Searching for "visual literacy"

07
Nov
2017

IRDL proposal

categories: Digital literacy, educational technology, information technology, instructional technology

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





07
Nov
2017

Key Issues in Teaching and Learning Survey

categories: Digital literacy, learning, mobile learning, teaching

The EDUCAUSE Learning Initiative has just launched its 2018 Key Issues in Teaching and Learning Survey, so vote today: http://www.tinyurl.com/ki2018.

Each year, the ELI surveys the teaching and learning community in order to discover the key issues and themes in teaching and learning. These top issues provide the thematic foundation or basis for all of our conversations, courses, and publications for the coming year. Longitudinally they also provide the way to track the evolving discourse in the teaching and learning space. More information about this annual survey can be found at https://www.educause.edu/eli/initiatives/key-issues-in-teaching-and-learning.

ACADEMIC TRANSFORMATION (Holistic models supporting student success, leadership competencies for academic transformation, partnerships and collaborations across campus, IT transformation, academic transformation that is broad, strategic, and institutional in scope)

ACCESSIBILITY AND UNIVERSAL DESIGN FOR LEARNING (Supporting and educating the academic community in effective practice; intersections with instructional delivery modes; compliance issues)

ADAPTIVE TEACHING AND LEARNING (Digital courseware; adaptive technology; implications for course design and the instructor’s role; adaptive approaches that are not technology-based; integration with LMS; use of data to improve learner outcomes)

COMPETENCY-BASED EDUCATION AND NEW METHODS FOR THE ASSESSMENT OF STUDENT LEARNING (Developing collaborative cultures of assessment that bring together faculty, instructional designers, accreditation coordinators, and technical support personnel, real world experience credit)

DIGITAL AND INFORMATION LITERACIES (Student and faculty literacies; research skills; data discovery, management, and analysis skills; information visualization skills; partnerships for literacy programs; evaluation of student digital competencies; information evaluation)

EVALUATING TECHNOLOGY-BASED INSTRUCTIONAL INNOVATIONS (Tools and methods to gather data; data analysis techniques; qualitative vs. quantitative data; evaluation project design; using findings to change curricular practice; scholarship of teaching and learning; articulating results to stakeholders; just-in-time evaluation of innovations). here is my bibliographical overview on Big Data (scroll down to “Research literature”: https://blog.stcloudstate.edu/ims/2017/11/07/irdl-proposal/ )

EVOLUTION OF THE TEACHING AND LEARNING SUPPORT PROFESSION (Professional skills for T&L support; increasing emphasis on instructional design; delineating the skills, knowledge, business acumen, and political savvy for success; role of inter-institutional communities of practices and consortia; career-oriented professional development planning)

FACULTY DEVELOPMENT (Incentivizing faculty innovation; new roles for faculty and those who support them; evidence of impact on student learning/engagement of faculty development programs; faculty development intersections with learning analytics; engagement with student success)

GAMIFICATION OF LEARNING (Gamification designs for course activities; adaptive approaches to gamification; alternate reality games; simulations; technological implementation options for faculty)

INSTRUCTIONAL DESIGN (Skills and competencies for designers; integration of technology into the profession; role of data in design; evolution of the design profession (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims/2017/10/04/instructional-design-3/); effective leadership and collaboration with faculty)

INTEGRATED PLANNING AND ADVISING FOR STUDENT SUCCESS (Change management and campus leadership; collaboration across units; integration of technology systems and data; dashboard design; data visualization (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims?s=data+visualization); counseling and coaching advising transformation; student success analytics)

LEARNING ANALYTICS (Leveraging open data standards; privacy and ethics; both faculty and student facing reports; implementing; learning analytics to transform other services; course design implications)

LEARNING SPACE DESIGNS (Makerspaces; funding; faculty development; learning designs across disciplines; supporting integrated campus planning; ROI; accessibility/UDL; rating of classroom designs)

