Searching for "cloud computing"

American AI Initiative

Trump creates American AI Initiative to boost research, train displaced workers

The order is designed to protect American technology, national security, privacy, and values when it comes to artificial intelligence.

STEPHEN SHANKLAND,SEAN KEANE FEBRUARY 11, 2019

https://www.cnet.com/news/trump-to-create-american-ai-initiative-with-executive-order/

President Donald Trump on Monday directed federal agencies to improve the nation’s artificial intelligence abilities — and help people whose jobs are displaced by the automation it enables.

t’s good for the US government to focus on AI, said Daniel Castro, chief executive of the Center for Data Innovation, a technology-focused think tank that supports the initiative.

Silicon Valley has been investing heavily in AI in recent years, but the path hasn’t always been an easy one. In October, for instance, Google withdrew from competition for a $10 billion Pentagon cloud computing contract, saying it might conflict with its principles for ethical use of AI.

Trump this week is also reportedly expected to sign an executive order banning Chinese telecom equipment from US wireless networks by the end of February.

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more on AI in this IMS blog
http://blog.stcloudstate.edu/ims?s=artificial+intelligence

IBM acquires Red Hat

IBM’s acquisition of Red Hat is huge news for the Linux world

Red Hat, although not quite a household name, is an undeniably significant company, with lots of fingers in lots of pies, especially when it comes to cloud computing and the Linux ecosystem.

The gem in its crown is arguably the platform-as-a-service (PaaS) provider OpenShift, which directly competes with the Salesforce-owned Heroku and Google App Engine. It also owns and develops Red Hat Enterprise Linux (RHEL), which is employed across several commercial settings, including workstations, servers, and supercomputers.

Red Hat is an enthusiastic contributor to several major Linux projects, playing a role in developing Libre Office and GNOME, as well as the Kernel itself.

in 2016, Red Hat was the second most prolific contributor to the Linux Kernel, narrowly trailing silicon mega-titan Intel

 

digital transformation online professional education

<h3 “>Sharpen the digital transformation 
strategy for your business.

Enroll today in Digital Transformation: From AI and IoT to Cloud, Blockchain, and Cybersecurity

https://professionalonline1.mit.edu/digital-transformation/index.php

PROGRAM FEES $2,300 STARTS ON November 28, 20182 months, online
6-8 hours per week

A Digital Revolution Is Underway.

In a rapidly expanding digital marketplace, legacy companies without a clear digital transformation strategy are being left behind. How can we stay on top of rapid—and sometimes radical—change? How can we position our organizations to take advantage of new technologies? How can we track and combat the security threats facing all of us as we are swept forward into the future?

Who is this Program for?

  • Professionals in traditional companies poised to implement strategic change, as well as entrepreneurs seeking to harness the opportunities afforded by new technologies, will learn the fundamentals of digital transformation and secure the necessary tools to navigate their enterprise to a digital platform.
  • Participants come from a wide range of industries and include C-suite executives, business consultants, corporate attorneys, risk officers, marketing, R&D, and innovation enablers.

<h3 “>Your Learning Journey

This online program takes you through the fundamentals of digital technologies transforming our world today. Led by MIT faculty at the forefront of data science, participants will learn the history and application of transformative technologies such as blockchain, artificial intelligence, cloud computing, IoT, and cybersecurity as well as the implications of employing—or ignoring—digitalization.

Brochure_MIT_PE_DigitalTransformation_17_Oct_18_V20-1w4qpjv

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Google China

Alphabet’s Plans for a China Comeback Go Beyond Google Search

Google has faced sharp criticism, including from its own employees, for its efforts to rebuild an internet search presence in Chinaafter quitting the country eight years ago over censorship issues.

for Google’s corporate parent, Alphabet, the opportunities in the world’s largest internet market may be too good to resist. And the full scope of the company’s interest in China now appears to be broader than just internet search.

The latest hint came from Waymo, the driverless-car company that was spun out of Google in 2016. Chinese media noticed this week that the business had quietly registered a Shanghai subsidiary in May, suggesting that it wants a piece of an industry that the Chinese government has made a priority.

