While we often get distracted by the latest device or platform release, video has quietly been riding the wave of all of these advancements, benefiting from broader access to phones, displays, cameras and, most importantly, bandwidth. In fact, 68 percent of teachers are using video in their classrooms, and 74 percent of middle schoolers are watching videos for learning. From social media streams chock-full of video and GIFs to FaceTime with friends to two-hour Twitch broadcasts, video mediates students’ relationships with each other and the world. Video is a key aspect of our always-online attention economy that’s impacting voting behavior, and fueling hate speech and trolling. Put simply: Video is a contested civic space.
We need to move from a conflation of digital citizenship with internet safety and protectionism to a view of digital citizenship that’s pro-active and prioritizes media literacy and savvy. A good digital citizen doesn’t just dodge safety and privacy pitfalls, but works to remake the world, aided by digital technology like video, so it’s more thoughtful, inclusive and just.
1. Help Students Identify the Intent of What They Watch
equip students with some essential questions they can use to unpack the intentions of anything they encounter. One way to facilitate this thinking is by using a tool like EdPuzzle to edit the videos you want students to watch by inserting these questions at particularly relevant points in the video.
2. Be Aware That the Web Is a Unique Beast
Compared to traditional media (like broadcast TV or movies), the web is the Wild West.
At democracy’s heart lies a set of paradoxes: a delicate interplay of identity and anonymity, secrecy and transparency. To be sure you are eligible to vote and that you do so only once, the authorities need to know who you are. But when it comes time for you to mark a ballot, the government must guarantee your privacy and anonymity. After the fact, it also needs to provide some means for a third party to audit the election, while also preventing you from obtaining definitive proof of your choice, which could lead to vote selling or coercion.
Building a system that accomplishes all this at once — and does so securely — is challenging enough in the physical world. It’s even harder online, as the recent revelation that Russian intelligence operatives compromised voting systems in multiple states makes clear.
In the decade since the elusive Satoshi Nakamoto published an infamous white paper outlining the idea behind bitcoin, a “peer-to-peer electronic cash system” based on a mathematical “consensus mechanism,” more than 1,500 new cryptocurrencies have come into being.
definition: Nathan Heller in the New Yorker, in which he compares the blockchain to a scarf knit with a single ball of yarn. “It’s impossible to remove part of the fabric, or to substitute a swatch, without leaving some trace,” Heller wrote. Typically, blockchains are created by a set of stakeholders working to achieve consensus at every step, so it might be even more apt to picture a knitting collective creating that single scarf together, moving forward only when a majority agrees that a given knot is acceptable.
Unlike bitcoin, a public blockchain powered by thousands of miners around the world, most voting systems, including Votem’s, employ what’s known as a “permissioned ledger,” in which a handful of approved groups (political parties, election observers, government entities) would be allowed to validate the transactions.
there’s the issue of targeted denial-of-service (DoS) attacks, in which a hacker directs so much traffic at a server that it’s overwhelmed and ceases to function.
Although a distributed ledger itself would likely withstand such an attack, the rest of the system — from voters’ personal devices to the many servers a vote would pass through on its way to the blockchain — would remain vulnerable.
there’s the so-called penetration attack, like the University of Michigan incursion, in which an adversary gains control of a server and deliberately alters the outcome of an election.
While it’s true that information recorded on a blockchain cannot be changed, a determined hacker might well find another way to disrupt the process. Bitcoin itself has never been hacked, for instance, but numerous bitcoin “wallets” have been, resulting in billions of dollars in losses. In early June 2018, a South Korean cryptocurrency exchange was penetrated, causing the value of bitcoin to tumble and resulting in a loss of $42 billion in market value. So although recording the vote tally on a blockchain introduces a new obstacle to penetration attacks, it still leaves holes elsewhere in the system — like putting a new lock on your front door but leaving your basement windows open.