MICRO-CREDENTIALING AND DIGITAL BADGING (Design of badging hierarchies; stackable credentials; certificates; role of open standards; ways to publish digital badges; approaches to meta-data; implications for the transcript; Personalized learning transcripts and blockchain technology (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims?s=blockchain) 

MOBILE LEARNING (Curricular use of mobile devices (here previous blog postings on this issue:

https://blog.stcloudstate.edu/ims/2015/09/25/mc218-remodel/; innovative curricular apps; approaches to use in the classroom; technology integration into learning spaces; BYOD issues and opportunities)

MULTI-DIMENSIONAL TECHNOLOGIES (Virtual, augmented, mixed, and immersive reality; video walls; integration with learning spaces; scalability, affordability, and accessibility; use of mobile devices; multi-dimensional printing and artifact creation)

NEXT-GENERATION DIGITAL LEARNING ENVIRONMENTS AND LMS SERVICES (Open standards; learning environments architectures (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/; social learning environments; customization and personalization; OER integration; intersections with learning modalities such as adaptive, online, etc.; LMS evaluation, integration and support)

ONLINE AND BLENDED TEACHING AND LEARNING (Flipped course models; leveraging MOOCs in online learning; course development models; intersections with analytics; humanization of online courses; student engagement)

OPEN EDUCATION (Resources, textbooks, content; quality and editorial issues; faculty development; intersections with student success/access; analytics; licensing; affordability; business models; accessibility and sustainability)

PRIVACY AND SECURITY (Formulation of policies on privacy and data protection; increased sharing of data via open standards for internal and external purposes; increased use of cloud-based and third party options; education of faculty, students, and administrators)

WORKING WITH EMERGING LEARNING TECHNOLOGY (Scalability and diffusion; effective piloting practices; investments; faculty development; funding; evaluation methods and rubrics; interoperability; data-driven decision-making)

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learning and teaching in this IMS blog
https://blog.stcloudstate.edu/ims?s=teaching+and+learning

19
Sep
2017

social media algorithms

categories: Digital literacy, social media

How algorithms impact our browsing behavior? browsing history?
What is the connection between social media algorithms and fake news?
Are there topic-detection algorithms as they are community-detection ones?
How can I change the content of a [Google] search return? Can I? 

Larson, S. (2016, July 8). What is an Algorithm and How Does it Affect You? The Daily Dot. Retrieved from https://www.dailydot.com/debug/what-is-an-algorithm/
Berg, P. (2016, June 30). How Do Social Media Algorithms Affect You | Forge and Smith. Retrieved September 19, 2017, from https://forgeandsmith.com/how-do-social-media-algorithms-affect-you/
Oremus, W., & Chotiner, I. (2016, January 3). Who Controls Your Facebook Feed. Slate. Retrieved from http://www.slate.com/articles/technology/cover_story/2016/01/how_facebook_s_news_feed_algorithm_works.html
Lehrman, R. A. (2013, August 11). The new age of algorithms: How it affects the way we live. Christian Science Monitor. Retrieved from https://www.csmonitor.com/USA/Society/2013/0811/The-new-age-of-algorithms-How-it-affects-the-way-we-live
Johnson, C. (2017, March 10). How algorithms affect our way of life. Desert News. Retrieved from https://www.deseretnews.com/article/865675141/How-algorithms-affect-our-way-of-life.html
Understanding algorithms and their impact on human life goes far beyond basic digital literacy, some experts said.
An example could be the recent outcry over Facebook’s news algorithm, which enhances the so-called “filter bubble”of information.
personalized search (https://en.wikipedia.org/wiki/Personalized_search)
Kounine, A. (2016, August 24). How your personal data is used in personalization and advertising. Retrieved September 19, 2017, from https://www.tastehit.com/blog/personal-data-in-personalization-and-advertising/
Hotchkiss, G. (2007, March 9). The Pros & Cons Of Personalized Search. Retrieved September 19, 2017, from http://searchengineland.com/the-pros-cons-of-personalized-search-10697
Magid, L. (2012). How (and why) To Turn Off Google’s Personalized Search Results. Forbes. Retrieved from https://www.forbes.com/sites/larrymagid/2012/01/13/how-and-why-to-turn-off-googles-personalized-search-results/#53a30be838f2
Nelson, P. (n.d.). Big Data, Personalization and the No-Search of Tomorrow. Retrieved September 19, 2017, from https://www.searchtechnologies.com/blog/big-data-search-personalization

gender

Massanari, A. (2017). #Gamergate and The Fappening: How Reddit’s algorithm, governance, and culture support toxic technocultures. New Media & Society, 19(3), 329-346. doi:10.1177/1461444815608807