Unlike Google, Apple runs its own app store in China, heeding government directives about the kinds of apps that can be available to Chinese users. Microsoft and Amazon offer cloud computing services, working with local partners and following strict controls on how customers’ data is stored.

Baidu, maker of the country’s leading search engine, has made its autonomous-vehicle software platform available to dozens of local and foreign companies. SAIC Motor, China’s largest carmaker, is working with the e-commerce titan Alibaba. BMW and Daimler have received permission in China to test their own self-driving vehicles.

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more on Google and China in this IMS blog
http://blog.stcloudstate.edu/ims?s=google+china

IRDL proposal

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

Title:

Learning to Harness Big Data in an Academic Library

Abstract (200)

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

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

 

 

Research Literature

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

Method

 

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

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

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

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

 

Sampling design

 

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

 

Project Schedule

 

Complete literature review and identify areas of interest – two months

Prepare and test instrument (survey) – month

IRB and other details – month

Generate a list of potential libraries to distribute survey – month

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

Collect, analyze data – two months

Write out data findings – month

Complete manuscript – month

Proofreading and other details – month

 

Significance of the work 

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

 

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

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

 

 

References:

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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more on big data





pedagogically sound Minecraft examples

FridayLive!! Oct 27 THIS WEEK 2:00 PM EDT 

Minecraft for Higher Ed? Try it. Pros, Cons, Recommendations? 

Description: Why Minecraft, the online video game? How can Minecraft improve learning for higher education?
We’ll begin with a live demo in which all can participate (see “Minecraft for Free”).
We’ll review “Examples, Not Rumors” of successful adaptations and USES of Minecraft for teaching/learning in higher education. Especially those submitted in advance
And we’ll try to extract from these activities a few recommendations/questions/requests re Minecraft in higher education.

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Examples:

Minecraft Education Edition: https://education.minecraft.net/
(more info: http://blog.stcloudstate.edu/ims/2017/05/23/minecraft-education-edition/)

K12: 

Minecraft empathy skillshttp://www.gettingsmart.com/wp-content/uploads/2017/04/How-Minecraft-Supports-SEL.pdf 

coding w MineCraft

Minecraft for Math

Higher Ed: 

Minecraft Higher Education?

Using MCEE in Higher Education

Why NOT to use minecraft in education:

https://higheredrevolution.com/why-educators-probably-shouldn-t-use-minecraft-in-their-classrooms-989f525c6e62

College Students Get Virtual Look at the Real World with ‘Minecraft’

Carnegie Mellon University uses the game-based learning tool to help students demonstrate engineering skills. SEP182017

https://edtechmagazine.com/higher/article/2017/09/college-students-get-virtual-look-real-world-minecraft

Using Minecraft in Higher Education

https://groups.google.com/forum/#!topic/minecraft-teachers/cED6MM0E0bQ

Using MinecraftEdu – Part 1 – Introduction

https://www.youtube.com/watch?v=Lsfd9J5UgVk

Physics with Minecraft example

Chemistry with Minecraft example

Biology

other disciplines

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Does learning really happen w Minecraft?

Callaghan, N. (2016). Investigating the role of Minecraft in educational learning environments. Educational Media International53(4), 244-260. doi:10.1080/09523987.2016.1254877

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d119571817%26site%3dehost-live%26scope%3dsite

Noelene Callaghan dissects the evolution in Australian education from a global perspective. She rightfully draws attention (p. 245) to inevitable changes in the educational world, which still remain ignored: e.g., the demise of “traditional” LMS (Educase is calling for their replacement with digital learning environments http://blog.stcloudstate.edu/ims/2017/07/06/next-gen-digital-learning-environment/ and so does the corporate world of learning: http://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/ ), the inevitability of BYOD (mainly by the “budget restrictions and sustainability challenges” (p. 245); by the assertion of cloud computing, and, last but not least, by the gamification of education.

p. 245 literature review. In my paper, I am offering more comprehensive literature review. While Callaghan focuses on the positive, my attempt is to list both pros and cons: http://scsu.mn/1F008Re

 

  1. 246 General use of massive multiplayer online role playing games (MMORPGs)

levels of interaction have grown dramatically and have led to the creation of general use of massive multiplayer online role playing games (MMORPGs)

  1. 247 In teaching and learning environments, affordances associated with edugames within a project-based learning (PBL) environment permit:
  • (1)  Learner-centered environments
  • (2)  Collaboration
  • (3)  Curricular content
  • (4)  Authentic tasks
  • (5)  Multiple expression modes
  • (6)  Emphasis on time management
  • (7)  Innovative assessment (Han & Bhattacharya, 2001).

