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Embedded Librarian and Gamification in Libraries

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Open Discussion: Embedded Librarian and Gamification in Libraries

by invitation of New Bulgarian University, Sofia, Bulgaria: https://www.nbu.bg/en
May 14, 9-11AM, New Bulgarian University.

short link: http://bit.ly/embed18

Live stream: https://www.facebook.com/InforMediaServices/ and recording available (предаване на живо и запис)

 

 qr code NBU

 

 

 

Live stream:
https://www.facebook.com/InforMediaServices/
and recording available
(предаване на живо и запис)

backchanneling: @scsutechinstruct ##NBUembed

Archived Discussion
https://www.facebook.com/InforMediaServices/videos/1532459913531167/

Video 360 excerpt from the discussion:

Семинар „Embedded“ библиотекари и геймификация в библиотеките:
Съвременни американски практики“, 14 май 2018 г., 9.00 ч.-11.00 ч.,

Embedded Librarian and Gamification in Libraries from Plamen Miltenoff

Preliminary Information and Literature. Please do not hesitate to share in the comments section your ideas, suggestions and questions
предварителна информация и литература по дискусията. Не се колебайте да споделите мнения, препоръки и въпроси в “Comment” секцията:

https://blog.stcloudstate.edu/ims/2017/10/03/embedded-librarianship-in-online-courses/

https://blog.stcloudstate.edu/ims/2017/08/24/embedded-librarian-qualifications/

https://blog.stcloudstate.edu/ims/2015/05/04/lms-and-embedded-librarianship/

“Embedded librarianship” also mentioned in:

https://blog.stcloudstate.edu/ims/2015/05/27/handbook-of-mobile-learning/

https://blog.stcloudstate.edu/ims/2016/08/18/digital-humanities-and-libraries/

Gaming and Gamification and Education:

https://blog.stcloudstate.edu/ims/2018/04/18/engage-with-dungeons-and-dragons/

https://blog.stcloudstate.edu/ims?s=iste+standards

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For more information and for backchanneling please use the following social media
за повече въпроси и информация, както и за споделяне на вашите идеи и мисли използвайте следните канали / социални медии:

Facebook:

Twitter:

https://twitter.com/SCSUtechinstruc/status/984437858244145152

LinkedIn discussion on VR/AR
https://www.linkedin.com/groups/2811/2811-6391674579739303939

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even more info

The embedded librarian from doberhelman

The Embedded Librarian: Using Technology in Service Delivery from Pavlinka Kovatcheva

Embedded Librarian-ALA 2011 from Info_Witch

Toward a Sustainable Embedded Librarian Program from Robin M. Ashford, MSLIS

The Embedded Librarian: Integrating Library Resources into Course Management Systems from Emily Daly

Embedded Librarian in Higher Education from Shahril Effendi

Ilago 2016 presentation: Next Steps in Embedded Librarian Instructional Design from Dawn Lowe-Wincentsen





BUT WAIT

how does embedded librarian relates to the emerging technologies in the library?

Emerging Technology Trends in Libraries for 2018 from David King

publish metrics ranking and citation info

EdTech Research – Where to Publish, How to Share (Part 2): Journal Metrics, Rankings and Citation Information

EdTech Research – Where to Publish, How to Share (Part 1): Journal Overview

electronic journals

International Review of Research in Open and Distributed Learning (IRRODL)

Publisher / Organization: Athabasca University Press

Year founded: 2000

Description: The International Review of Research in Open and Distributed Learning disseminates original research, theory, and best practice in open and distributed learning worldwide.

First Monday

Publisher / Organization: The University of Illinois at Chicago- University Library

Year founded: 1996

Description: First Monday is among the very first open access journals in the EdTech field. The journal’s subject matter encompasses the full range of Internet issues, including educational technologies, social media and web search. Contributors are urged via author guidelines to use simple explanations and less complex sentences and to be mindful that a large proportion of their readers are not part of academia and do not have English as a first language.