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d121748152%26site%3dehost-live%26scope%3dsite

community detection algorithms:

Bedi, P., & Sharma, C. (2016). Community detection in social networks. Wires: Data Mining & Knowledge Discovery, 6(3), 115-135.

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dllf%26AN%3d114513548%26site%3dehost-live%26scope%3dsite

CRUZ, J. D., BOTHOREL, C., & POULET, F. (2014). Community Detection and Visualization in Social Networks: Integrating Structural and Semantic Information. ACM Transactions On Intelligent Systems & Technology, 5(1), 1-26. doi:10.1145/2542182.2542193

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3daph%26AN%3d95584126%26site%3dehost-live%26scope%3dsite

Bai, X., Yang, P., & Shi, X. (2017). An overlapping community detection algorithm based on density peaks. Neurocomputing, 2267-15. doi:10.1016/j.neucom.2016.11.019

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d120321022%26site%3dehost-live%26scope%3dsite

topic-detection algorithms:

Zeng, J., & Zhang, S. (2009). Incorporating topic transition in topic detection and tracking algorithms. Expert Systems With Applications, 36(1), 227-232. doi:10.1016/j.eswa.2007.09.013

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d34892957%26site%3dehost-live%26scope%3dsite

topic detection and tracking (TDT) algorithms based on topic models, such as LDA, pLSI (https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis), etc.

Zhou, E., Zhong, N., & Li, Y. (2014). Extracting news blog hot topics based on the W2T Methodology. World Wide Web, 17(3), 377-404. doi:10.1007/s11280-013-0207-7

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d94609674%26site%3dehost-live%26scope%3dsite

The W2T (Wisdom Web of Things) methodology considers the information organization and management from the perspective of Web services, which contributes to a deep understanding of online phenomena such as users’ behaviors and comments in e-commerce platforms and online social networks.  (https://link.springer.com/chapter/10.1007/978-3-319-44198-6_10)

ethics of algorithm

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

journalism

Malyarov, N. (2016, October 18). Journalism in the age of algorithms, platforms and newsfeeds | News | FIPP.com. Retrieved September 19, 2017, from http://www.fipp.com/news/features/journalism-in-the-age-of-algorithms-platforms-newsfeeds

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https://blog.stcloudstate.edu/ims?s=algorithm
more on algorithms in this IMS blog

see also

30
Aug
2017

NMC Horizon Report 2017 K12

categories: Digital literacy, educational technology, information technology, instructional technology

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

29
Jun
2017

workforce skills

categories: Digital literacy, information literacy, media literacy, Millennials, technology literacy

These are the top 10 workforce skills students will need by 2020

By Laura Ascione, Managing Editor, Content Services, @eSN_Laura
June 20th, 2017
a recent McGraw-Hill Education survey, just 40 percent of college seniors said they felt their college experience was helpful in preparing for a career. Alarmingly, that percentage plummeted to 19 percent for women answering the same question.
data from the nonprofit Institute for the Future, there are 6 drivers of change in today’s workforce:
1. Extreme longevity: People are living longer–by 2025 the number of Americans older than 60 will increase by 70 percent
2. The rise of smart machines and systems: Technology can augment and extend our own capabilities, and workplace automation is killing repetitive jobs
3. Computational world: Increases in sensors and processing makes the world a programmable system; data will give us the ability to see things on a scale that has never been possible
4. New media ecology: New communication tools require media literacies beyond text; visual communication media is becoming a new vernacular
5. Superstructured organizations: Social technologies drive new forms of production and value creation, and social tools are allowing organizations to work at extreme scales
6. Globally connected world: Diversity and adaptability are at the center of operations–the U.S. and Europe no longer hold a monopoly on job creation, innovation, and political power

