myra, a middle-class white fifteen-year-old from Iowa, turned funny and sad when “lack of time” became a verbal trick in response to every question. From learning Czech to trakc, from orchestra to work in a nursery, she told me that her mother organized “98%” of her daily routine. Myra did not like all of these activities, but her mother thought they were important.
Myra noted that her mother meant well, but she was exhausted and felt socially disconnected because she did not have time to connect with friends outside of class.
p. 100 danger
are sexual predators lurking everywhere
p. 128 bullying. is social media amplifying meanness and cruelty.
p. 131 defining bullying in a digital era. p. 131 Dan Olweus narrowed in the 70s bulling to three components: aggression, repetition and imbalance on power. p. 152 SM has not radically altered the dynamics of bullying, but it has made these dynamics more visible to more people. we must use this visibility not to justify increased punishment, but to help youth who are actually crying out for attention.
p. 153 inequality. can SM resolve social divisions?
p. 176 literacy. are today’s youth digital natives? p. 178 Barlow and Rushkoff p. 179 Prensky. p. 180 youth need new literacies. p. 181 youth must become media literate. when they engage with media–either as consumers or producers–they need to have the skills to ask questions about the construction and dissemination of particular media artifacts. what biases are embedded in the artifact? how did the creator intend for an audience to interpret the artifact, and what are the consequences of that interpretation.
p. 183 the politics of algorithms (see also these IMS blog entries http://blog.stcloudstate.edu/ims?s=algorithms) Wikipedia and google are fundamentally different sites. p. 186 Eli Pariser, The Filter Bubble: the personalization algorithms produce social divisions that undermine any ability to crate an informed public. Harvard’s Berkman Center have shown, search engines like Google shape the quality of information experienced by youth.
p. 192 digital inequality. p. 194 (bottom) 195 Eszter Hargittai: there are signifficant difference in media literacy and technical skills even within age cohorts. teens technological skills are strongly correlated with socio-economic status. Hargittai argues that many youth, far from being digital natives, are quite digitally naive.
p. 195 Dmitry  Epstein: when society frames the digital divide as a problem of access, we see government and industry as the responsible party for the addressing the issue. If DD as skills issue, we place the onus on learning how to manage on individuals and families.
p. 196 beyond digital natives

Palfrey, J., & Gasser, U. (2008). Born Digital: Understanding the First Generation of Digital Natives (1 edition). New York: Basic Books.

John Palfrey, Urs Gasser: Born Digital
Digital Natives share a common global culture that is defined not by age, strictly, but by certain attributes and experience related to how they interact with information technologies, information itself, one another, and other people and institutions. Those who were not “born digital’ can be just as connected, if not more so, than their younger counterparts. And not everyone born since, say 1982, happens to be a digital native.” (see also http://blog.stcloudstate.edu/ims/2018/04/15/no-millennials-gen-z-gen-x/

p. 197. digital native rhetoric is worse than inaccurate: it is dangerous
many of the media literacy skills needed to be digitally savvy require a level of engagement that goes far beyond what the average teen pick up hanging out with friends on FB or Twitter. Technical skills, such as the ability to build online spaces requires active cultivation. Why some search queries return some content before others. Why social media push young people to learn how to to build their own systems, versus simply using a social media platforms. teens social status and position alone do not determine how fluent or informed they are via-a-vis technology.
p. 199 Searching for a public on their own

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Daum, M. (2018, August 24). My Affair With the Intellectual Dark Web – Great Escape. Retrieved October 9, 2018, from https://medium.com/s/greatescape/nuance-a-love-story-ae6a14991059

the intellectual dark web

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more on media literacy in this IMS blog
http://blog.stcloudstate.edu/ims?s=media+literacy

