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NMC Horizon Report 2017 K12

NMC/CoSN Horizon Report 2017 K–12 Edition

https://cdn.nmc.org/wp-content/uploads/2017-nmc-cosn-horizon-report-K12-advance.pdf
p. 16 Growing Focus on Measuring Learning
p. 18 Redesigning Learning Spaces
Biophilic Design for Schools : The innate tendency in human beings to focus on life and lifelike processes is biophilia

p. 20 Coding as a Literacy

 https://www.facebook.com/bracekids/
Best Coding Tools for High School http://go.nmc.org/bestco

p. 24

Significant Challenges Impeding Technology Adoption in K–12 Education
Improving Digital Literacy.
 Schools are charged with developing students’ digital citizenship, ensuring mastery of responsible and appropriate technology use, including online etiquette and digital rights and responsibilities in blended and online learning settings. Due to the multitude of elements comprising digital literacy, it is a challenge for schools to implement a comprehensive and cohesive approach to embedding it in curricula.
Rethinking the Roles of Teachers.
Pre-service teacher training programs are also challenged to equip educators with digital and social–emotional competencies, such as the ability to analyze and use student data, amid other professional requirements to ensure classroom readiness.
p. 28 Improving Digital Literacy
Digital literacy spans across subjects and grades, taking a school-wide effort to embed it in curricula. This can ensure that students are empowered to adapt in a quickly changing world
Education Overview: Digital Literacy Has to Encompass More Than Social Use

What Web Literacy Skills are Missing from Learning Standards? Are current learning standards addressing the essential web literacy skills everyone should know?https://medium.com/read-write-participate/what-essential-web-skills-are-missing-from-current-learning-standards-66e1b6e99c72

 

web literacy;
alignment of stadards

The American Library Association (ALA) defines digital literacy as “the ability to use information and communication technologies to find, evaluate, create, and communicate or share information, requiring both cognitive and technical skills.” While the ALA’s definition does align to some of the skills in “Participate”, it does not specifically mention the skills related to the “Open Practice.”

The library community’s digital and information literacy standards do not specifically include the coding, revision and remixing of digital content as skills required for creating digital information. Most digital content created for the web is “dynamic,” rather than fixed, and coding and remixing skills are needed to create new content and refresh or repurpose existing content. Leaving out these critical skills ignores the fact that library professionals need to be able to build and contribute online content to the ever-changing Internet.

p. 30 Rethinking the Roles of Teachers

Teachers implementing new games and software learn alongside students, which requires
a degree of risk on the teacher’s part as they try new methods and learn what works
p. 32 Teaching Computational Thinking
p. 36 Sustaining Innovation through Leadership Changes
shift the role of teachers from depositors of knowledge to mentors working alongside students;
p. 38  Important Developments in Educational Technology for K–12 Education
Consumer technologies are tools created for recreational and professional purposes and were not designed, at least initially, for educational use — though they may serve well as learning aids and be quite adaptable for use in schools.
Drones > Real-Time Communication Tools > Robotics > Wearable Technology
Digital strategies are not so much technologies as they are ways of using devices and software to enrich teaching and learning, whether inside or outside the classroom.
> Games and Gamification > Location Intelligence > Makerspaces > Preservation and Conservation Technologies
Enabling technologies are those technologies that have the potential to transform what we expect of our devices and tools. The link to learning in this category is less easy to make, but this group of technologies is where substantive technological innovation begins to be visible. Enabling technologies expand the reach of our tools, making them more capable and useful
Affective Computing > Analytics Technologies > Artificial Intelligence > Dynamic Spectrum and TV White Spaces > Electrovibration > Flexible Displays > Mesh Networks > Mobile Broadband > Natural User Interfaces > Near Field Communication > Next Generation Batteries > Open Hardware > Software-Defined Networking > Speech-to-Speech Translation > Virtual Assistants > Wireless Powe
Internet technologies include techniques and essential infrastructure that help to make the technologies underlying how we interact with the network more transparent, less obtrusive, and easier to use.
Bibliometrics and Citation Technologies > Blockchain > Digital Scholarship Technologies > Internet of Things > Syndication Tools
Learning technologies include both tools and resources developed expressly for the education sector, as well as pathways of development that may include tools adapted from other purposes that are matched with strategies to make them useful for learning.
Adaptive Learning Technologies > Microlearning Technologies > Mobile Learning > Online Learning > Virtual and Remote Laboratories
Social media technologies could have been subsumed under the consumer technology category, but they have become so ever-present and so widely used in every part of society that they have been elevated to their own category.
Crowdsourcing > Online Identity > Social Networks > Virtual Worlds
Visualization technologies run the gamut from simple infographics to complex forms of visual data analysis
3D Printing > GIS/Mapping > Information Visualization > Mixed Reality > Virtual Reality
p. 46 Virtual Reality
p. 48 AI
p. 50 IoT