The top 10 workforce skills of 2020 include:

1. Sense making: The ability to determine the deeper meaning or significance of what is being expressed. The Drivers: Rise of smart machines and systems

2. Social intelligence: The ability to connect to others in a deep and direct way, to sense and stimulate reactions and desired interactions. The Drivers: Rise of smart machines and systems, globally connected world

3. Novel and adaptive thinking: Proficiency at thinking and coming up with solutions and responses beyond that which is rote or rule-based. The Drivers: Rise of smart machines and systems, globally connected world

4. Cross cultural competency: The ability to operate in different cultural settings. The Drivers: Superstructured organizations, globally connected world

5. Computational thinking: The ability to translate vast amounts of data into abstract concepts and to understand data based reasoning. The Drivers: New media ecology, computational world

6. New media literacy: The ability to critically assess and develop content that uses new media forms, and to leverage these media for persuasive communication. The Drivers: Extreme longevity, new media ecology, Superstructured organizations

7. Transdisciplinary: Literacy in and ability to understand concepts across multiple disciplines. The Drivers: Extreme longevity, computational world

8. Design mindset: The ability to represent and develop tasks and work processes for desired outcomes. The Drivers: Superstructured organizations, computational world

9. Cognitive load management: The ability to discriminate and filter information for importance, and to understand how to maximize cognitive functions. The Drivers: Superstructured organizations, computational world, new media ecology

10. Virtual collaboration: The ability to work productively, drive engagement, and demonstrate presence as a member of a virtual team. The Drivers: Superstructured organizations, globally connected world

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

25
May
2017

media education differentiated instruction

categories: Digital literacy, instructional technology, media literacy

Friezem, Y. (2017). THE MEDIA PRODUCTION HIVE: USING MEDIA EDUCATION FOR DIFFERENTIATED INSTRUCTION.  Media Education, 8(1), 123-140

https://www.academia.edu/33079666/The_media_production_hive_Using_media_education_for_differentiated_instruction?auto=download

keywords: Media production, media literacy, Universal Design for Learning (UDL), executive
functioning, Media Production Hive

the theoretical framework of Universal Design for Learning (Rose & Meyer, 2002), teaching the same material via various strategies that cumulatively address needs and learning types of each student in the classroom (p. 126). acknowledge all the various types of learners in his class, such as visual learners, auditory learners, write-read learners, and kinesthetic learners, following Gardner’s (1983) multiple intelligence theory.
various ways of receiving, processing, and expressing information by different learners
various ways students can chose to engage in the process of learning
(p. 127) multiple means of representation guarantees each learner processes information in the best way they can, but it also provides repetition of the topic in various ways to deepen understanding
Students need to organize recently acquired knowledge in a strategic way and communicate their understanding to the teacher. Rose and Meyer (2002) created a detailed pathway for teachers to apply UDL using assistive technology.

Media education practices involve demystifying media messages and learning to use
media wisely through activities of evaluation, composition, introspection, and civic engagement. the links between the instructional design of lessons for all students and
the critical analysis, expression, and reflection on media messages are gradually
explored (Dalton, 2017).
Dalton, E. M. (2017). Universal design for learning: Guiding principles to reduce
barriers to digital & media literacy competence. Journal of Media Literacy Education, 9(2).

p. 128 Media production is the process of composing a message via a single or various media platforms. Media production includes creating videos, podcasts, presentations, posters, drawings, and books. With the increasing use of digital devices and applications, students are engaged in various ways to convey their messages using multiple ways of expression and multiple types of representations.

digitalempathy.net/hive

digital empathy media production hive

digital and media literacy competencies (Hobbs, 2010)

p. 137 challenges

Group dynamics often reveal power struggles among team members (Friesem, 2014). The responsibility of the media educator, who is not a mediator by training, is to find the way to mitigate the tension caused by differences among group members (Friesem, 2010). In addition, students have the tendency to use media production as a transgressive practice (Moore, 2011; Grace & Tubin, 1998). Facilitating the process of production involves constant reflection on the classroom power relationship using critical and pragmatic lenses.
Grace, D., & Tobin, J. (1998). Butt jokes and mean-teacher parodies: Video production
in the elementary classroom. In D. Buckingham (Ed.), Teaching popular culture: Beyond radical pedagogy (pp. 42-62). London, UK: University College London Press.