These affordances develop both social and cognitive abilities of students

 

Nebel, S., Schneider, S., Beege, M., Kolda, F., Mackiewicz, V., & Rey, G. (2017). You cannot do this alone! Increasing task interdependence in cooperative educational videogames to encourage collaboration. Educational Technology Research & Development65(4), 993-1014. doi:10.1007/s11423-017-9511-8

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Abrams, S. S., & Rowsell, J. (2017). Emotionally Crafted Experiences: Layering Literacies in Minecraft. Reading Teacher70(4), 501-506.

Nebel, S., Schneider, S., & Daniel Rey, G. (2016). Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research. Source: Journal of Educational Technology & Society, 19(192), 355–366. Retrieved from http://www.jstor.org/stable/jeductechsoci.19.2.355

Cipollone, M., Schifter, C. C., & Moffat, R. A. (2014). Minecraft as a Creative Tool: A Case Study. International Journal Of Game-Based Learning4(2), 1-14.

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Niemeyer, D. J., & Gerber, H. R. (2015). Maker culture and Minecraft : implications for the future of learning. Educational Media International52(3), 216-226. doi:10.1080/09523987.2015.1075103

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Nebel, S., Schneider, S., & Daniel Rey, G. (2016). Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research. Journal of Educational Technology & Society, 19(192), 355–366. Retrieved from http://www.jstor.org/stable/jeductechsoci.19.2.355

 

Wilkinson, B., Williams, N., & Armstrong, P. (2013). Improving Student Understanding, Application and Synthesis of Computer Programming Concepts with Minecraft. In The European Conference on Technology in the Classroom 2013. Retrieved from http://iafor.info/archives/offprints/ectc2013-offprints/ECTC2013_0477.pdf

Berg Marklund, B., & Alklind Taylor, A.-S. (2015). Teachers’ Many Roles in Game-Based Learning Projects. In Academic Conferences International Limited (pp. 359–367). Retrieved from https://search.proquest.com/openview/15e084a1c52fdda188c27b9d2de6d361/1?pq-origsite=gscholar&cbl=396495

Uusi-Mäkelä, M., & Uusi-Mäkelä, M. (2014). Immersive Language Learning with Games: Finding Flow in MinecraftEdu. EdMedia: World Conference on Educational Media and Technology (Vol. 2014). Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/noaccess/148409/

Birt, J., & Hovorka, D. (2014). Effect of mixed media visualization on learner perceptions and outcomes. In 25th Australasian Conference on Information Systems (pp. 1–10). Retrieved from http://epublications.bond.edu.au/fsd_papers/74

Al Washmi, R., Bana, J., Knight, I., Benson, E., Afolabi, O., Kerr, A., Hopkins, G. (2014). Design of a Math Learning Game Using a Minecraft Mod. https://doi.org/10.13140/2.1.4660.4809
https://www.researchgate.net/publication/267135810_Design_of_a_Math_Learning_Game_Using_a_Minecraft_Mod
https://docs.google.com/document/d/1uch2iC_CGsESdF9lpATGwWkamNbqQ7JOYEu_D-V03LQ/edit?usp=sharing

