NMC Horizon Report > 2017 Higher Education Edition
- Digital Literacy
- Mobile Learning
- Next Generation LMS
p. 20 Coding as a Literacy
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
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
more on NMC Horizon Reports in this IMS blog
PDF file 2017-nmc-horizon-report-library-EN-20ml00b
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 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/on
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, James M. Matarazzo, Toby Pearlstein, 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
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
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
more on Horizon Reports in this IMS blog
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
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
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
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
Online learning here is used as a blanket term for all related terms:
Web enhanced learning occurs in a traditional face-to-face (f2f) course when the instructor incorporates web resources into the design and delivery of the course to support student learning. The key difference between Web Enhanced Learning versus other forms of e-learning (online or hybrid courses) is that the internet is used to supplement and support the instruction occurring in the classroom rather than replace it. Web Enhanced Learning may include activities such as: accessing course materials, submitting assignments, participating in discussions, taking quizzes and exams, and/or accessing grades and feedback.”
Goette, W. F., Delello, J. A., Schmitt, A. L., Sullivan, J. R., & Rangel, A. (2017). Comparing Delivery Approaches to Teaching Abnormal Psychology: Investigating Student Perceptions and Learning Outcomes. Psychology Learning and Teaching, 16(3), 336–352. https://doi.org/10.1177/1475725717716624
Helms (2014) described blended education as incorporating both online and F2F character- istics into a single course. This definition captures an important confound to comparing course administration formats because otherwise traditional F2F courses may also incorp- orate aspects of online curriculum. Blended learning may thus encompass F2F classes in which any course content is available online (e.g., recorded lectures or PowerPoints) as well as more traditionally blended courses. Helms recommended the use of ‘‘blended’’ over ‘‘hybrid’’ because these courses combine different but complementary approaches rather than layer opposing methods and formats.
Blended learning can merge the relative strengths of F2F and online education within a flexible course delivery format. As such, this delivery form has a similar potential of online courses to reduce the cost of administration (Bowen et al., 2014) while addressing concerns of quality and achievement gaps that may come from online education. Advantages of blended courses include: convenience and efficiency for the student; promotion of active learning; more effective use of classroom space; and increased class time to spend on higher- level learning activities such as cooperative learning, working with case studies, and discuss- ing big picture concepts and ideas (Ahmed, 2010; Al-Qahtani & Higgins, 2013; Lewis & Harrison, 2012).
Backchannel and CRS (or Audience Response Systems):
Blended Synchronous Learning project (http://blendsync.org/)
Minutes from November 29 meeting . (all documents are work in progress)
CATT (mixed of collective bargaining and various academic areas), student technology groups, TPR (Technological and Pedagogical Roundtable) – tech issue specific to faculty. not tech admin but broad issues.
Student tech fee commitee, ITS staff, SCSU Divisions (?); Management Team, MN stte system office / CIO; It external review members (?); STCC IT
More on charge of these groups
IT Strategic Planning – Lisa Foss, Phil Thorson, Shelly Mumm, Mike Freer, LaVonne, Joe Ben ueckler
Strategic Planning Team meets in the summer with the Management Team.
System office did the Educause survey w faculty and students. Horizon Report
D2L move to the cloud, domain change.
Lisa Foss; mini swats from SCSU deans . summer shaped a “certain perspectives”
2010 strategic vision for IT (30+ pages) never got off the ground, but the teams are the same. An external 2012 consultant (Koludes COmpany)
IT assessment group (?)
latest discussions: how to consult better campus users (Tom ?)
SCSU Strategic Plan as a template. Using similar/same goals and objectives: 1. engage students. objectives (come from the SCSU plan) a. integrate student learning and support. Strategy and source. This is on the Sharepoint site (Phil Thorson email
SCSU Tech Plan Engaged Students Objectives: what people will be able to do, if the plan is successful. 1.D. change from Engagement to Student Belonging. Analytics and Social Media is in the objectives. the objectives as they are too broad. I understand the need to keep them broad, but as they are they are too broad, which poses the danger of each stakeholder to interpret differently.
training and instruction what is the state and what is the plan. instead of department, can we build a network of people spread across departments. nationally 92% ecar survey https://www.educause.edu/ecar
engaged campus strategic priority. comprehensive technology training (?). the text reads as it is pertaining to IT staff only. Is it? if it is the entire campus, why does not mention it. so it is IT only at this point and needs to be reworded to be clear that included the entire campus. 2010 plan did not think about all different issues of technology in each department. one size fit the entire campus.
Engaged Communities: four campuses – Alnwick, Plymouth, SC and online
technology consortia: how to partner, lead etc
serving community members as community patrons.
what are the tactics comes late. aspirational
what the roadblocks. innovation
Tom (the faculty from the School of Health and Human Services – telemedicine) Janet Tilstred Communication Disorders
Phil Thorson: how is risk management fit in the complex issues.
Next step: what is this plan mean for COSE, for the other schools?
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
PROPOSAL: Paper presentation in a Themed Session
Virtual Reality and Gamification in the Educational Process: The Experience from an Academic Library
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.
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.
digitally mediated learning
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.
Learning to Harness Big Data in an Academic Library
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions.
Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
“Big data is 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.
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).
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.
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
Here is the “literature”:
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:
Topic :Gaming and Gamification in Academic Settings
Discussion: Are the presented reasons sufficient to justify a profound restructure of curricula and learning spaces?
Discussion: Is there a way to build a simpler but comprehensive structure/definition to encompass the process of gaming and gamification in education?
Discussion: Which side are you on and why?
Discussion: do you see a trend to suggest that either one or the other will prevail? Convergence?
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?
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?
Discussion: How do you see a transition from the traditional assessment to a new and more flexible academic assessment?
1 2 3 4 Next