The discourse about the implementations of UDL with digital technology has been broad and used for several research studies (Rose & Meyer, 2002).
Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal
design for learning. Alexandria, VA: Association for Supervision and Curriculum Development (ASCD).

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more on media literacy in this IMS blog
https://blog.stcloudstate.edu/ims?s=media+literacy
more on instructional desing in this IMS blog
https://blog.stcloudstate.edu/ims?s=instructional+design

13
Apr
2017

apps for special needs students

categories: Digital literacy, e-learning, educational technology, information literacy, instructional technology, iPhone, mobile learning

Android

Categories
Apps
Android Apps for Learners with Autism
Android Apps for Learners with Dyslexia
Android Apps for Vision Impaired

iOS

Categories
Apps
Apps for Dyslexic Learners
Apps for Autistic Learners
Apps for The Visually Impaired
Apps for Learners with Writing Difficulties

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more on special ed in this IMS blog
https://blog.stcloudstate.edu/ims?s=special+education

23
Mar
2017

NMC Horizon Report 2017 Library

categories: digital identity, Digital literacy, technology literacy

NMC Horizon Report > 2017 Library Edition

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

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

p. 26 Improving Digital Literacy

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

p. 34 Embracing the need for radical change

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

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

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

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

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

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

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

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

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

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

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

new horizon report 2017 technologies

 

 

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

p. 40  Digital Scholarship Technologies

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

p. 42 Library Services Platforms

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

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

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

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

p. 46  Artificial Intelligence

https://www.semanticscholar.org/

p. 48 IoT

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

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

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

07
Mar
2017

Library Technology Conference 2017

categories: Digital literacy, educational technology, gamification, gaming, instructional technology, Millennials, student-centered learning, technology literacy

LibTechConf

http://libtechconf.org/2017schedule/
The Next Generation of Library Orientation: http://libtechconf.org/2017schedule/

Please have a link to the presentation: https://tinyurl.com/vr360lib
#LTC2017 #vrlib

Join us online, Thursday, March 16, 2:15PM via:
Adobe Connect archived recording: http://scsuconnect.stcloudstate.edu/p7qm3hg7u0h/
or via
Facebook Live: https://www.facebook.com/InforMediaServices/

Video 360 in the Library from Plamen Miltenoff

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more on LibTech conferences:
https://blog.stcloudstate.edu/ims?s=library+technology+conference

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notes from the conference

Keynote speaker: Lauren Di Monte  http://sched.co/955P 

Debating Data Science – a roundtable http://rhr.dukejournals.org/content/2017/127/133.abstract

Lauren

Lauren

how data is produced, collected and analyzed. make accessible all kind of data and info

ask good q/s and find good answers, share finding in meaningful ways. this is where digital literacy overshadows information literacy and this the fact that SCSU library does not understand; besides teaching students how to find and evaluate data, I also teach them how to communicate effectively using electronic tools.

connecting people tools and resources and making it easier for everybody. building collaborative, open and interdisciplinary

robust data computational literates. developing workshops, project and events to practice new skills. to position the library as the interdisciplinary nexus 

what are data: definition. items of information, facts, traces of content and form. higher level, conception discussion about data in terms of social effects: matadata capturing information about the world, social political and economic changes. move away the mystic conceptions about data. nothing objective about data.

the emergence of IoT – digital meets physical. cyber physical systems. smart objects driven by industry. . proliferation of sensor and device – smart devices.