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more on NMC Horizon Reports in this IMS blog

http://blog.stcloudstate.edu/ims?s=new+media+horizon

NMC Horizon Report 2017 Library

NMC Horizon Report > 2017 Library Edition

http://www.nmc.org/publication/nmc-horizon-report-2017-library-edition/

PDF file 2017-nmc-horizon-report-library-EN-20ml00b

p. 26 Improving Digital Literacy

As social networking platforms proliferate and more interactions take place digitally, there are more opportunities for propagation of misinformation, copyright infringement, and privacy breaches.
http://blog.stcloudstate.edu/ims/2017/03/28/fake-news-3/
http://blog.stcloudstate.edu/ims/2017/03/28/fake-news-resources/

p. 34 Embracing the need for radical change

40% of faculty report that their students ” rarely” interact with campus librarians.

Empathy as the Leader’s Path to Change | Leading From the Library, By on October 27, 2016, http://lj.libraryjournal.com/2016/10/opinion/leading-from-the-library/empathy-as-the-leaders-path-to-change-leading-from-the-library/

Empathy as a critical quality for leaders was popularized in Daniel Goleman’s work about emotional intelligence. It is also a core component of Karol Wasylyshyn’s formula for achieving remarkable leadership. Elizabeth Borges, a women’s leadership program organizer and leadership consultant, recommends a particular practice, cognitive empathy.

Leadership in disruptive times, , First Published September 27, 2016, http://journals.sagepub.com/doi/full/10.1177/0340035216658911

What is library leadership?  a library leader is defined as the individual who articulates a vision for the organization/task and is able to inspire support and action to achieve the vision. A manager, on the other hand, is the individual tasked with organizing and carrying out the day-to-day operational activities to achieve the vision.Work places are organized in hierarchical and in team structures. Managers are appointed to administer business units or organizations whereas leaders may emerge from all levels of the hierarchical structures. Within a volatile climate the need for strong leadership is essential.  

Leaders are developed and educated within the working environment where they act and co-work with their partners and colleagues. Effective leadership complies with the mission and goals of the organization. Several assets distinguish qualitative leadership:

Mentoring. Motivation. Personal development and skills. Inspiration and collaboration. Engagement. Success and failure. Risk taking. Attributes of leaders.

Leaders require having creative minds in shaping strategies and solving problems. They are mentors for the staff, work hard and inspire them to do more with less and to start small and grow big. Staff need to be motivated to work at their optimum performance level. Leadership entails awareness of the responsibilities inherent to the roles of a leader. However, effective leadership requires the support of the upper management.

p. 36. Developments in Technology for Academic and Research Libraries

http://horizon.wiki.nmc.org/Horizon+Topics

  1. consumer technologies
  2. Digital strategies are not so much technologies as they are ways of using devices and software to enrich teaching, learning, research and information management, whether inside or outside the library. Effective Digital strategies can be used in both information and formal learning; what makes them interesting is that they transcended conventional ideas to create something that feels new, meaningful, and 21st century.
  3. enabling technologies
    this group of technologies is where substantive technological innovation begins to be visible.
  4. Internet technologies.
  5. learning technologies
  6. social media technologies. could have been subsumed under the consumer technology category, but they have become so ever-present and so widely used in every part of society that they have been elevated to their own category. As well-established as social media is, it continues to evolve at a rapid pace, with new ideas, tools, and developments coming online constantly.
  7. Visualization technologies.  from simple infographics to complex forms of visual data analysis. What they have in common is that they tap the brain’s inherent ability to rapidly process visual information, identify patterns, and sense order in complex situations. These technologies are a growing cluster of tools and processes for mining large data sets, exploring dynamic processes, and generally making the complex simple.