URL: http://firstmonday.org/

International Journal of Educational Technology in Higher Education(ETHE)

Publisher / Organization: Springer (from 2013)

Academic Management: University of Catalonia (UOC)

Year founded: 2004

Description: This journal aims to: provide a vehicle for scholarly presentation and exchange of information between professionals, researchers and practitioners in the technology-enhanced education field; contribute to the advancement of scientific knowledge regarding the use of technology and computers in higher education; and inform readers about the latest developments in the application of information technologies (ITs) in higher education learning, training, research and management.

URL: https://educationaltechnologyjournal.springeropen.com/

Online Learning (formerly JOLT / JALN)

Publisher / Organization: Online Learning Consortium

Year founded: 1997

Description: Online Learning promotes the development and dissemination of new knowledge at the intersection of pedagogy, emerging technology, policy, and practice in online environments. The journal has been published for over 20 years as the Journal of Asynchronous Learning Networks (JALN) and recently merged with the Journal of Online Learning and Teaching (JOLT).

URL: https://olj.onlinelearningconsortium.org/

Journal of Educational Technology & Society

Publisher / Organization: International Forum of Educational Technology & Society

Year founded:1998

Description: Educational Technology & Society seeks academic articles on the issues affecting the developers of educational systems and educators who implement and manage these systems. Articles should discuss the perspectives of both communities – the programmers and the instructors. The journal is currently still accepting submissions for ongoing special issues, but will cease publication in the future as the editors feel that the field of EdTech is saturated with high quality publications.

URL: http://www.ds.unipi.gr/et&s/index.php

Australasian Journal of Educational Technology

Publisher / Organization: Ascilite (Organization) & PKP Publishing Services Network

Year founded: 1985

Description: The Australasian Journal of Educational Technology aims to promote research and scholarship on the integration of technology in tertiary education, promote effective practice, and inform policy. The goal is to advance understanding of educational technology in post-school education settings, including higher and further education, lifelong learning, and training.

URL: https://ajet.org.au/index.php/AJET

Print Journals

The Internet and Higher Education

Publisher / Organization: Elsevier Ltd.

YEAR FOUNDED: 1998

DESCRIPTION: The Internet and Higher Education is devoted to addressing contemporary issues and future developments related to online learning, teaching, and administration on the Internet in post-secondary settings. Articles should significantly address innovative deployments of Internet technology in instruction and report on research to demonstrate the effects of information technology on instruction in various contexts in higher education.

URL: https://www.journals.elsevier.com/the-internet-and-higher-education

British Journal of Educational Technology

Publisher / Organization: British Educational Research Association (BERA)

YEAR FOUNDED: 1970

DESCRIPTION: The journal publishes theoretical perspectives, methodological developments and empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.

LINK: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8535

Computers & Education

Publisher / Organization: Elsevier Ltd.

Year founded: 1976

Description: Computers & Education aims to increase knowledge and understanding of ways in which digital technology can enhance education, through the publication of high quality research, which extends theory and practice.

URL: https://www.journals.elsevier.com/computers-and-education/

Tech Trends

Publisher / Organization: Springer US

Year founded: 1985

Description: TechTrends targets professionals in the educational communication and technology field. It provides a vehicle that fosters the exchange of important and current information among professional practitioners. Among the topics addressed are the management of media and programs, the application of educational technology principles and techniques to instructional programs, and corporate and military training.

URL: https://link.springer.com/journal/11528

International Journal on E-Learning (IJEL)

Year founded: 2002

Description: Advances in technology and the growth of e-learning to provide educators and trainers with unique opportunities to enhance learning and teaching in corporate, government, healthcare, and higher education. IJEL serves as a forum to facilitate the international exchange of information on the current research, development, and practice of e-learning in these sectors.

Led by an Editorial Review Board of leaders in the field of e-Learning, the Journal is designed for the following audiences: researchers, developers, and practitioners in corporate, government, healthcare, and higher education. IJEL is a peer-reviewed journal.