what does privacy looks like ? what is netneutrality when IoT? library must restructure : collaborate across institutions about collections of data in opien and participatory ways. put IoT in the hands of make and break things (she is maker space aficionado)

make and break things hackathons – use cheap devices such as Arduino and Pi.Arduino

data literacy programs with higher level conception exploration; libraries empower the campus in data collection. data science norms, store and share data to existing repositories and even catalogs. commercial services to store and connect data, but very restrictive and this is why libraries must be involved.

linked data and dark data

linked data – draw connections around online data most of the data are locked. linked data uses metadata to link related information in ways computers can understand.

libraries take advantage of link data. link data opportunity for semantics, natural language processing etc. if hidden data is relative to our communities, it is a library responsibility to provide it. community data practitioners

dark data

massive data, which cannot be analyzed by relational processing. data not yield significant findings. might be valuable for researchers: one persons trash is another persons’ treasure. preserving data and providing access to info. collaborate with researchers across disciplines and assist decide what is worth keeping and what discarding and how to study.

rich learning experience working with lined and dark data enable fresh perspective and learning how to work with data architecture. data literacy programming.

open practices  https://www.data.gov/

in context of data is different from open source and open projects. the social side of data science . advising researchers on navigation data, ethical compilations.

open science movement .https://cos.io/ pushing beyond licences and reframe, position ourselves as collaborators

analysis and publishing ; use tools that can be shared and include data, code and executable files.

reproducibility and contestability https://www.lib.ncsu.edu/events/series/summer-of-open-science

Python and Raspberry Pi. jupitor notebook server,
she is advocating for faculty not only being the leader but the DOERs of basic fucntions, which SCSU IT is rigorously fighting to keep for themselves. The sad part is that the rest of the nation is moving in this direction and SCSU continues to sink in an old 90ish campus structure of leaving IT as the gatekeeprs to functions now widely democratized. 

public libraries: citizen science projects.

her undergrad is visual studies and her grad studies is interdisciplinary studies. only in the information school she got into science.

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social media for the library

http://sched.co/954Z

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 Library Website Redesign: Turning Awful into Awesome

http://sched.co/953o  dysfunctional committee
Here is the Facebook Live link to the session:

lib guides versus curation : https://blog.stcloudstate.edu/ims/2016/12/06/digital-curation/

crazyegg:

Putting it all together: a holistic approach to utilizing your library’s user data for making informed web design decisions  (2016 conference)

In the age of Big Data, there is an abundance of free or cheap data sources available to libraries about their users’ behavior across the many components that make up their web presence. Data from vendors, data from Google Analytics or other third-party tracking software, and data from user testing are all things libraries have access to at little or no cost. However, just like many students can become overloaded when they do not know how to navigate the many information sources available to them, many libraries can become overloaded by the continuous stream of data pouring in from these sources. This session will aim to help librarians understand 1) what sorts of data their library already has (or easily could have) access to about how their users use their various web tools, 2) what that data can and cannot tell them, and 3) how to use the datasets they are collecting in a holistic manner to help them make design decisions. The presentation will feature examples from the presenters’ own experience of incorporating user data in decisions related to design the Bethel University Libraries’ web presence.

http://tinyurl.com/jbchapf

Website redesign presentation

 

 

 

 

 

 

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Making Something Out of Nothing: Building Digital Humanities Partnerships

Facebook Live https://www.facebook.com/InforMediaServices/videos/1136610906449405/
what is digital humanities?
3 questions to rotate the conversation around them
bit.ly/DHLibQ1
https://omeka.org/ 
bit.ly/DHLibQ2
group discussions
silos, IT barrier, focusing on student success, retention, server space is cheap, if
promotion and tenure for faculty can include incentive to work with the librarian
being perceived as a “no” person
http://dhdebates.gc.cuny.edu/debates/part/6 
https://www.insidehighered.com/blogs/technology-and-learning/6-questions-digital-humanities-librarian
How can we can help one another build partnerships within and across institutions?
bit.ly/DHLibQ3
Midwest consortium for grants among liberal colleges to distribute support
http://www.ala.org/acrl/aboutacrl/directoryofleadership/interestgroups/acr-igdh 
http://lists.ala.org/sympa/info/acrldigitalhumanitiesig
https://slack.com/signin
http://chrisalensula.org/digital-humanities-and-libraries-a-conceptual-model/
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PALNI Code School: Developing Skills for the Future of Librarianship

