Searching for "hybrid"

topics for IM260

proposed topics for IM 260 class

  • Media literacy. Differentiated instruction. Media literacy guide.
    Fake news as part of media literacy. Visual literacy as part of media literacy. Media literacy as part of digital citizenship.
  • Web design / web development
    the roles of HTML5, CSS, Java Script, PHP, Bootstrap, JQuery, React and other scripting languages and libraries. Heat maps and other usability issues; website content strategy. THE MODEL-VIEW-CONTROLLER (MVC) design pattern
  • Social media for institutional use. Digital Curation. Social Media algorithms. Etiquette Ethics. Mastodon
    I hosted a LITA webinar in the fall of 2016 (four weeks); I can accommodate any information from that webinar for the use of the IM students
  • OER and instructional designer’s assistance to book creators.
    I can cover both the “library part” (“free” OER, copyright issues etc) and the support / creative part of an OER book / textbook
  • Big Data.” Data visualization. Large scale visualization. Text encoding. Analytics, Data mining. Unizin. Python, R in academia.
    I can introduce the students to the large idea of Big Data and its importance in lieu of the upcoming IoT, but also departmentalize its importance for academia, business, etc. From infographics to heavy duty visualization (Primo X-Services API. JSON, Flask).
  • NetNeutrality, Digital Darwinism, Internet economy and the role of your professional in such environment
    I can introduce students to the issues, if not familiar and / or lead a discussion on a rather controversial topic
  • Digital assessment. Digital Assessment literacy.
    I can introduce students to tools, how to evaluate and select tools and their pedagogical implications
  • Wikipedia
    a hands-on exercise on working with Wikipedia. After the session, students will be able to create Wikipedia entries thus knowing intimately the process of Wikipedia and its information.
  • Effective presentations. Tools, methods, concepts and theories (cognitive load). Presentations in the era of VR, AR and mixed reality. Unity.
    I can facilitate a discussion among experts (your students) on selection of tools and their didactically sound use to convey information. I can supplement the discussion with my own findings and conclusions.
  • eConferencing. Tools and methods
    I can facilitate a discussion among your students on selection of tools and comparison. Discussion about the their future and their place in an increasing online learning environment
  • Digital Storytelling. Immersive Storytelling. The Moth. Twine. Transmedia Storytelling
    I am teaching a LIB 490/590 Digital Storytelling class. I can adapt any information from that class to the use of IM students
  • VR, AR, Mixed Reality.
    besides Mark Gill, I can facilitate a discussion, which goes beyond hardware and brands, but expand on the implications for academia and corporate education / world
  • IoT , Arduino, Raspberry PI. Industry 4.0
  • Instructional design. ID2ID
    I can facilitate a discussion based on the Educause suggestions about the profession’s development
  • Microcredentialing in academia and corporate world. Blockchain
  • IT in K12. How to evaluate; prioritize; select. obsolete trends in 21 century schools. K12 mobile learning
  • Podcasting: past, present, future. Beautiful Audio Editor.
    a definition of podcasting and delineation of similar activities; advantages and disadvantages.
  • Digital, Blended (Hybrid), Online teaching and learning: facilitation. Methods and techniques. Proctoring. Online students’ expectations. Faculty support. Asynch. Blended Synchronous Learning Environment
  • Gender, race and age in education. Digital divide. Xennials, Millennials and Gen Z. generational approach to teaching and learning. Young vs old Millennials. Millennial employees.
  • Privacy, [cyber]security, surveillance. K12 cyberincidents. Hackers.
  • Gaming and gamification. Appsmashing. Gradecraft
  • Lecture capture, course capture.
  • Bibliometrics, altmetrics
  • Technology and cheating, academic dishonest, plagiarism, copyright.

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





intellectual property

When:  October 24, 2017    2:00-3:00pm
Where: Adobe Connect meeting:  https://webmeeting.minnstate.edu/oercommunityconversations

Who: Karen Pikula, Psychology faculty, Central Lakes College, and Minnesota State OER Faculty Development Coordinator

Special Guest: Gary Hunter System Director for Intellectual Property

Questions?  

Feel free to contact Kimberly Johnson, Director of Faculty and Instructional Development at kimberly.johnson@minnstate.edu or Karen Pikula, Minnesota State OER Faculty Development Coordinator, at karen.pikula@minnstate.edu.