URL: http://www.aace.org/pubs/ijel/

Journal of Computers in Mathematics and Science Teaching (JCMST)

Year founded: 1981

Description: JCMST is a highly respected scholarly journal which offers an in-depth forum for the interchange of information in the fields of science, mathematics, and computer science. JCMST is the only periodical devoted specifically to using information technology in the teaching of mathematics and science.

URL: https://www.aace.org/pubs/jcmst/

Just as researchers build reputation over time that can be depicted (in part) through quantitative measures such as h-index and i10-index, journals are also compared based on the number of citations they receive..

Journal of Interactive Learning Research (JILR)

Year founded: 1997

Description: The Journal of Interactive Learning Research (JILR) publishes papers related to the underlying theory, design, implementation, effectiveness, and impact on education and training of the following interactive learning environments: authoring systems, cognitive tools for learning computer-assisted language learning computer-based assessment systems, computer-based training computer-mediated communications, computer-supported collaborative learning distributed learning environments, electronic performance support systems interactive learning environments, interactive multimedia systems interactive simulations and games, intelligent agents on the Internet intelligent tutoring systems, microworlds, virtual reality based learning systems.

URL: http://learntechlib.org/j/JILR/

Journal of Educational Multimedia and Hypermedia (JEMH)

Year founded: 1996

Description: JEMH is designed to provide a multi-disciplinary forum to present and discuss research, development and applications of multimedia and hypermedia in education. It contributes to the advancement of the theory and practice of learning and teaching in environments that integrate images, sound, text, and data.

URL: https://www.aace.org/pubs/jemh/

Journal of Technology and Teacher Education (JTATE)

Publisher / Organization: Society for Information Technology and Teacher Education (SITE)

Year founded: 1997

Description: JTATE serves as a forum for the exchange of knowledge about the use of information technology in teacher education. Journal content covers preservice and inservice teacher education, graduate programs in areas such as curriculum and instruction, educational administration, staff development instructional technology, and educational computing.

URL: https://www.aace.org/pubs/jtate/

Journal on Online Learning Research (JOLR)

Publisher / Organization: Association for the Advancement of Computing in Education (AACE)

YEAR FOUNDED: 2015

DESCRIPTION: The Journal of Online Learning Research (JOLR) is a peer-reviewed, international journal devoted to the theoretical, empirical, and pragmatic understanding of technologies and their impact on primary and secondary pedagogy and policy in primary and secondary (K-12) online and blended environments. JOLR is focused on publishing manuscripts that address online learning, catering particularly to the educators who research, practice, design, and/or administer in primary and secondary schooling in online settings. However, the journal also serves those educators who have chosen to blend online learning tools and strategies in their face-to-face classroom.

URL: https://www.aace.org/pubs/jolr/

 

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part 2

The most commonly used index to measure the relative importance of journals is the annual Journal Citation Reports (JCR). This report is published by Clarivate Analytics (previously Thomson Reuters).

SCImago

SCImago Journal Rank (SJR indicator) measures the influence of journals based on the number of citations the articles in the journal receive and the importance or prestige of the journals where such citations come from. The SJR indicator is a free journal metric which uses an algorithm similar to PageRank and provides an open access alternative to the journal impact factor in the Web of Science Journal Citation Report. The portal draws from the information contained in the Scopus database (Elsevier B.V.).

Google Scholar Journal Rank

Introduced by Google in 2004, Scholar is a freely accessible search engine that indexes the full text or metadata of scholarly publications across an array of publishing formats and disciplines.

Scopus Journal Metrics

Introduced by Elsevier in 2004, Scopus is an abstract and citation database that covers nearly 18,000 titles from more than 5,000 publishers. It offers journal metrics that go beyond just journals to include most serial titles, including supplements, special issues and conference proceedings. Scopus offers useful information such as the total number of citations, the total number of articles published, and the percent of articles cited.