A blockchain is only as valuable as the data stored on it. And whereas traditional paper ballots preserve an indelible record of the actual intent of each voter, digital votes “don’t produce an original hard-copy record of any kind,”
In the end, democracy always depends on a certain leap of faith, and faith can never be reduced to a mathematical formula. The Economist Intelligence Unit regularly ranks the world’s most democratic counties. In 2017, the United States came in 21st place, after Uruguay and Malta. Meanwhile, it’s now widely believed that John F. Kennedy owed his 1960 win to election tampering in Chicago. The Supreme Court decision granting the presidency to George W. Bush rather than calling a do-over — despite Al Gore’s popular-vote win — still seems iffy. Significant doubts remain about the 2016 presidential race.
While little doubt remains that Russia favored Trump in the 2016 election, the Kremlin’s primary target appears to have been our trust in the system itself. So if the blockchain’s trendy allure can bolster trust in American democracy, maybe that’s a net positive for our national security. If someone manages to hack the system, hopefully they’ll do so quietly. Apologies to George Orwell, but sometimes ignorance really is strength.
United States digital literacy frameworks tend to focus on educational policy details and personal empowerment, the latter encouraging learners to become more effective students, better creators, smarter information consumers, and more influential members of their community.
National policies are vitally important in European digital literacy work, unsurprising for a continent well populated with nation-states and struggling to redefine itself, while still trying to grow economies in the wake of the 2008 financial crisis and subsequent financial pressures
African digital literacy is more business-oriented.
Middle Eastern nations offer yet another variation, with a strong focus on media literacy. As with other regions, this can be a response to countries with strong state influence or control over local media. It can also represent a drive to produce more locally-sourced content, as opposed to consuming material from abroad, which may elicit criticism of neocolonialism or religious challenges.
p. 14 Digital literacy for Humanities: What does it mean to be digitally literate in history, literature, or philosophy? Creativity in these disciplines often involves textuality, given the large role writing plays in them, as, for example, in the Folger Shakespeare Library’s instructor’s guide. In the digital realm, this can include web-based writing through social media, along with the creation of multimedia projects through posters, presentations, and video. Information literacy remains a key part of digital literacy in the humanities. The digital humanities movement has not seen much connection with digital literacy, unfortunately, but their alignment seems likely, given the turn toward using digital technologies to explore humanities questions. That development could then foster a spread of other technologies and approaches to the rest of the humanities, including mapping, data visualization, text mining, web-based digital archives, and “distant reading” (working with very large bodies of texts). The digital humanities’ emphasis on making projects may also increase
Digital Literacy for Business: Digital literacy in this world is focused on manipulation of data, from spreadsheets to more advanced modeling software, leading up to degrees in management information systems. Management classes unsurprisingly focus on how to organize people working on and with digital tools.
Digital Literacy for Computer Science: Naturally, coding appears as a central competency within this discipline. Other aspects of the digital world feature prominently, including hardware and network architecture. Some courses housed within the computer science discipline offer a deeper examination of the impact of computing on society and politics, along with how to use digital tools. Media production plays a minor role here, beyond publications (posters, videos), as many institutions assign multimedia to other departments. Looking forward to a future when automation has become both more widespread and powerful, developing artificial intelligence projects will potentially play a role in computer science literacy.
In traditional instruction, students’ first contact with new ideas happens in class, usually through direct instruction from the professor; after exposure to the basics, students are turned out of the classroom to tackle the most difficult tasks in learning — those that involve application, analysis, synthesis, and creativity — in their individual spaces. Flipped learning reverses this, by moving first contact with new concepts to the individual space and using the newly-expanded time in class for students to pursue difficult, higher-level tasks together, with the instructor as a guide.
Let’s take a look at some of the myths about flipped learning and try to find the facts.
Myth: Flipped learning is predicated on recording videos for students to watch before class.
Fact: Flipped learning does not require video. Although many real-life implementations of flipped learning use video, there’s nothing that says video must be used. In fact, one of the earliest instances of flipped learning — Eric Mazur’s peer instruction concept, used in Harvard physics classes — uses no video but rather an online text outfitted with social annotation software. And one of the most successful public instances of flipped learning, an edX course on numerical methods designed by Lorena Barba of George Washington University, uses precisely one video. Video is simply not necessary for flipped learning, and many alternatives to video can lead to effective flipped learning environments [http://rtalbert.org/flipped-learning-without-video/].