fake news in this IMS blog
http://blog.stcloudstate.edu/ims?s=fake+news

AI tracks students writings

Schools are using AI to track what students write on their computers

By Simone Stolzoff August 19, 2018
50 million k-12 students in the US
Under the Children’s Internet Protection Act (CIPA), any US school that receives federal funding is required to have an internet-safety policy. As school-issued tablets and Chromebook laptops become more commonplace, schools must install technological guardrails to keep their students safe. For some, this simply means blocking inappropriate websites. Others, however, have turned to software companies like GaggleSecurly, and GoGuardian to surface potentially worrisome communications to school administrators
In an age of mass school-shootings and increased student suicides, SMPs Safety Management Platforms can play a vital role in preventing harm before it happens. Each of these companies has case studies where an intercepted message helped save lives.
Over 50% of teachers say their schools are one-to-one (the industry term for assigning every student a device of their own), according to a 2017 survey from Freckle Education
But even in an age of student suicides and school shootings, when do security precautions start to infringe on students’ freedoms?
When the Gaggle algorithm surfaces a word or phrase that may be of concern—like a mention of drugs or signs of cyberbullying—the “incident” gets sent to human reviewers before being passed on to the school. Using AI, the software is able to process thousands of student tweets, posts, and status updates to look for signs of harm.
SMPs help normalize surveillance from a young age. In the wake of the Cambridge Analytica scandal at Facebook and other recent data breaches from companies like Equifax, we have the opportunity to teach kids the importance of protecting their online data
in an age of increased school violence, bullying, and depression, schools have an obligation to protect their students. But the protection of kids’ personal information is also a matter of their safety

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more on cybersecurity in this IMS blog
http://blog.stcloudstate.edu/ims?s=cybersecurity

more on surveillance  in this IMS blog
http://blog.stcloudstate.edu/ims?s=surveillance

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

Digital Literacy for SPED 405

Digital Literacy for SPED 405. Behavior Theories and Practices in Special Education.

Instructor Mark Markell. mamarkell@stcloudstate.edu Mondays, 5:30 – 8:20 PM. SOE A235

Preliminary Plan for Monday, Sept 10, 5:45 PM to 8 PM

Introduction – who are the students in this class. About myself: http://web.stcloudstate.edu/pmiltenoff/faculty Contact info, “embedded” librarian idea – I am available to help during the semester with research and papers

about 40 min: Intro to the library: http://web.stcloudstate.edu/pmiltenoff/bi/
15 min for a Virtual Reality tours of the Library + quiz on how well they learned the library:
http://bit.ly/VRlib
and 360 degree video on BYOD:
Play a scavenger hunt IN THE LIBRARY: http://bit.ly/learnlib
The VR (virtual reality) and AR (augmented reality) component; why is it important?
why is this technology brought up to a SPED class?
http://blog.stcloudstate.edu/ims/2015/11/18/immersive-journalism/
autism: http://blog.stcloudstate.edu/ims/2018/09/10/sound-and-brain/
Social emotional learning
http://blog.stcloudstate.edu/ims/2018/05/31/vr-ar-sel-empathy/
(transition to the next topic – digital literacy)

about 50 min:

  1. Digital Literacy

How important is technology in our life? Profession?

http://blog.stcloudstate.edu/ims/2018/08/20/employee-evolution/

Do you think technology overlaps with the broad field of special education? How?
How do you define technology? What falls under “technology?”

What is “digital literacy?” Do we need to be literate in that sense? How does it differ from technology literacy?
http://blog.stcloudstate.edu/ims?s=digital+literacy

Additional readings on “digital literacy”
http://blog.stcloudstate.edu/ims/2017/08/23/nmc-digital-literacy/

Digital Citizenship: http://blog.stcloudstate.edu/ims/2015/10/19/digital-citizenship-info/
Play Kahoot: https://play.kahoot.it/#/k/e844253f-b5dd-4a91-b096-b6ff777e6dd7
Privacy and surveillance: how does these two issues affect your students? Does it affect them more? if so, how?  http://blog.stcloudstate.edu/ims/2018/08/21/ai-tracks-students-writings/

Social Media:
http://web.stcloudstate.edu/pmiltenoff/lib290/. if you want to survey the class, here is the FB group page: https://www.facebook.com/groups/LIB290/

Is Social Media part of digital literacy? Why? How SM can help us become more literate?

Digital Storytelling:
http://web.stcloudstate.edu/pmiltenoff/lib490/

How is digital storytelling essential in digital literacy?

about 50 min:

  1. Fake News and Research

Syllabus: Teaching Media Manipulation: https://datasociety.net/pubs/oh/DataAndSociety_Syllabus-MediaManipulationAndDisinformationOnline.pdf

#FakeNews is a very timely and controversial issue. in 2-3 min choose your best source on this issue. 1. Mind the prevalence of resources in the 21st century 2. Mind the necessity to evaluate a) the veracity of your courses b) the quality of your sources (the fact that they are “true” does not mean that they are the best). Be prepared to name your source and defend its quality.
How do you determine your sources? How do you decide the reliability of your sources? Are you sure you can distinguish “good” from “bad?”
Compare this entry https://en.wikipedia.org/wiki/List_of_fake_news_websites
to this entry: https://docs.google.com/document/d/10eA5-mCZLSS4MQY5QGb5ewC3VAL6pLkT53V_81ZyitM/preview to understand the scope

Do you know any fact checking sites? Can you identify spot sponsored content? Do you understand syndication? What do you understand under “media literacy,” “news literacy,” “information literacy.”  http://blog.stcloudstate.edu/ims/2017/03/28/fake-news-resources/

Why do we need to explore the “fake news” phenomenon? Do you find it relevant to your professional development?