Anne-Wil Harzing:

Citations are not just a reflection of the impact that a particular piece of academic work has generated. Citations can be used to tell stories about academics, journals and fields of research, but they can also be used to distort stories”.

Harzing, A.-W. (2013). The publish or perish book: Your guide to effective and responsible citation analysis. http://harzing.com/popbook/index.htm

ResearchGate

ResearchGate is a social networking site for scientists and researchers to share papers, ask and answer questions, and find collaborators. The community was founded in May 2008. Today it has over 14 million members.

Google Scholar

Google Scholar allows users to search for digital or physical copies of articles, whether online or in libraries. It indexes “full-text journal articles, technical reports, preprints, theses, books, and other documents, including selected Web pages that are deemed to be ‘scholarly. It comprises an estimated 160 million documents.

Academia.edu

Academia.edu is a social-networking platform for academics to share research papers. You can upload your own work, and follow the updates of your peers. Founded in 2008, the network currently has 59 million users, and adding 20 million documents.

ORCID

The ORCHID (Open Researcher and Contributor ID) is a nonproprietary alphanumeric code to uniquely identify scientific and other academic authors and contributors. It provides a persistent identity for humans, similar to content-related entities on digital networks that utilize digital object identifiers (DOIs). The organization offers an open and independent registry intended to be the de facto standard for contributor identification in research and academic publishing.

SCOPUS

The Scopus Author Identifier assigns a unique number to groups of documents written by the same author via an algorithm that matches authorship based on a certain criteria. If a document cannot be confidently matched with an author identifier, it is grouped separately. In this case, you may see more than one entry for the same author.

 

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more on metrics in this iMS blog

https://blog.stcloudstate.edu/ims?s=metrics

Selecting LMS

A Guide to Picking a Learning Management System: The Right Questions to Ask

By Mary Jo Madda (Columnist)     Feb 14, 2017

https://www.edsurge.com/news/2017-02-14-a-guide-to-learning-management-systems-the-right-questions-to-ask

Over the past 10 years, new learning management systems (LMSs) have sprung on the scene to rival the Blackboards and Moodles of old. On the EdSurge Product Index alone, 56 products self-identify and fall into the LMS category. And with certain established companies like Pearson pulling out of the LMS ranks, where do you start?

As University of Central Florida’s Associate Vice President of Distributed Learning, Tom Cavanagh, wrote in an article for EDUCAUSE, “every institute has a unique set of instructional and infrastructure circumstances to consider when deciding on an LMS,” but at the same time, “all institutions face certain common requirements”—whether a small charter school, a private university or a large public school district.

The LMS Checklist

#1: Is the platform straightforward and user-friendly?

#2: Who do we want to have access to this platform, and can we adjust what they can see?

#3: Can the instructor and student(s) talk to and communicate with each other easily?

“Students and faculty live a significant portion of their daily lives online in social media spaces,” writes University of Central Florida’s Tom Cavanagh in his article on the LMS selection process. “Are your students and faculty interested in these sorts of interplatform connections?”

#5: Does this platform plug in with all of the other platforms we have?

“Given the pace of change and the plethora of options with educational technology, it’s very difficult for any LMS vendor to keep up with stand-alone tools that will always outperform built-in tools,” explains Michael Truong, executive director of innovative teaching and technology at Azusa Pacific University. According to Truong, “no LMS will be able to compete directly with tools like Piazza (discussion forum), Socrative (quizzing), EdPuzzle (video annotation), etc.” 

As a result, Truong says, “The best way to ‘prepare’ for future technological changes is to go with an LMS that plays well with external tools.

#6: Is the price worth the product?

A reality check: There is no perfect LMS.