Fact: Flipped learning optimizes face-to-face teaching. Flipped learning may (but does not always) replace lectures in class, but this is not to say that it replaces teaching. Teaching and “telling” are not the same thing.
Myth: Flipped learning has no evidence to back up its effectiveness.
Fact: Flipped learning research is growing at an exponential pace and has been since at least 2014. That research — 131 peer-reviewed articles in the first half of 2017 alone — includes results from primary, secondary, and postsecondary education in nearly every discipline, most showing significant improvements in student learning, motivation, and critical thinking skills.
Myth: Flipped learning is a fad.
Fact: Flipped learning has been with us in the form defined here for nearly 20 years.
Myth: People have been doing flipped learning for centuries.
Fact: Flipped learning is not just a rebranding of old techniques. The basic concept of students doing individually active work to encounter new ideas that are then built upon in class is almost as old as the university itself. So flipped learning is, in a real sense, a modern means of returning higher education to its roots. Even so, flipped learning is different from these time-honored techniques.
Myth: Students and professors prefer lecture over flipped learning.
Fact: Students and professors embrace flipped learning once they understand the benefits. It’s true that professors often enjoy their lectures, and students often enjoy being lectured to. But the question is not who “enjoys” what, but rather what helps students learn the best.They know what the research says about the effectiveness of active learning
Assertion: Flipped learning provides a platform for implementing active learning in a way that works powerfully for students.
The Exposure Approach: we don’t provide a way for participants to determine if they learned anything new or now have the confidence or competence to apply what they learned.
The Exemplar Approach: from ‘show and tell’ for adults to show, tell, do and learn.
The Tutorial Approach: Getting a group that can meet at the same time and place can be challenging. That is why many faculty report a preference for self-paced professional development.build in simple self-assessment checks. We can add prompts that invite people to engage in some sort of follow up activity with a colleague. We can also add an elective option for faculty in a tutorial to actually create or do something with what they learned and then submit it for direct or narrative feedback.
The Course Approach: a non-credit format, these have the benefits of a more structured and lengthy learning experience, even if they are just three to five-week short courses that meet online or in-person once every week or two.involve badges, portfolios, peer assessment, self-assessment, or one-on-one feedback from a facilitator
The Academy Approach: like the course approach, is one that tends to be a deeper and more extended experience. People might gather in a cohort over a year or longer.Assessment through coaching and mentoring, the use of portfolios, peer feedback and much more can be easily incorporated to add a rich assessment element to such longer-term professional development programs.
The Mentoring Approach: The mentors often don’t set specific learning goals with the mentee. Instead, it is often a set of structured meetings, but also someone to whom mentees can turn with questions and tips along the way.
The Coaching Approach: A mentor tends to be a broader type of relationship with a person.A coaching relationship tends to be more focused upon specific goals, tasks or outcomes.
The Peer Approach:This can be done on a 1:1 basis or in small groups, where those who are teaching the same courses are able to compare notes on curricula and teaching models. They might give each other feedback on how to teach certain concepts, how to write syllabi, how to handle certain teaching and learning challenges, and much more. Faculty might sit in on each other’s courses, observe, and give feedback afterward.
The Self-Directed Approach:a self-assessment strategy such as setting goals and creating simple checklists and rubrics to monitor our progress. Or, we invite feedback from colleagues, often in a narrative and/or informal format. We might also create a portfolio of our work, or engage in some sort of learning journal that documents our thoughts, experiments, experiences, and learning along the way.
In 2014, administrators at Central Piedmont Community College (CPCC) in Charlotte, North Carolina, began talks with members of the North Carolina State Board of Community Colleges and North Carolina Community College System (NCCCS) leadership about starting a CBE program.