Let’s watch another video and play this Kahoot: https://play.kahoot.it/#/k/21379a63-b67c-4897-a2cd-66e7d1c83027

So, how do we do academic research? Let’s play another Kahoot: https://play.kahoot.it/#/k/5e09bb66-4d87-44a5-af21-c8f3d7ce23de
If you to structure this Kahoot, what are the questions, you will ask? What are the main steps in achieving successful research for your paper?

  • Research using social media

what is social media (examples). why is called SM? why is so popular? what makes it so popular?

use SM tools for your research and education:

– Determining your topic. How to?
Digg http://digg.com/, Reddit https://www.reddit.com/ , Quora https://www.quora.com
Facebook, Twitter – hashtags (class assignment 2-3 min to search)
LinkedIn Groups
YouTube and Slideshare (class assignment 2-3 min to search)
Flickr, Instagram, Pinterest for visual aids (like YouTube they are media repositories)

Academia.com (https://www.academia.edu/Academia.edu, a paper-sharing social network that has been informally dubbed “Facebook for academics,” https://www.academia.edu/31942069_Facebook_for_Academics_The_Convergence_of_Self-Branding_and_Social_Media_Logic_on_Academia.edu

ResearchGate: https://www.researchgate.net/

– collecting and managing your resources:
Delicious https://del.icio.us/
Diigo: https://www.diigo.com/
Evernote: evernote.com OneNote (Microsoft)

blogs and wikis for collecting data and collaborating

– Managing and sharing your information:
Refworks,
Zotero https://www.zotero.org/,
Mendeley, https://www.mendeley.com/

– Testing your work against your peers (globally):

Wikipedia:
First step:Using Wikipedia.Second step: Contributing to Wikipedia (editing a page). Third step: Contributing to Wikipedia (creating a page)  https://www.evernote.com/shard/s101/sh/ef743d1a-4516-47fe-bc5b-408f29a9dcb9/52d79bfa20ee087900764eb6a407ec86

– presenting your information


please use this form to cast your feedback. Please feel free to fill out only the relevant questions:
http://bit.ly/imseval

Google go home

‘Google go home’: the Berlin neighbourhood fighting off a tech giant

Other cities have embraced the company, but in Kreuzberg opposition to a planned Google campus is vociferous. What makes Berlin different?

https://www.theguardian.com/cities/2018/may/09/fuck-off-google-the-berlin-neighbourhood-fighting-off-a-tech-giant-kreuzberg

Google’s sites in London, Madrid, Tel Aviv, Seoul, São Paulo and Warsaw (in a converted former vodka distillery) are hubs for entrepreneurs, providing workspace for startup founders as well as networking and educational events.

the recent offer from Sidewalk Labs – a company owned by Alphabet, Google’s parent company – to redevelop Toronto’s waterfront as a reason to be concerned about the company’s interests in potentially extracting data from cities.

Google’s history of tax evasion and mass surveillance as examples of actions that make it incompatible with the progressive values of the local area.

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more on Google in this IMS blog
http://blog.stcloudstate.edu/ims?s=google

China of Xi

Time of Xi



My note: CCTV (http://english.cctv.com/), accidentally overlaps with cctv (https://en.wikipedia.org/wiki/Closed-circuit_television): “also known as video surveillance”

China Central Television (formerly Beijing Television), commonly abbreviated as CCTV, is the predominant state television broadcaster in the People’s Republic of China. CCTV has a network of 50 channels broadcasting different programmes and is accessible to more than one billion viewers.[1] As of present, there are 50 television channels, and the broadcaster provides programming in six different languages. Most of its programmes are a mixture of news, documentary, social education, comedy, entertainment, and drama, the majority of which consists of Chinese soap operas and entertainment.[2]

https://en.wikipedia.org/wiki/China_Central_Television

CCTV is one of the official mouthpieces of the Communist Party of China, and is part of what is known in China as the “central three” (中央三台), with the others being China National Radio and China Radio International.