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

ECAR Study of Undergraduate Students and Information Technology, 2017

ECAR Study of Undergraduate Students and Information Technology, 2017

  • Students would like their instructors to use more technology in their classes.Technologies that provide students with something (e.g., lecture capture, early-alert systems, LMS, search tools) are more desired than those that require students to give something (e.g., social media, use of their own devices, in-class polling tools). We speculate that sound pedagogy and technology use tied to specific learning outcomes and goals may improve the desirability of the latter.
  • Students reported that faculty are banning or discouraging the use of laptops, tablets, and (especially) smartphones more often than in previous years. Some students reported using their devices (especially their smartphones) for nonclass activities, which might explain the instructor policies they are experiencing. However, they also reported using their devices for productive classroom activities (e.g., taking notes, researching additional sources of information, and instructor-directed activities).

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

PALS at CATT

Campus Academic Technology Teams Webinar:

Online Education Report:

https://mnscu.sharepoint.com/sites/SO-UG-Educational-Innovations/Shared%20Documents/CATTs/2017-11-28/Advancing%20Online%20Education%20-%20Full%20Report.pdf?slrid=9d6b319e-e02a-4000-c1b7-12461657a5be

PALS: Enhancing Library System Solutions

PALS is housed in Mankato, 40+ years, shared by all MnSCU institutions. smaller libraries with smaller staff benefit.

Funding: Centrally from the Chancellor Office and privately.

Ex Libris. Alma (management software) discovery software is Primo. Implementation from Sept 2017 to 2019

value-added services?  A valueadded service (VAS) is a popular telecommunications industry term for non-coreservices, or, in short, all services beyond standard voice calls and fax transmissions. However, it can be used in any service industry, for services available at little or no cost, to promote their primary business.

Value-added service – Wikipedia

The new library system: backroom processing: – acquisitions – resources management (phys + electr) – analytics / reports /APIs
fulfillment : circulation and ILL
Discovery (Primo)
– phys + electr
– institution, consortium, remote resources
advantanges:
Hosted apps
web-based staff interface (until now on Windows)
all in one vs four separate apps – staff efficiency, common services, student success?
electronic resource management
Electronic resource management (ERM) is the practices and techniques used by librarians and library staff to track the selection, acquisition, licensing, access, maintenance, usage, evaluation, retention, and de-selection of a library’s electronic information resources. These resources include, but are not limited to, electronic journalselectronic booksstreaming mediadatabasesdatasetsCD-ROMs, and computer softwarehttps://en.wikipedia.org/wiki/Electronic_resource_management
Primo – comprehensive discovery
one search point; phys + electr; integrated into central system; academic resources available in central index; analytics and reporting; library consortia
EZ Proxy – provides access to library resources off campus
Islandora – open source digital asset management solution tha preserves, manages, and provide access to docs, unique history (photos, publications); research, other resources
Islandora is considered for OER, link to course materials through D2L
Leganto – expensive ExLibris for D2L integration
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Thurs, Nov 30 – continuation from Tues, Nov 28
Islandora. open source digital assessment tool. STCC is using Islandora
Primo is the discovery tool for campus only w subscription. PALS does not fund Primo. PALS does it through state-wide dbases.
ILL of electronic resources among campuses; the new system is making it easier.
your comments about the new system making electronic resources more available : does it mean that I will not have to go through my campus ILL persona can “borrow” directly? or it is too optimistic to expect that?
 Stephen Kelly: Tim Anderson has shared with me some thoughts on how Islandora can assist with archiving Open Educational Resources (OERs), but could you comment further on that for the benefit of everyone on the call? Answer: safe place to save OER. Drupal-based front end. Customizable. What is the connection to Primo
Stephen Kelly: Could it facilitate easier sharing of resources between institutions? For instance, if an OER was created at one institution and uploaded to Islandora, could it easily be populated for every other institution to access the materials as well?
Piggybacking on Stephen Kelly: are the account permissions similar to the average social media tool, where faculty can decide how “wide” the permission of h/er OER product is? E.g. a blog or YouTube / Kaltura can have: private / unlisted / public levels. Does Islandora function the same?
ownership of the OER.
copyright can be placed on each screen.