lack of fear, changing the mindset.
deep collaboration both within and cross-consortia
don’t rely on vendor solutions. changing mindset
development = oppty (versus development as “work”)
private higher education is PALNI
https://www.facebook.com/PALNIConsortium/
badges CSS and Java Script generate links, which can be used for the course
server is external so it is independent from IT constrains.
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March 16

Keynote Speaker: Patrick Meier

Facebook Live session: https://www.facebook.com/InforMediaServices/videos/1137175723059590/

technology is not dehumanizing, but on the opposite, it is an extend of humanity and humanitarian aid
DH Network (digital humanitarian network)  http://digitalhumanitarians.com/
keyone speaker meier

3d virtual picture of disastrous areas. unlock the digital information to be digitally accessible to all people who might be interested.
they opened the maps of Katmandu for the local community and they were coming up with the strategies to recover. democracy in action

http://werobotics.org/

mountain tsunami: http://www.natgeotv.com/uk/seconds-from-disaster/videos/mountain-tsunami

i can’t stop thinking that the keynote speaker efforts are mere follow up of what Naomi Klein explains in her Shock Doctrine: http://www.naomiklein.org/shock-doctrine: a government country seeks reasons to destroy another country or area and then NGOs from the same country go to remedy the disasters

A question from a librarian from the U about the use of drones. My note: why did the SCSU library have to give up its drone? 

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http://sched.co/953l

Building an Ebook Platform from Scratch: Are You Daft?

https://www.theguardian.com/books/2017/mar/14/ebook-sales-continue-to-fall-nielsen-survey-uk-book-sales
Douglas County Library model. too resource intensive to continue
Marmot Library Network
ILS integrated library system – shared with other counties, same sever for the entire consortium. they have a programmer, viewfind, open source, discovery player, he customized viewfind community to viewfind plus. instead of using the ILS public access catalogue, they are using the Vufind interface
Caiifa Enki. public library – single access collection. they purchase ebooks from the publisher and they are using also the viewfind interface. but not integrated with the library catalogs.  Kansas public library went from OverDrive to Viewfind. CA State library is funding for the time being this effort.
VuFInd, runs PhP. Adobe Content Server (not much documentation, Media Server, sftp sever, DRM is needed. https://en.wikipedia.org/wiki/Digital_rights_management
technical infrastructure
publisher agreement needed
metadata is a big issue. it can come in any format – spreadsheet, HTML and need to be able to convert into MARC
Amigos is a consortium of schools, but also academic and public. small public library could not handle the spreadsheet
authentication – SIP https://en.wikipedia.org/wiki/Session_Initiation_Protocol
types of content – publisher will not understand issue, which clear for librarians
PDF and epub formats
purchase content –
title by title selection – academia is tired of selections. although it is intended to buy also collections
library – owned ( and shared collections)
host content from libraries – papers in academic lib, genealogy in pub lib.

options in license models .
e resource content. not only ebooks, after it is taken care of, add other types of digital objects.

instead of replicate, replacement of the commercial aggregators,

Amigos Shelf interface is the product of the presenter

instead of having a young reader collection as SCSU has on the third floor, an academic library is outsourcing through AMigos shelf ebooks for young readers

purchasing marketing was built from scratch on PhP. https://laravel.com/

Harper Collins is too cumbersome and the reason to avoid working with them.

security issues. some of the material sent over ftp and immediately moved to sftp

decisions – use of internal resources only, if now – amazon
programmer used for the pilot. contracted programmers. lack of the ability to see the large picture. eventually hired a full time person, instead of outsourcing. RDA compliant MARC.
ONIX, spreadsheet MARC.
Decision about who to start with : public or academic.
attempt to keep pricing down –
own agreement with the customers, separate from the agreement with the Publisher

current development: web-based online reading, shared-consortial collections and SIP2 authentication

new CIO closed the project.

