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IRDL proposal

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

Title:

Learning to Harness Big Data in an Academic Library

Abstract (200)

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

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

 

 

Research Literature

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

Method

 

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

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

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

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

 

Sampling design

 

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

 

Project Schedule

 

Complete literature review and identify areas of interest – two months

Prepare and test instrument (survey) – month

IRB and other details – month

Generate a list of potential libraries to distribute survey – month

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

Collect, analyze data – two months

Write out data findings – month

Complete manuscript – month

Proofreading and other details – month

 

Significance of the work 

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

 

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

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

 

 

References:

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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more on big data





code4lib 2018

Code4Lib 2018 is a loosely-structured conference that provides people working at the intersection of libraries/archives/museums/cultural heritage and technology with a chance to share ideas, be inspired, and forge collaborations. For more information about the Code4Lib community, please visit http://code4lib.org/about/.

The conference will be held at the Omni Shoreham Hotel in Washington, DC, from February 13, 2018 – February 16, 2018.  More information about Code4lib 2018 is available on this year’s conference website http://2018.code4lib.org.

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Mark Gill and Plamen Miltenoff proposal:

Gamification of Library Orientation and Instruction

Abstract

The rapid advent in the technologies of augmented and virtual reality (VR) in the last several years and the surge down in price creates possibilities for its increasing and ubiquitous application in education. A collaboration by a librarian and VR specialist led to testing opportunities to apply 360 video in academic library orientation. The team seeks to bank on the inherited interest of Millennials toward these technologies and their inextricable part of a growing gaming environment in education. A virtual introduction via 360 video aims to familiarize patrons with the library and its services: http://bit.ly/VRlib. I short Surveymonkey survey following the virtual introduction assesses learning outcomes and allows further instruction when necessary. Patrons can use any electronic devices from desktop to any size mobile devices. Patrons can also watch in panorama mode, and are provided with goggles if they would like to experience the VR mode.

The next step is an introduction to basic bibliographic instruction, followed by a gamified “scavenger hunt”-kind of exercise, which aims to gamify students’ ability to perform basic research: http://bit.ly/learnlib. The game is web-based and it can be played on any electronic devices from desktops to mobile devices. The game is followed by a short Google Form survey, which assesses learning outcomes and allows further work shall any knowledge gaps occur.

The team relies on the constructivist theory of assisting patrons in building their knowledge in their own pace and on their own terms, rather than being lectured and guided by a librarian only.

This proposal envisions half a day activities for participants to study the opportunities presented by 360 video camera and acquire the necessary skills to collect quickly useful footage and process it for the library needs. The second half of the day is allocated for learning Adobe Dreamweaver to manipulate the preexisting “templates” (HTML and jQuery code) for the game and adapt the content and the format to the needs of the participants’ libraries.

Mark Gill mcgill@stcloudstate.edu 320-308-5605

Mr. Gill is a Visualization Engineer for the College of Science and Engineering and runs the Visualization Laboratory.  He has worked for several major universities as well as Stennis Space Center and Mechdyne, Inc.  He holds a Masters of Science in Software Engineering.

Plamen Miltenoff, Ph.D., MLIS  pmiltenoff@stcloudstate.edu 320-308-3072

Dr. Miltenoff is part of a workgroup within the academic library, which works with faculty, students and staff on the application of new technologies in education. Dr. Miltenoff’s most recent research with Mark Gill is on the impact of Video 360 on students during library orientation: http://web.stcloudstate.edu/pmiltenoff/bi/

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more about code4lib in this IMS blog
https://blog.stcloudstate.edu/ims/2016/11/11/code4lib/

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

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

IM554 discussion on GBL

IM554 discussion on Game Based Learning

Here is the “literature”:
https://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) (https://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) (https://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?

NISO Webinar IoT

Wednesday, October 19, 2016
1:00 p.m. – 2:30 p.m. (Eastern Time)

About the Webinar

As the cost of sensors and the connectivity necessary to support those sensors has decreased, this has given rise to a network of interconnected devices.  This network is often described as the Internet of Things and it is providing a variety of information management challenges.  For the library and publishing communities, the internet of things presents opportunities and challenges around data gathering, organization and processing of the tremendous amounts of data which the internet of things is generating.  How will these data be incorporated into traditional publication, archiving and resource management systems?  Additionally, how will the internet of things impact resource management within our community?   In what ways will interconnected resources provide a better user experience for patrons and readers?  This session will introduce concepts and potential implications of the internet of things on the information management community.  It will also explore applications related to managing resources in a library environment that are being developed and implemented.