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more on Minecraft in this IMS blog
http://blog.stcloudstate.edu/ims?s=minecraft

instructional design

Developments in Instructional Design

https://library.educause.edu/~/media/files/library/2015/5/eli7120-pdf.pdf

Mobile computing, cloud computing, and data-rich repositories have altered ideas about where and how learning takes place.
designers can find themselves filling a variety of roles. They might design large, complex systems or work with faculty and departments to develop courses and curricula. They might migrate traditional resources to mobile or adaptive platforms. They might help administrators understand the value and potential of new learning strategies and tools. Today’s instructional designer might work with subject-matter experts, coders, graphic designers, and others. Moreover, the work of an instructional designer increasingly continues throughout the duration of a course rather than taking place upfront
Given the expanding role and landscape of technology—as well as the growing body of knowledge about learning and about educational activities and assessments—dedicated instructional designers are increasingly common and often take a stronger role.
Competency based learning allows students to progress at their own pace and finish assignments, courses, and degree plans as time and skills permit. Data provided by analytics systems can help instructional designers predict which pedagogical approaches might be most effective and tailor learning experiences accordingly. The use of mobile learning continues to grow, enabling new kinds of learning experiences.
In some contexts, instructional designers might work more directly with students, teaching them lifelong learning skills. Students might begin coursework by choosing from a menu of options, creating their own path through content, making choices about learning options, being more hands-on, and selecting best approaches for demonstrating mastery. Educational models that feature adaptive and personalized learning will increasingly be a focus of instructional design.
Instructional designers bring a cross-disciplinary approach to their work, showing faculty how learning activities used in particular subject areas might be effective in others. In this way, instructional designers can cultivate a measure of consistency across courses and disciplines in how educational strategies and techniques are incorporated.
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more on instructional design in this IMS blog
http://blog.stcloudstate.edu/ims?s=instructional+design

evaluate IT in K12

New Ways to Evaluate School Technologies to Save Money & Boost Efficiencies

https://event.on24.com/wcc/r/1483433/3117E1766D64897841ED782BEEFC3C83?mode=login&email=pmiltenoff@stcloudstate.edu

Please join me September 20 for a free webinar where Dr. Sheryl Abshire, CTO of Calcasieu Parish SD and a recognized leader in K-12 technology, shares her insights on the top strategies, best practices and most valuable ideas that can reduce IT departmental costs and increase efficiencies.

What: New Ways to Measure & Leverage the Value of IT
When: 09/20 @ 2:00 PM ET | 11:00 AM PT

Register Now

Listen in and learn how to:
·         Use data you already collect to justify needs and resources
·         Create a new value proposition for IT
·         Measure the strategic use of IT in the district
·         Determine if your current technology is making the difference you expected

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My notes from the webinar:
Gartner: K12 technology ; http://www.gartner.com/technology/research/content/education.jsp

https://www.schooldude.com/ Tech support costs in K12 increased by 50% in the last four years from 14% to 21% of the technology budget. One half of the school technology leaders said that their school board understands that technology relates to district oveall goals , it is not as supportive financially. Worse, 8% felt that the school board does not believe technology is important to their district overall goals

Harvard Business Report Driving Digital Transformation. 2015 surveyed digital leaders. Driving innovation most important role breaking down internal silos

https://hbr.org/resources/pdfs/comm/RedHat/RedHatReportMay2015.pdf

  • align technology with educational mission of the school district
  • show value
  • eliminate silos
  • look for cost savings
  • other investments with long-term savings
  • transformational strategies
  • engage community – bond issues, levies, and other funding

consortium for school networking: 10 concepts http://www.nmc.org/organization/cosn/

virtualization; data deluge; energy and green IT; complex resource tracking; consumerization and social software; unified communications; mobile and wireless; system density; mashups and portals; cloud computing

what is a quick recovery?

Action plan: 1. Focus on virtualization and green IT for immediate cost and flexibility benefits. 2. Look at storage virtualization, deduplication and thin provisioning. 3. Evaluate web social software to transform interactions 4. exploit mashups and cloud-based services to address immediate user needs. 5. link UC to collaboration and enterprise applications to support growth initiatives. 6. begin to track weak signals and subtle patterns – from everywhere.


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SchoolDude – Josh Green, Application Engineer, josh.green@schooldude.com

  • lack of budget and staff
  • managing upkeep and replacement of growing number of devices
  • time
  • perception gap (what we are doing)

tool: Insight
agentless network discovery mechanism. scanning of devices on the network. optimize hard software usage, improve planning and budgeting process with status reporting.