new horizon report 2017 technologies

 

 

p. 38 Big Data
Big data has significant implications for academic libraries in their roles as facilitators and supporters of the research process. big data use in the form of digital humanities research. Libraries are increasingly seeking to recruit for positions such as research data librarians, data curation specialists, or data visualization specialists

p. 40  Digital Scholarship Technologies

digital humanities scholars are leveraging new tools to aid in their work. ubiquity of new forms of communication including social media, text analysis software such as Umigon is helping researchers gauge public sentiment. The tool aggregates and classifies tweets as negative, positive, or neutral.

p. 42 Library Services Platforms

Diversity of format and materials, in turn, required new approaches to content collection and curation that were unavailable in the incumbent integrated library systems (ILS), which are primarily designed for print materials. LSP is different from ILS in numerous ways. Conceptually, LSPs are modeled on the idea of software as a service (SaaS),which entails delivering software applications over the internet.

p. 44 Online Identity.
incorporated  the  management of digital footprints into their programming and resources

simplify the idea of digital footprint as“data about the data” that people are searching or using online. As resident champions for advancing digital literacy,304 academic and research libraries are well-positioned to guide the process of understanding and crafting online identities.

Libraries are becoming integral players in helping students understand how to create and manage their online identities. website includes a social media skills portal that enables students to view their digital presence through the lens in which others see them, and then learn how they compare to their peers.

p. 46  Artificial Intelligence

https://www.semanticscholar.org/

p. 48 IoT

beacons are another iteration of the IoT that libraries have adopted; these small wireless devices transmit a small package of data continuously so that when devices come into proximity of the beacon’s transmission, functions are  triggered based on a related application.340 Aruba Bluetooth low-energy beacons to link digital resources to physical locations, guiding patrons to these resources through their custom navigation app and augmenting the user experience with location-based information, tutorials, and videos.

students and their computer science  professor  have  partnered  with   Bavaria’s State Library to develop a library app that triggers supplementary information about its art collection or other points of interest as users explore the space

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more on Horizon Reports in this IMS blog
http://blog.stcloudstate.edu/ims?s=horizon+report

Horizon Report 2015

2015-nmc-horizon-report-HE-EN

Horizon Report > 2015 Higher Education Edition

Key Trends Accelerating Technology Adoption in Higher Education 6
Long-Term Trends: Driving Ed Tech adoption in higher education for five or more years
> Advancing Cultures of Change and Innovation 8
> Increasing Cross-Institution Collaboration 10
Mid-Term Trends: Driving Ed Tech adoption in higher education for three to five years
> Growing Focus on Measuring Learning 12
> Proliferation of Open Educational Resources 14
Short-Term Trends: Driving Ed Tech adoption in higher education for the next one to two years
> Increasing Use of Blended Learning 16
> Redesigning Learning Spaces 18
Significant Challenges Impeding Technology Adoption in Higher Education 20
Solvable Challenges: Those that we understand and know how to solve
> Blending Formal and Informal Learning 22
> Improving Digital Literacy 24
Difficult Challenges: Those we understand but for which solutions are elusive
> Personalizing Learning 26
> Teaching Complex Thinking 28
Wicked Challenges: Those that are complex to even define, much less address
> Competing Models of Education 30
> Rewarding Teaching 32
Important Developments in Educational Technology for Higher Education 34

Horizon Report 2014, Library edition

http://cdn.nmc.org/media/2014-nmc-horizon-report-library-EN.pdf

p. 4 new and rapidly changing technologies, an abundance of digital information in myriad formats, an increased understanding of how students learn evolving research methods, and changing practices in how scholars communicate and disseminate their research and creative work.

Engagement requires an outward focus

A liaison who understands how scholars in a particular discipline communicate and share
information with one another can inform the design and development of new publishing services, such as
digital institutional repositories.