Education in the Internet of Things
Bryan Alexander, Consultant;

How will the Internet of Things shape education? We can explore this question by assessing current developments, looking for future trends in the first initial projects. In this talk I point to new concepts for classroom and campus spaces, examining attendant rises in data gathering and analysis. We address student life possibilities and curricular and professional niches. We conclude with notes on campus strategy, including privacy, network support, and futures-facing organizations.

What Does The Internet of Things Mean to a Museum?
Robert Weisberg, Senior Project Manager, Publications and Editorial Department; Metropolitan Museum of Art;

What does the Internet of Things mean to a museum? Museums have slowly been digitizing their collections for years, and have been replacing index cards with large (and costly, and labor-intensive) CMS’s long before that, but several factors have worked against adopting smart and scalable practices which could unleash data for the benefit of the institution, its collection, and its audiences. Challenges go beyond non-profit budgets in a very for-profit world and into the siloed behaviors learned from academia, practices borne of the uniqueness of museum collections, and the multi-faceted nature of modern museums which include not only curator, but conservators, educators, librarians, publishers, and increasing numbers of digital specialists. What have museums already done, what are they doing, and what are they preparing for, as big data becomes bigger and ever more-networked?
The Role of the Research Library in Unpacking The Internet of Things
Lauren di Monte, NCSU Libraries Fellow, Cyma Rubin Fellow, North Carolina State University

The Internet of Things (IoT) is a deceptively simple umbrella term for a range of socio-technical tools and processes that are shaping our social and economic worlds. Indeed, IoT represents a new infrastructural layer that has the power to impact decision-making processes, resources distribution plans, information access, and much more. Understanding what IoT is, how “things” get networked, as well as how IoT devices and tools are constructed and deployed, are important and emerging facets of information literacy. Research libraries are uniquely positioned to help students, researchers, and other information professionals unpack IoT and understand its place within our knowledge infrastructures and digital cultures. By developing and modeling the use of IoT devices for space and program assessment, by teaching patrons how to work with IoT hardware and software, and by developing methods and infrastructures to collect IoT devices and data, we can help our patrons unlock the potential of IoT and harness the power of networked knowledge.

Lauren Di Monte is a Libraries Fellow at NC State. In this role she develops programs that facilitate critical and creative engagements with technologies and develops projects to bring physical and traditional computing into scholarship across the disciplines. Her current research explores the histories and futures of STEM knowledge practices.

What does the internet of things mean for education?

Bryan Alexander:

I’m not sure if the IoT will hit academic with the wave force of the Web in the 1990s, or become a minor tangent.  What do schools have to do with Twittering refrigerators?

Here are a few possible intersections.

  1. Changing up the campus technology space.  IT departments will face supporting more technology strata in a more complex ecosystem.  Help desks and CIOs alike will have to consider supporting sensors, embedded chips, and new devices.  Standards, storage, privacy, and other policy issues will ramify.
  2. Mutating the campus.  We’ve already adjusted campus spaces by adding wireless coverage, enabling users and visitors to connect from nearly everywhere.  What happens when benches are chipped, skateboards sport sensors, books carry RFID, and all sorts of new, mobile devices dot the quad?  One British school offers an early example.
  3. New forms of teaching and learning.  Some of these take preexisting forms and amplify them, like tagging animals in the wild or collecting data about urban centers.  The IoT lets us gather more information more easily and perform more work upon it.  Then we could also see really new ways of learning, like having students explore an environment (built or natural) by using embedded sensors, QR codes, and live datastreams from items and locations.  Instructors can build treasure hunts through campuses, nature preserves, museums, or cities.  Or even more creative enterprises.
  4. New forms of research.  As with #3, but at a higher level.  Researchers can gather and process data using networked swarms of devices.  Plus academics studying and developing the IoT in computer science and other disciplines.
  5. An environmental transformation.  People will increasingly come to campus with experiences of a truly interactive, data-rich world.  They will expect a growing proportion of objects to be at least addressable, if not communicative.  This population will become students, instructors, and support staff.  They will have a different sense of the boundaries between physical and digital than we now have in 2014. Will this transformed community alter a school’s educational mission or operations?