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Tutorials

http://bit.ly/LTC2017SVC
https://docs.google.com/presentation/d/1FnlTJVdu4KkvjB21NXp82zHCYwQfmwpzi_dlZp4AgYU/edit#slide=id.p

http://bit.ly/LTC2017tutorial2

18
Aug
2016

digital humanities and libraries

categories: announcement, Library and information science

The deadline for proposals has been extended to September 9th, 2016. Thank you.

THE DIGITAL HUMANITIES: IMPLICATIONS FOR LIBRARIANS, LIBRARIES, AND LIBRARIANSHIP

The redefinition of humanities scholarship has received major attention in higher education over the past few years. The advent of digital humanities has challenged many aspects of academic librarianship. With the acknowledgement that librarians must be a necessary part of this scholarly conversation, the challenges facing subject/liaison librarians, technical service librarians, and library administrators are many. Developing the knowledge base of digital tools, establishing best procedures and practices, understanding humanities scholarship, managing data through the research lifecycle, teaching literacies (information, data, visual) beyond the one-shot class, renegotiating the traditional librarian/faculty relationship as ‘service orientated,’ and the willingness of library and institutional administrators to allocate scarce resources to digital humanities projects while balancing the mission and priorities of their institutions are just some of the issues facing librarians as they reinvent themselves in the digital humanities sphere.

College & Undergraduate Libraries, a peer-reviewed journal published by Taylor & Francis, invites proposals for articles to be published in the fall of 2017. The issue will be co-edited by Kevin Gunn (gunn@cua.edu) of the Catholic University of America and Jason Paul (pauljn@stolaf.edu) of St. Olaf College.

The issue will deal with the digital humanities in a very broad sense, with a major focus on their implications for the roles of academic librarians and libraries as well as on librarianship in general. Possible article topics include, but are not limited to, the following themes, issues, challenges, and criticism:

  • Developing the project development mindset in librarians
  • Creating new positions and/or cross-training issues for librarians
  • Librarian as: point-of-service agent, an ongoing consultant, or as an embedded project librarian
  • Developing managerial and technological competencies in librarians
  • Administration support (or not) for DH endeavors in libraries
  • Teaching DH with faculty to students (undergraduate and graduate) and faculty
  • Helping students working with data
  • Managing the DH products of the data life cycle
  • Issues surrounding humanities data collection development and management
  • Relationships of data curation and digital libraries in DH
  • Issues in curation, preservation, sustainability, and access of DH data, projects, and products
  • Linked data, open access, and libraries
  • Librarian and staff development for non-traditional roles
  • Teaching DH in academic libraries
  • Project collaboration efforts with undergraduates, graduate students, and faculty
  • Data literacy for librarians
  • The lack of diversity of librarians and how it impacts DH development
  • Advocating and supporting DH across the institution
  • Developing institutional repositories for DH
  • Creating DH scholarship from the birth of digital objects
  • Consortial collaborations on DH projects
  • Establishing best practices for DH labs, networks, and services
  • Assessing, evaluating, and peer reviewing DH projects and librarians.

Articles may be theoretical or ideological discussions, case studies, best practices, research studies, and opinion pieces or position papers.

Proposals should consist of an abstract of up to 500 words and up to six keywords describing the article, together with complete author contact information. Articles should be in the range of 20 double-spaced pages in length. Please consult the following link that contains instructions for authors: http://www.tandfonline.com/action/authorSubmission?journalCode=wcul20&page=instructions#.V0DJWE0UUdU.

Please submit proposals to Kevin Gunn (gunn@cua.edu) by September 9th, 2016; please do not use Scholar One for submitting proposals. First drafts of accepted proposals will be due by February 1, 2017 with the issue being published in the fall of 2017. Feel free to contact the editors with any questions that you may have.

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more on digital humanities in this IMS blog:

https://blog.stcloudstate.edu/ims?s=digital+humanities

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