Liaisons cannot be experts themselves in each new capability, but knowing when to call in a
colleague, or how to describe appropriate expert capabilities to faculty, will be key to the new liaison role.

an increasing focus on what users do (research, teaching, and learning) rather than on what librarians do (collections, reference, library instruction).

hybrid model, where liaisons pair their expertise with that of functional specialists, both within and outside of libraries

p. 6 Trend 1: Develop user-centered library services

Many libraries are challenged to brand such a service point, citing a “hub” or “center” to refer to services that can include circulation, reference, computer support, writing assistance, and more.

For liaisons, time at a reference desk has been replaced by anticipating recurrent needs and developing
easily accessible online materials (e.g., LibGuides, screencasts) available to anyone at any time, and
by providing more advanced one-on-one consultations with students, instructors, and researchers who
need expert help. Liaisons not only answer questions using library resources, but they also advise and
collaborate on issues of copyright, scholarly communication, data management, knowledge management,
and information literacy. The base level of knowledge that a liaison must possess is much broader than
familiarity with a reference collection or facility with online searching; instead, they must constantly keep up
with evolving pedagogies and research methods, rapidly developing tools, technologies, and ever-changing
policies that facilitate and inform teaching, learning, and research in their assigned disciplines.

Librarians at many institutions are now focusing on collaborating with faculty to develop thoughtful assignments
and provide online instructional materials that are built into key courses within a curriculum and provide
scaffolding to help students develop library research skills over the course of their academic careers

p. 7 Trend 2: A hybrid model of liaison and functional specialist is emerging.

Current specialist areas of expertise include copyright, geographic information systems (GIS), media production and integration, distributed education or e-learning, data management, emerging technologies,
user experience, instructional design, and bioinformatics.

At the University of Guelph, the liaison model was abandoned altogether in favor of a functional specialist
approach

p. 8 Trend 3: Organizational flexibility must meet changing user needs.

p. 9 provide education and consultation services for personal information management. Tools, workshops, websites, and individual consults are offered in areas such as citation management, productivity tools, managing alerts and feeds, personal archiving, and using social networking for teaching and professional development.

p. 11 data management, knowledge management and scholarly communication

digital scholarship

p. 12 Liaisons need to be able to provide a general level of knowledge about copyright, data management, the need for metadata and the ontologies available in their disciplines.

p. 13 Liaisons need to be able to provide a general level of knowledge about copyright, data management, the need for metadata and the ontologies available in their disciplines.

p. 16 replacing the traditional tripartite model of collections, reference, and instruction

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

 

IRDL proposal

Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.

Title:

Learning to Harness Big Data in an Academic Library

Abstract (200)

Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions.
Introduction to the problem. Limitations

The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.

 

 

Research Literature

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

Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.

In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).

The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).

Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).

De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).

The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics.  The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).

Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).

Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?

Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.

Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success.  Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).

Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.

Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).

The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.

Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.

 

Method

 

The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data.  Send survey URL to (academic?) libraries around the world.

Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.

The study will include the use of closed-ended survey response questions and open-ended questions.  The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.

Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17).  Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).

 

Sampling design

 

  • Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
  • 1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
  • data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.

 

Project Schedule

 

Complete literature review and identify areas of interest – two months

Prepare and test instrument (survey) – month

IRB and other details – month

Generate a list of potential libraries to distribute survey – month

Contact libraries. Follow up and contact again, if necessary (low turnaround) – month

Collect, analyze data – two months

Write out data findings – month

Complete manuscript – month

Proofreading and other details – month

 

Significance of the work 

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

 

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

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

 

 

References:

 

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

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

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

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

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

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

Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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more on big data





IM554 discussion on GBL

IM554 discussion on Game Based Learning

Here is the “literature”:
http://blog.stcloudstate.edu/ims/2015/03/19/recommendations-for-games-and-gaming-at-lrs/
this link reflects my recommendations to the SCSU library, based on my research and my publication: http://scsu.mn/1F008Re

Here are also Slideshare shows from conferences’ presentations on the topic:

https://www.slideshare.net/aidemoreto/gamification-and-byox-in-academic-libraries-low-end-practical-approach

https://www.slideshare.net/aidemoreto/gaming-and-gamification-in-academic-and-library-settings

Topic :Gaming and Gamification in Academic Settings

  1. Intro: why is it important to discuss this trend
    1. The fabric of the current K12 and higher ed students: Millennials and Gen Z
    2. The pedagogical theories and namely constructivism
      1. Csikszentmihalyi’s “flow” concept (being in the zone)
      2. Active learning
      3. Sociocultural Theory
      4. Project-Based Learning
    3. The general milieu of increasing technology presence, particularly of gaming environment
    4. The New Media Consortium and the Horizon Report

Discussion: Are the presented reasons sufficient to justify a profound restructure of curricula and learning spaces?