How the internet could evolve to 2026: responding to Pew Posted on

Stacks CMS for libraries

EBSCO Launches Content Management System for Libraries

By Leila Meyer 09/26/16

https://thejournal.com/articles/2016/09/26/ebsco-debuts-content-management-system-for-libraries.aspx

EBSCO Information Services has debuted Stacks, a hosted content management system for libraries, and Stacks Mobile, a native app for iOS and Android devices.

Social media integration, including Goodreads, Facebook, Twitter and LinkedIn;

++++++++++++++++

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

Free Tech Instruction (About Us)

>>>Fall 2019 workshops IMS instruction technology sessions<<<

Student’s relationship with technology is complex. They recognize its value but still need guidance when it comes to better using it for academics. Educause’s ECAR Study, 2013

InforMedia Services

IMS faculty would be happy to meet with you or your group at your convenience.
Please request using this Google Form: http://scsu.mn/1OjBMf9 or
by email: pmiltenoff@stcloudstate.edu | informedia@stcloudstate.edu
Here is the evaluation form: http://bit.ly/imseval

How you can reach us:

Services we provide:

  • Instruct and collaborate with faculty, staff and students on specific computer, Cloud and mobile applications
  • Assist faculty in course design and instruction to incorporate SCSU’s resources
  • Join faculty in the classroom instructional design to assist students with learning technology application for the class
  • Consult with faculty on instructional design issues, particularly those that use the World Wide Web, multimedia techniques and interactivity
  • Collaborate with faculty, staff and students on technology-related projects
  • Work with campus units in technology planning and acquisition
  • Respond to faculty, staff and students requests and technology developments

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Desire2Learn (D2L), Digital literacy, digital photography, e-learning, educational technology, gamification, gaming, image editing, interactive apps, learning, lecture capture, Millennials, mobile apps, mobile apps, mobile devices, mobile learning, MOOC, online learning, Photoshop, podcasting, programming languages, smartboard, social media, teaching, technology, technology literacy, video editing, virtualization, web conferencing platform, web development, web editing

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password management

LITA listsrev has an excellent discussion on password management.
I personally am using LastPass for two years: great free option, paid one can be used on mobiles.

=========================

From: lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of Michael J. Paulmeno
Sent: Wednesday, January 06, 2016 1:36 PM
To: lita-l@lists.ala.org
Subject: RE: [lita-l] Question on password management

 

I second Keepass.  Not only is it free, open source, and multi-OS, but it lives on your computer, not in the cloud (although the database can be put on a shared drive or in DropBox for access across devices).  Personally that makes me feel much safer.  There are clients available for Windows, Mac, Linux, IPhone, Android and even Blackberry.

 

Cheers,

Mike

 

From: lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of Ronald Houk
Sent: Wednesday, January 06, 2016 12:38 PM
To: lita-l@lists.ala.org
Subject: Re: [lita-l] Question on password management

 

I use lastpass as well.  However, LastPass was just bought by LogMeIn, so lots of people are holding their breath hoping that things stay good.  Another open source, multi-os, alternative is keepass (keepass.info)

 

On Wed, Jan 6, 2016 at 11:43 AM, Yvonne Reed <yvonner@ranchomiragelibrary.org> wrote:

Hi Everyone,

I would like offer or recommend a password management tool to my library staff that’s reliable and easy to use. Do any of you have one you can recommend?

 

 

Thank you,

 

Yvonne Reed

Technology Librarian

Rancho Mirage Public Library

71-100 Hwy 111

Rancho Mirage, CA 92270

(760)341-7323 x770
————————————–

From: lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of O’English, Lorena
Sent: Wednesday, January 06, 2016 12:51 PM
To: lita-l@lists.ala.org
Subject: RE: [lita-l] Question on password management

 

I really like Dashlane (dashlane.com) – it has a lot of options, including the ability to give someone else access to your passwords in certain situations (plus, they support Firefox financially via low-impact ads). I think of this sometimes when I think about what would happen if a piano fell on me tomorrow – what a mess it would be for my spouse to cope with my digital life! That said, although I use Dashlane, I still have not quite managed to get myself to use all its functionality.