Fake news and CCTV

https://en.wikipedia.org/wiki/China_Central_Television

https://blogs.wsj.com/chinarealtime/2014/03/28/china-targets-fake-news/

http://ascportfolios.org/chinaandmedia/2011/01/31/fake-news-in-the-news/

https://www.huffingtonpost.com/entry/united-states-china-fake-news_us_592494d5e4b00c8df29f88d7

CCTV mentioned positively: http://www.bbc.com/news/world-asia-china-22424129

topics for IM260

proposed topics for IM 260 class

  • Media literacy. Differentiated instruction. Media literacy guide.
    Fake news as part of media literacy. Visual literacy as part of media literacy. Media literacy as part of digital citizenship.
  • Web design / web development
    the roles of HTML5, CSS, Java Script, PHP, Bootstrap, JQuery, React and other scripting languages and libraries. Heat maps and other usability issues; website content strategy. THE MODEL-VIEW-CONTROLLER (MVC) design pattern
  • Social media for institutional use. Digital Curation. Social Media algorithms. Etiquette Ethics. Mastodon
    I hosted a LITA webinar in the fall of 2016 (four weeks); I can accommodate any information from that webinar for the use of the IM students
  • OER and instructional designer’s assistance to book creators.
    I can cover both the “library part” (“free” OER, copyright issues etc) and the support / creative part of an OER book / textbook
  • Big Data.” Data visualization. Large scale visualization. Text encoding. Analytics, Data mining. Unizin. Python, R in academia.
    I can introduce the students to the large idea of Big Data and its importance in lieu of the upcoming IoT, but also departmentalize its importance for academia, business, etc. From infographics to heavy duty visualization (Primo X-Services API. JSON, Flask).
  • NetNeutrality, Digital Darwinism, Internet economy and the role of your professional in such environment
    I can introduce students to the issues, if not familiar and / or lead a discussion on a rather controversial topic
  • Digital assessment. Digital Assessment literacy.
    I can introduce students to tools, how to evaluate and select tools and their pedagogical implications
  • Wikipedia
    a hands-on exercise on working with Wikipedia. After the session, students will be able to create Wikipedia entries thus knowing intimately the process of Wikipedia and its information.
  • Effective presentations. Tools, methods, concepts and theories (cognitive load). Presentations in the era of VR, AR and mixed reality. Unity.
    I can facilitate a discussion among experts (your students) on selection of tools and their didactically sound use to convey information. I can supplement the discussion with my own findings and conclusions.
  • eConferencing. Tools and methods
    I can facilitate a discussion among your students on selection of tools and comparison. Discussion about the their future and their place in an increasing online learning environment
  • Digital Storytelling. Immersive Storytelling. The Moth. Twine. Transmedia Storytelling
    I am teaching a LIB 490/590 Digital Storytelling class. I can adapt any information from that class to the use of IM students
  • VR, AR, Mixed Reality.
    besides Mark Gill, I can facilitate a discussion, which goes beyond hardware and brands, but expand on the implications for academia and corporate education / world
  • IoT , Arduino, Raspberry PI. Industry 4.0
  • Instructional design. ID2ID
    I can facilitate a discussion based on the Educause suggestions about the profession’s development
  • Microcredentialing in academia and corporate world. Blockchain
  • IT in K12. How to evaluate; prioritize; select. obsolete trends in 21 century schools. K12 mobile learning
  • Podcasting: past, present, future. Beautiful Audio Editor.
    a definition of podcasting and delineation of similar activities; advantages and disadvantages.
  • Digital, Blended (Hybrid), Online teaching and learning: facilitation. Methods and techniques. Proctoring. Online students’ expectations. Faculty support. Asynch. Blended Synchronous Learning Environment
  • Gender, race and age in education. Digital divide. Xennials, Millennials and Gen Z. generational approach to teaching and learning. Young vs old Millennials. Millennial employees.
  • Privacy, [cyber]security, surveillance. K12 cyberincidents. Hackers.
  • Gaming and gamification. Appsmashing. Gradecraft
  • Lecture capture, course capture.
  • Bibliometrics, altmetrics
  • Technology and cheating, academic dishonest, plagiarism, copyright.

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 (http://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (http://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

Bail, C. A. (2014). The cultural environment: measuring culture with big data. Theory and Society, 43(3–4), 465–482. https://doi.org/10.1007/s11186-014-9216-5

Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press.

Bruns, A. (2013). Faster than the speed of print: Reconciling ‘big data’ social media analysis and academic scholarship. First Monday, 18(10). Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/4879

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.