  1. Definition and delineation
    1. Games
    2. Serious Games
    3. Gamification
    4. Game-based learning
    5. Digital game-based learning
    6. Games versus gamification
    7. Simulations, the new technological trends such as human-computer interaction (HCI) such as augmented reality (AR),virtual reality (VR) and mixed reality (MR) (http://blog.stcloudstate.edu/ims/2017/02/22/virtual-augmented-mixed-reality/ )

Discussion: Is there a way to build a simpler but comprehensive structure/definition to encompass the process of gaming and gamification in education?

  1. Gaming and Gamification
    1. Pros
    2. Cons
    3. Debates

Discussion: Which side are you on and why?

  1. Gaming and Gamification and BYOD (or BYOx)
    1. gaming consoles versus gaming over wi-fi
    2. gaming using mobile devices instead of consoles
    3. human-computer interaction (HCI) such as augmented reality (AR),virtual reality (VR) and mixed reality (MR) (http://blog.stcloudstate.edu/ims/2017/02/22/virtual-augmented-mixed-reality/ )

Discussion: do you see a trend to suggest that either one or the other will prevail? Convergence?

  1. Gaming in Education
    1. student motivation, student-centered learning, personalized learning
    2. continued practice, clear goals and immediate feedback
    3. project-based learning, Minecraft and SimCity EDU
    4. Gamification of learning versus learning with games
    5. organizations to promote gaming and gamification in education (p. 6 http://scsu.mn/1F008Re)
    6. the “chocolate-covered broccoli” problem

Discussion: why gaming and gamification is not accepted in a higher rate? what are the hurdles to enable greater faster acceptance? What do you think, you can do to accelerate this process?

  1. Gaming in an academic library
    1. why the academic library? sandbox for experimentation
    2. the connection between digital literacy and gaming and gamificiation
    3. Gilchrist and Zald’s model for instruction design through assessment
    4. the new type of library instruction:
      in house versus out-of-the box games. Gamification of the process
      http://web.stcloudstate.edu/pmiltenoff/bi/

Discussion: based on the example (http://web.stcloudstate.edu/pmiltenoff/bi/), how do you see transforming academic library services to meet the demands of 21st century education?

  1. Gaming, gamification and assessment (badges)
    1. inability of current assessments to evaluate games as part of the learning process
    2. “microcredentialing” through digital badges
    3. Mozilla Open Badges and Badgestack
    4. leaderboards

Discussion: How do you see a transition from the traditional assessment to a new and more flexible academic assessment?

digital literacy in the NHR 2016

The New Horizon Report, 2016

http://www.nmc.org/nmc-horizon-news/nmc-launches-app-for-the-2016-nmc-horizon-report/

page 24. Improving Digital Literacy

For years educators have leveraged curation tools such as Scoop.it, Storify, and Pinterest to help students critically evaluate online resources.
 (my bold to emphasize the difference between the definition of digital literacy, which I am fighting to establish at SCSU LRS and the continuous “information literacy” trend of the reference librarians )
Mapping Digital Literacy Policy and Practice in the Canadian Landscape
http://mediasmarts.ca/research-policy/mapping-digital-literacy-policy-practice-canadian-education-landscape

A well-rounded digital literacy incorporates print literacy but adds new capacities, competencies and comportments into the mix. Now included is the technical know-how to create a website, produce and upload a video, edit an image, design a functional information architecture for accessing or sharing knowledge – as well as many “soft skills” such as critical thinking and ethical behaviour. One of the primary transformations of the digital era in the 21st Century has been the introduction of end-users as actors in the world of communication, autonomous (producers and consumers of information) who can access and disseminate content in Web 2.0 domains without the regulatory controls of traditional filters and gatekeepers. Given this development, end-users now need greater critical thinking capacities to manage content: to decide what is valid and truthful and be able to incorporate multiple perspectives and voices into expanding worldviews. Additionally, exhibiting ethical behaviour in what may be said or posted online is essential to contemporary civic mindedness whether in a local context or the broader global village.