 

Lorena

***

Washington State University Libraries

oenglish@wsu.edu

wsulorena: Twitter, Skype, GTalk, Yahoo IM

———–

—–Original Message—–
From: lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of Cary Gordon
Sent: Wednesday, January 06, 2016 12:37 PM
To: lita-l@lists.ala.org
Subject: Re: [lita-l] Question on password management

 

1Password ++

————–

 

—–Original Message—–
From: lita-l-request@lists.ala.org [mailto:lita-l-request@lists.ala.org] On Behalf Of COLLINS, MATTHEW
Sent: Wednesday, January 06, 2016 12:35 PM
To: lita-l@lists.ala.org
Subject: RE: [lita-l] Question on password management

 

I have used Roboform for at least 10 years and never had a problem.  It manages passwords for logins and bookmarks on my PCs, my iPhone and iPad.  It synchs online so work, home, tablet and phone all have the same info.  It also stores personal info (name & multiple addresses) and confidential notes and other info.

 

–Matthew

———————-

Has anyone mentioned Password Safe? http://passwordsafe.sourceforge.net/

 

It’s worked well for organizing and managing usernames/passwords.

 

 

Angela Stangl

 

Digital Services Coordinator

Rodney A. Briggs Library

University of Minnesota, Morris

(320) 589-6164

——————————-

FEATURES

http://keepass.info/features.html

 

PLUGINS

http://keepass.info/plugins.html

Note: CAPS is used here and there to call attention without extra Gmail formatting, not to shout at anyone. Still…I know I look like I yell here. I have flogged myself, I will now bathe in the River Salt.

 

MWoT

Ok, check it out.

Plugins, macros, group/profile/source/target/timing locks, separate DBs and separate metadata for these if you like, INTERNALLY-ROTATING SUPERKEYS via REGULAR KEY TRANSORMATIONS and TWO-CHANNEL AUTO-TYPE OBFUSCATION (for obfuscating your auto-typed passwords or keys, if you select Auto)….!!!…

…and well-reasoned, well-EXPLAINED approaches to certain critical areas of password management in general and to KeePass in particular.

 

For instance: In the FAQ, read the logic breakdown (thought-by-thought explanation) of why Keepass does NOT lock itself when a SUB-dialogue box is open in Keepass whle the user then LOCKS the workstation. =)

Why doesn’t KeePass lock when Windows locks and a KeePass sub-dialog is open?

http://keepass.info/help/base/faq_tech.html#noautolock

My support of Keepass as a primary, then a close alternative, comes from four of my six years in IT being in direct computer and network security roles. Sure, not the most trench years out there, but they are all engineering and tiered-analyst roles for several major US corporations.

I’m proud of that…and in terms of relevance, I worked – and still work – with and around many engineers, analysts, and scientists (data, algorithmic). I look up to these people a great deal, and many of these coworkers come fully assembled having forgotten more than I’ll ever know and still learning faster than I could ever talk about… and even THEY use Keepass and they use it powerfully.

Detection of each site’s contact (HTTP GET, form forcus, etc) or “touch” can be different with each browser it integrates into, and that’s just for starters. One can also script up a different timing to use before the credentials are passed….;)….one can also relegate references to a central database, or one can refer only to the local system or even just a specific profile that can access said .kdbx file (KeePass database), or one can limit the data source to just one .kdbx single-instance database file, or one can use the .kdbx as a secondary for some other central repository failure, if that happens.

One can make several .kdbx files for different uses…no requirement to have just one! Each a diffferent base of unique data keys, each wtih a different direction administered on when it is referenced, how it is run, and where it lives on a system.

Aaaaaand it can integrate with other DBMs, it’s not an island!

Keepass is not the end-all be-all, but it IS FOSS (Free and Open-Source Software, great for investigating its machinery). Also it is:

Programmable (via the Plugins model, you can write some yourself if you like!)

Modularizable (again, via the Plugins model)

Profile lockable, (<— really neat!)

– SMM (Secure Memory Manageable, for Windows Clipboard and the like)

– and more!

Anyway, Keepass is rad for its cost, but, like the others on this thread, I will second LastPass as well. LastPass  is a an alternative to Keepass. =)

Daniel Strickland
linkedin.com/in/dwstrickland

 

 

Matthew Collins

Director of the Ernest Miller White Library Associate Professor of Research and Bibliography Louisville Presbyterian Theological Seminary

1044 Alta Vista Road

Louisville, KY 40205

mcollins@lpts.edu| 502.992.5420

 

educational technology and faculty development

Educational Technology and Faculty Development in Higher Education

http://net.educause.edu/ir/library/pdf/ers1507.pdf

 The Potential of Mobile Devices for Teaching and Learning

Despite the near ubiquity of student laptops and smartphones, in-class BYOD is still an emerging practice.

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