MDM (mobile device management). supports both BYOD and school devices. control app distribution across the network, supervise device usage, remotely manage device policy

Helpdesk: complete ticket to close helpdesk solution

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Q&A time

technology facilitators: spend time at assigned schools; talk to teacher and try to figure out what teachers know about technology and then work the principal to customize workshops (PLCs) to build the skills based on their skills set. versus technology facilitator at every school. Help them grow their own.

certificate of attendance-Plamen Miltenoff

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more on digital literacy for EDAD in this IMS blog
http://blog.stcloudstate.edu/ims?s=digital+literacy+edad

industry 4.0

A Strategist’s Guide to Industry 4.0. Global businesses are about to integrate their operations into a seamless digital whole, and thereby change the world.

https://www.strategy-business.com/article/A-Strategists-Guide-to-Industry-4.0
Industrial revolutions are momentous events. By most reckonings, there have been only three. The first was triggered in the 1700s by the commercial steam engine and the mechanical loom. The harnessing of electricity and mass production sparked the second, around the start of the 20th century. The computer set the third in motion after World War II.
Henning Kagermann, the head of the German National Academy of Science and Engineering (Acatech), did exactly that in 2011, when he used the term Industrie 4.0 to describe a proposed government-sponsored industrial initiative.
The term Industry 4.0 refers to the combination of several major innovations in digital technology
These technologies include advanced robotics and artificial intelligence; sophisticated sensors; cloud computing; the Internet of Things; data capture and analytics; digital fabrication (including 3D printing); software-as-a-service and other new marketing models; smartphones and other mobile devices; platforms that use algorithms to direct motor vehicles (including navigation tools, ride-sharing apps, delivery and ride services, and autonomous vehicles); and the embedding of all these elements in an interoperable global value chain, shared by many companies from many countries.
Companies that embrace Industry 4.0 are beginning to track everything they produce from cradle to grave, sending out upgrades for complex products after they are sold (in the same way that software has come to be updated). These companies are learning mass customization: the ability to make products in batches of one as inexpensively as they could make a mass-produced product in the 20th century, while fully tailoring the product to the specifications of the purchaser
.

adoption industry 4.0 by sector

Three aspects of digitization form the heart of an Industry 4.0 approach.

• The full digitization of a company’s operations

•  The redesign of products and services

•  Closer interaction with customers

Making Industry 4.0 work requires major shifts in organizational practices and structures. These shifts include new forms of IT architecture and data management, new approaches to regulatory and tax compliance, new organizational structures, and — most importantly — a new digitally oriented culture, which must embrace data analytics as a core enterprise capability.

Klaus Schwab put it in his recent book The Fourth Industrial Revolution (World Economic Forum, 2016), “Contrary to the previous industrial revolutions, this one is evolving at an exponential rather than linear pace.… It is not only changing the ‘what’ and the ‘how’ of doing things, but also ‘who’ we are.”

This great integrating force is gaining strength at a time of political fragmentation — when many governments are considering making international trade more difficult. It may indeed become harder to move people and products across some national borders. But Industry 4.0 could overcome those barriers by enabling companies to transfer just their intellectual property, including their software, while letting each nation maintain its own manufacturing networks.
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more on the Internet of Things in this IMS blog
http://blog.stcloudstate.edu/ims?s=internet+of+things

also Digital Learning

http://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/

K-12 Technology

A Digital Future: K-12 Technology by 2018

http://www.theedadvocate.org/a-digital-future-k-12-technology-by-2018/

The recently-released New Media Consortium Horizon Report details six up-and-coming technologies in the next five years for K-12 classrooms.

Horizon #1: In the next year, or less.

Mobile learning. Cloud computing.

Horizon #2: Within two to three years.

Learning analytics. Open content.

Horizon #3: Within four to five years.

3D printing. Virtual laboratories.

Presented on the NMC K-12 Horizon Report over the weekend at the Alliance for International Education Conference held at Yew Chung International School of Shanghai: http://www.slideshare.net/davidwdeeds/aie-2015-china-conference-using-the-nmc-k12-horizon-report

 

AIE 2015 China Conference: Using the NMC K-12 Horizon Report from David W. Deeds

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