Chen, X. W., & Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Access, 2, 514–525. https://doi.org/10.1109/ACCESS.2014.2325029

Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576

Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230

Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132

De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061

De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823

Dumbill, E. (2012). Making Sense of Big Data. Big Data, 1(1), 1–2. https://doi.org/10.1089/big.2012.1503

Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115

Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009

Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121

Harper, L., & Oltmann, S. (2017, April 2). Big Data’s Impact on Privacy for Librarians and Information Professionals. Retrieved November 7, 2017, from https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006

Hwangbo, H. (2014, October 22). The future of collaboration: Large-scale visualization. Retrieved November 7, 2017, from http://usblogs.pwc.com/emerging-technology/the-future-of-collaboration-large-scale-visualization/

Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.

Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746

Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015

Power, D. J. (2014). Using ‘Big Data’ for analytics and decision support. Journal of Decision Systems, 23(2), 222–228. https://doi.org/10.1080/12460125.2014.888848

Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508

Reilly, S. (2013, December 12). What does Horizon 2020 mean for research libraries? Retrieved November 7, 2017, from http://libereurope.eu/blog/2013/12/12/what-does-horizon-2020-mean-for-research-libraries/

Reyes, J. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends: Linking Research & Practice to Improve Learning, 59(2), 75–80. https://doi.org/10.1007/s11528-015-0842-1

Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data & Society, 1(2), 2053951714563194. https://doi.org/10.1177/2053951714563194

Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187

Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.

van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.

Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010

Weiss, A. (2018). Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals. Rowman & Littlefield Publishers. Retrieved from https://rowman.com/ISBN/9781538103227/Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals

West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance Studies at Brookings, 4, 1–0.

Willis, J. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause Review Online. Retrieved from https://docs.lib.purdue.edu/idcpubs/1

Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109

Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157

 

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more on big data





IoT

Survey: IoT Overtakes Mobile as Security Threat

By Rhea Kelly 06/05/17

https://campustechnology.com/articles/2017/06/05/survey-iot-overtakes-mobile-as-security-threat.aspx

a report from ISACA, a nonprofit association focused on knowledge and practices for information systems. The 2017 State of Cyber Security Study surveyed IT security leaders around the globe on security issues, the emerging threat landscape, workforce challenges and more.

  • 53 percent of survey respondents reported a year-over-year increase in cyber attacks;
  • 62 percent experienced ransomware in 2016, but only 53 percent have a formal process in place to address a ransomware attack;
  • 78 percent reported malicious attacks aimed at impairing an organization’s operations or user data;
  • Only 31 percent said they routinely test their security controls, while 13 percent never test them; and
  • 16 percent do not have an incident response plan.
  • 65 percent of organizations now employ a chief information security officers, up from 50 percent in 2016, yet still struggle to fill open cyber security positions;
  • 48 percent of respondents don’t feel comfortable with their staff’s ability to address complex cyber security issues;
  • More than half say cyber security professionals “lack an ability to understand the business”;
  • One in four organizations allot less than $1,000 per cyber security team member for training; and
  • About half of the organizations surveyed will see an increase in their cyber security budget, down from 61 percent in 2016.

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IoT to Represent More Than Half of Connected Device Landscape by 2021

By Sri Ravipati 06/09/17

https://campustechnology.com/articles/2017/06/09/iot-to-represent-more-than-half-of-connected-device-landscape-by-2021.aspx

20121 prediction for data in North America

analysis comes from Cisco’s recent Visual Networking Index for the 2016-2021 forecast period.

  • IP video traffic will increase from 73 percent of all internet consumer traffic in 2016 to 82 percent in 2021 (with live streaming accounting for 13 percent);
  • Virtual and augmented reality traffic is expected to increase 20-fold during the forecast period at a compound annual growth rate of 82 percent; and
  • Internet video surveillance traffic is anticipated to grow during the forecast period, comprising 3.4 percent of all internet traffic.

To learn more, view the full report.

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5 ways to use the Internet of Things in higher ed

By Danielle R. June 14th, 2017
 1. Labeling and Finding
 campus’ buildings were able to transmit interactive map data to a student finding their way around for the first time
2. Booking and Availability
3. Preparation
4. Intervention
As FitBit and other personal wearables become better at tracking various health markers, these markers can be put to use tracking individual patterns in the student body.
 The University of Southern California is currently researching the impact that analyzing IoT-gathered data can have on student performance, but the IoT can be used to prevent more than just academic difficulties.
the privacy concerns such use might raise; as universities implement systems that integrate wearables, they will encounter this hurdle and have to implement policies to address it.
5. Research
Laboratories are often required to be completely controlled spaces with considerations made for climate, light, and sometimes even biometric data inside the lab.

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http://blog.stcloudstate.edu/ims?s=internet+of+things
http://blog.stcloudstate.edu/ims?s=iot 

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