International Conference on Learning Athens Greece

Twenty-fifth International Conference on Learning

2018 Special Focus: Education in a Time of Austerity and Social Turbulence  21–23 June 2018 University of Athens, Athens, Greece http://thelearner.com/2018-conference

Theme 8: Technologies in Learning

  • Technology and human values: learning through and about technology
  • Crossing the digital divide: access to learning in, and about, the digital world
  • New tools for learning: online digitally mediated learning
  • Virtual worlds, virtual classrooms: interactive, self-paced and autonomous learning
  • Ubiquitous learning: using the affordances of the new mediaDistance learning: reducing the distance

Theme 9: Literacies Learning

  • Defining new literacies
  • Languages of power: literacy’s role in social access
  • Instructional responses to individual differences in literacy learning
  • The visual and the verbal: Multiliteracies and multimodal communications
  • Literacy in learning: language in learning across the subject areas
  • The changing role of libraries in literacies learning
  • Languages education and second language learning
  • Multilingual learning for a multicultural world
  • The arts and design in multimodal learning
  • The computer, internet, and digital media: educational challenges and responses

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PROPOSAL: Paper presentation in a Themed Session

Title

Virtual Reality and Gamification in the Educational Process: The Experience from an Academic Library

short description

VR, AR and Mixed Reality, as well as gaming and gamification are proposed as sandbox opportunity to transition from a lecture-type instruction to constructivist-based methods.

long description

The NMC New Horizon Report 2017 predicts a rapid application of Video360 in K12. Millennials are leaving college, Gen Z students are our next patrons. Higher Education needs to meet its new students on “their playground.” A collaboration by a librarian and VR specialist is testing the opportunities to apply 360 degree movies and VR in academic library orientation. The team seeks to bank on the inheriting interest of young patrons toward these technologies and their inextricable part of a rapidly becoming traditional gaming environment. A “low-end,” inexpensive and more mobile Google Cardboard solution was preferred to HTC Vive, Microsoft HoloLens or comparable hi-end VR, AR and mixed reality products.

The team relies on the constructivist theory of assisting students in building their knowledge in their own pace and on their own terms, rather than being lectured and/or being guided by a librarian during a traditional library orientation tour. Using inexpensive Google Cardboard goggles, students can explore a realistic set up of the actual library and familiarize themselves with its services. Students were polled on the effectiveness of such approach as well as on their inclination to entertain more comprehensive version of library orientation. Based on the lessons from this experiment, the team intends to pursue also a standardized approach to introducing VR to other campus services, thus bringing down further the cost of VR projects on campus. The project is considered a sandbox for academic instruction across campus. The same concept can be applied for [e.g., Chemistry, Physics, Biology) lab tours; for classes, which anticipate preliminary orientation process.

Following the VR orientation, the traditional students’ library instruction, usually conducted in a room, is replaced by a dynamic gamified library instruction. Students are split in groups of three and conduct a “scavenger hunt”; students use a jQuery-generated Web site on their mobile devices to advance through “hoops” of standard information literacy test. E.g., they need to walk to the Reference Desk, collect specific information and log their findings in the Web site. The idea follows the strong interest in the educational world toward gaming and gamification of the educational process. This library orientation approach applies the three principles for gamification: empowers learners; teaches problem solving and increases understanding.
Similarly to the experience with VR for library orientation, this library instruction process is used as a sandbox and has been successfully replicated by other instructors in their classes.