Building on an existing project at CPCC for identifying the elements of a digital learning environment (DLE), which was itself influenced by the EDUCAUSE publication The Next Generation Digital Learning Environment: A Report on Research,1 the committee reached consensus on a DLE concept and a shared lexicon: the “Digital Learning Environment Operational Definitions,
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.
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.
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 entrieshttps://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 https://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.
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 Gaggle, Securly, 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 casestudies 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
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
#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.” https://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?
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)
https://www-wired-com.cdn.ampproject.org/c/s/www.wired.com/story/187-things-the-blockchain-is-supposed-to-fix/amp
Blockchains, which use advanced cryptography to store information across networks of computers, could eliminate the need for trusted third parties, like banks, in transactions, legal agreements, and other contracts. The most ardent blockchain-heads believe it has the power to reshape the global financial system, and possibly even the internet as we know it.
Now, as the technology expands from a fringe hacker toy to legitimate business applications, opportunists have flooded the field. Some of the seekers are mercenaries pitching shady or fraudulent tokens, others are businesses looking to cash in on a hot trend, and still others are true believers in the revolutionary and disruptive powers of distributed networks.
Mentions of blockchains and digital currencies on corporate earnings calls doubled in 2017 over the year prior, according to Fortune. Last week at Consensus, the country’s largest blockchain conference, 100 sponsors, including top corporate consulting firms and law firms, hawked their wares.
Here is a noncomprehensive list of the ways blockchain promoters say they will change the world. They run the spectrum from industry-specific (a blockchain project designed to increase blockchain adoption) to global ambitions (fixing the global supply chain’s apparent $9 trillion cash flow issue).
Things Blockchain Technology Will Fix
Bots with nefarious intent
Skynet
People not taking their medicine
Device storage that could be used for bitcoin mining
Apps like WhatsApp, Facebook, Snapchat, Instagram, Twitter, LinkedIn, Viber
Felix Krause described in 2017 that when a user grants an app access to their camera and microphone, the app could do the following:
Access both the front and the back camera.
Record you at any time the app is in the foreground.
Take pictures and videos without telling you.
Upload the pictures and videos without telling you.
Upload the pictures/videos it takes immediately.
Run real-time face recognition to detect facial features or expressions.
Livestream the camera on to the internet.
Detect if the user is on their phone alone, or watching together with a second person.
Upload random frames of the video stream to your web service and run a proper face recognition software which can find existing photos of you on the internet and create a 3D model based on your face.
For instance, here’s a Find my Phone application which a documentary maker installed on a phone, then let someone steal it. After the person stole it, the original owner spied on every moment of the thief’s life through the phone’s camera and microphone.
The government
Edward Snowden revealed an NSA program called Optic Nerves. The operation was a bulk surveillance program under which they captured webcam images every five minutes from Yahoo users’ video chats and then stored them for future use. It is estimated that between 3% and 11% of the images captured contained “undesirable nudity”.
Hackers can also gain access to your device with extraordinary ease via apps, PDF files, multimedia messages and even emojis.
An application called Metasploit on the ethical hacking platform Kali uses an Adobe Reader 9 (which over 60% of users still use) exploit to open a listener (rootkit) on the user’s computer. You alter the PDF with the program, send the user the malicious file, they open it, and hey presto – you have total control over their device remotely.
Once a user opens this PDF file, the hacker can then:
Install whatever software/app they like on the user’s device.
Use a keylogger to grab all of their passwords.
Steal all documents from the device.
Take pictures and stream videos from their camera.
Capture past or live audio from the microphone.
Upload incriminating images/documents to their PC, and notify the police.
And, if it’s not enough that your phone is tracking you – surveillance cameras in shops and streets are tracking you, too
You might even be on this website, InSeCam, which allows ordinary people online to watch surveillance cameras free of charge. It even allows you to search cameras by location, city, time zone, device manufacturer, and specify whether you want to see a kitchen, bar, restaurant or bedroom.
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
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.
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.
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Title:
Learning to Harness Big Data in an Academic Library
Abstract (200)
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
Method
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Sampling design
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Project Schedule
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
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