Getting Started: Multimedia Literacy

http://guides.lib.udel.edu/multimedia

Multimedia literacy is the set of abilities that enables an individual to effectively find, interpret, evaluate, use, and create multimedia.

http://www.deakin.edu.au/library/teach/digital-literacy/elements-of-digital-literacy – too simplistic, too traditional, no significant departure from the conservative information literacy

More on digital literacy in this IMS blog:

http://blog.stcloudstate.edu/ims?s=digital+literacy

libraries and learning

How Libraries Fit in the Future of Learning

http://www.edtechmagazine.com/k12/article/2016/01/how-libraries-fit-future-learning

Amy Brown, M.Ed. is a K-12 education strategist for CDW·G. January 20, 2016

According to the New Media Consortium’s Horizon Report: 2015 K-12 Edition, schools all over the nation have begun promoting content creation over content consumption.

When evaluating equipment, administrators need to consider how it will work with the space.

More about school media places and the future for information media in academia:

http://blog.stcloudstate.edu/ims/?s=library&submit=Search

related IMS blog entry: http://blog.stcloudstate.edu/ims/2016/02/24/open-educational-resources/

Trends Tomorrow’s Teaching and Learning Environments

Innovating Pedagogy: Which Trends Will Influence Tomorrow’s Teaching and Learning Environments?

Stefanie Panke

In November 2015, the Open University released the latest edition of its ‘Innovating Pedagogyreport, the fourth rendition of an annual educational technology and teaching techniques forecast. While the timelines and publishing interval may remind you of the Horizon Report, the methodology for gathering the trends is different.

The NMC Horizon Team uses a modified Delphi survey approach with a panel of experts.

Teaching and Learning Environments

10 Innovative Pedagogy Trends from the 2015 Edition:

  1. Crossover Learning: recognition of diverse, informal achievements with badges.
  2. Learning through Argumentation: To fully understand scientific ideas and effectively participate in public debates students should practice the kinds of inquiry and communication processes that scientists use, and pursue questions without known answers, rather than reproducing facts.
  3. Incidental Learning: A subset of informal learning, incidental learning occurs through unstructured exploration, play and discovery. Mobile technologies can support incidental learning. An example is the app and website Ispot Nature.
  4. Context-based Learning: Mobile applications and augmented reality can enrich the learners’ context. An example is the open source mobile game platform ARIS.
  5. Computational Thinking: The skills that programmers apply to analyze and solve problems are seen as an emerging trend . An example is the programming environment SCRATCH.
  6. Learning by Doing Science with Remote Labs:  A collection of accessible labs is ilab
  7. Embodied learning: involving the body is essential for some forms of learning, how physical activities can influence cognitive processes.
  8. Adaptive Teaching: intelligent tutoring systems – computer applications that analyse data from learning activities to provide learners with relevant content and sequence learning activities based on prior knowledge.
  9. Analytics of Emotions: As techniques for tracking eye movements, emotions and engagement have matured over the past decade, the trend prognoses opportunities for emotionally adaptive learning environments.
  10. Stealth Assessment: In computer games the player’s progress gradually changes the game world, setting increasingly difficult problems through unobtrusive, continuous assessment.

6 Themes of Pedagogical Innovation

Based upon a review of previous editions, the report tries to categorize pedagogical innovation into six overarching themes:

 “What started as a small set of basic teaching methods (instruction, discovery, inquiry) has been extended to become a profusion of pedagogies and their interactions. So, to try to restore some order, we have examined the previous reports and identified six overarching themes: scale, connectivity, reflection, extension, embodiment, and personalisation.”

  1. Delivering education at massive scale.
  2. Connecting learners from different nations, cultures and perspectives.
  3. Fostering reflection and contemplation.
  4. Extending traditional teaching methods and settings.
  5. Recognizing embodied learning (explore, create, craft, and construct).
  6. Creating a personalized path through educational content.

Further Reading

Follow these links to blog posts and EdITLib resources to further explore selected trends:

full article can be found here:

Innovating Pedagogy: Which Trends Will Influence Tomorrow’s Teaching and Learning Environments?

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