Keywords

academic library

literacies learning

digitally mediated learning

 

digital humanities

7 Things You Should Know About Digital Humanities

Published:   Briefs, Case Studies, Papers, Reports  

https://library.educause.edu/resources/2017/11/7-things-you-should-know-about-digital-humanities

Lippincott, J., Spiro, L., Rugg, A., Sipher, J., & Well, C. (2017). Seven Things You Should Know About Digital Humanities (ELI 7 Things You Should Know). Retrieved from https://library.educause.edu/~/media/files/library/2017/11/eli7150.pdf

definition

The term “digital humanities” can refer to research and instruction that is about information technology or that uses IT. By applying technologies in new ways, the tools and methodologies of digital humanities open new avenues of inquiry and scholarly production. Digital humanities applies computational capabilities to humanistic questions, offering new pathways for scholars to conduct research and to create and publish scholarship. Digital humanities provides promising new channels for learners and will continue to influence the ways in which we think about and evolve technology toward better and more humanistic ends.

As defined by Johanna Drucker and colleagues at UCLA, the digital humanities is “work at the intersection of digital technology and humanities disciplines.” An EDUCAUSE/CNI working group framed the digital humanities as “the application and/or development of digital tools and resources to enable researchers to address questions and perform new types of analyses in the humanities disciplines,” and the NEH Office of Digital Humanities says digital humanities “explore how to harness new technology for thumanities research as well as those that study digital culture from a humanistic perspective.” Beyond blending the digital with the humanities, there is an intentionality about combining the two that defines it.

digital humanities can include

  • creating digital texts or data sets;
  • cleaning, organizing, and tagging those data sets;
  • applying computer-based methodologies to analyze them;
  • and making claims and creating visualizations that explain new findings from those analyses.

Scholars might reflect on

  • how the digital form of the data is organized,
  • how analysis is conducted/reproduced, and
  • how claims visualized in digital form may embody assumptions or biases.

Digital humanities can enrich pedagogy as well, such as when a student uses visualized data to study voter patterns or conducts data-driven analyses of works of literature.

Digital humanities usually involves work by teams in collaborative spaces or centers. Team members might include

  • researchers and faculty from multiple disciplines,
  • graduate students,
  • librarians,
  • instructional technologists,
  • data scientists and preservation experts,
  • technologists with expertise in critical computing and computing methods, and undergraduates

projects:

downsides

  • some disciplinary associations, including the Modern Language Association and the American Historical Association, have developed guidelines for evaluating digital proj- ects, many institutions have yet to define how work in digital humanities fits into considerations for tenure and promotion
  • Because large projects are often developed with external funding that is not readily replaced by institutional funds when the grant ends sustainability is a concern. Doing digital humanities well requires access to expertise in methodologies and tools such as GIS, mod- eling, programming, and data visualization that can be expensive for a single institution to obtain
  • Resistance to learning new tech- nologies can be another roadblock, as can the propensity of many humanists to resist working in teams. While some institutions have recognized the need for institutional infrastructure (computation and storage, equipment, software, and expertise), many have not yet incorporated such support into ongoing budgets.

Opportunities for undergraduate involvement in research, provid ing students with workplace skills such as data management, visualization, coding, and modeling. Digital humanities provides new insights into policy-making in areas such as social media, demo- graphics, and new means of engaging with popular culture and understanding past cultures. Evolution in this area will continue to build connections between the humanities and other disci- plines, cross-pollinating research and education in areas like med- icine and environmental studies. Insights about digital humanities itself will drive innovation in pedagogy and expand our conceptualization of classrooms and labs

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

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 (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).

 

This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.

Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.

 

 

References:

 

Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.

Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.

Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125

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





online learning attitudes

online learning attitudes

Students match their preference for hybrid learning with a belief that it is the most effective learning environment for them.

Despite the fact that faculty prefer teaching in a hybrid environment, they remain skeptical of online learning. Nearly half do not agree online 45% learning is effective.

https://library.educause.edu/~/media/files/library/2017/9/studentst2017infog.pdf

 

Students asked what technologies they wish their instructors used more, and we asked faculty what technologies they think could make them more effective instructors. Both agree that content and resource-focused technologies should be incorporated more and social media and tablets should be incorporated less.

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more on the use (or not) of ed technology in the classroom in this IMS blog
https://blog.stcloudstate.edu/ims/2017/04/03/use-of-laptops-in-the-classroom/

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