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notes from the webinar

Gary Hunter. copyright. movies, public performance rights, youtube videos. up

the compliance of the terms of service of the web site. Contract law. copyright law. system procedure – copyright clearance, clearing the copyright means using it without violating the copyright law.

clearing copyright:

  • determine if materials are or are not protected
  • use your own original materials
  • perform fair use analysis with fair use checklist to usitify use
  • use in compliance with sections 110 (1) & (2) of copyright act
  • use materials avaialble through an open or CC license
  • get permission (letter, email, subscription, license, etc.)

http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/forms.html 

8 categories of copyright works

establishing copyright. eligibility requirements;

  • fixation
  • originality
  • minimal creativity

when these three criteria met, copyright arises automatically.

registering a copyright https://www.copyright.gov/ . $35. 70 years for individuals and 95 for corporations or 210 years

not protected by copyright

  • public domain (expired copyright/donated)
  • federal gov publications and web site info
  • works typically registered as a trademark
    • tag lines and slogans
      • just do it – nike 1988
      • got milk – 1993
  • math equations and formulas
  • recipes
  • blank forms
  • phone books

copyright holder exclusive rights

  1. make copies of the work
  2. prepare derivative works
  3. distribute copies
  4. perform the work – performing live (band concert); pre-record audio visual of the same items. DVD play of a movie is considered “performing”
  5. display the work

legality vs reality

legality – activity may be copyright infringement from a legal point of view.

reality – tolerated or ignored by the copyright holder for various reasons

limitations on copyright

  • fair use (#107). librarians use it a lot to copy. using copyright works in F2F teaching, scholarship, research and other non-profit ed purposes.
    1. criticism, comment, news reporting, teaching, scholarship, research

four factors to consider (not educational exception) ; it is a four part test to apply: 1. purpose and character if tge yse 2. nature of the copyirghted work (e.g. factual v creative) 3. amount
http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/docs/Fair_Use_Checklist1.pdf

http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/forms.html

fair use >> . transformation: 1. add / subtract from original 2. use for different purpose; >> parody songs – using enough of music and words to recognize the song, but not enough to it to be copyright infrigement. memes.

students’ use of copyrighted works. students may: use the entire copyrighted work but not publish openly

copyright act #110 (1) applies to F2F teaching.

copyright act #110 (2) applies to Hybrid/Online teaching. exception one digital copy can made and uploaded on D2L. reasonable and limited portions of dramatic musical or audiovisual works

http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/forms.html

personal use v public performance.

if people identifiable ask them to sign a media release form

plagiarism v copyright infringement.

Creative Commons (CC). search engine for content available through cc licenses. https://creativecommons.org/ CC BY – attribution needed; CC BY-SA may remix, tweak CC BY-ND can redistribute, but not alter CC BY-NC for non profit. CC BY-NC-SA

copyright questions

book chapters: one is a rule of thumb
PDF versions of the eassays textbook acceptable, if the students purchased it

music performance licenses: usually cover – educational activities on campus; ed activities at off-campus locations that are outreach

music licenses: BMI, ASCAP, SESAC

#201. Ownership of Copyright. Student ownership http://www.minnstate.edu/system/asa/academicaffairs/policy/copyright/forms.html

MnSCU board policy 3.26 intellectual policy. part 4, subpart A: institutional works; scholarly works; personal works; student works. MnSCU board policy 3.27.1: copyright clearance.

Gary.Hunter@so.mnscu.edu

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

online learning attitudes

online learning attitudes

Students match their preference for hybrid learning with a belief that it is the most effective learning environment for them.

Despite the fact that faculty prefer teaching in a hybrid environment, they remain skeptical of online learning. Nearly half do not agree online 45% learning is effective.

https://library.educause.edu/~/media/files/library/2017/9/studentst2017infog.pdf

 

Students asked what technologies they wish their instructors used more, and we asked faculty what technologies they think could make them more effective instructors. Both agree that content and resource-focused technologies should be incorporated more and social media and tablets should be incorporated less.

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more on the use (or not) of ed technology in the classroom in this IMS blog
http://blog.stcloudstate.edu/ims/2017/04/03/use-of-laptops-in-the-classroom/

Embedded Librarianship in Online Courses

Embedded Librarianship in Online Courses

Instructor: Mimi O’Malley Dates: October 2nd to 27th, 2017

http://libraryjuiceacademy.com/081-embedded-online.php

Learning outcomes:

  • Discuss ways to incorporate library services through the learning management system level.
  • Examine bibliographic instruction in the virtual classroom through team teaching, guest lecturing.
  • Identify librarian roles during the design and development of online courses.
  • Assessing embedded librarianship efforts.

Mimi O’Malley is the learning technology translation strategist at Spalding University. She helps faculty prepare course content for hybrid and fully online courses in addition to incorporating open education resources into courses. She previously wrote and facilitated professional development courses and workshops at the Learning House, Inc. Mimi has presented workshops on online learning topics including assessment, plagiarism, copyright, and curriculum trends at the Learning House, Inc. CONNECT Users Conference, SLOAN-C ALN, Pencils and Pixels and New Horizons Teaching & Learning Conference. Interview with Mimi O’Malley

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more on embedded librarian in this IMS blog
http://blog.stcloudstate.edu/ims?s=embedded+librarian

Scopus webinar

Scopus Content: High quality, historical depth and expert curation

Bibliographic Indexing Leader

Register for the September 28th webinar

https://www.brighttalk.com/webcast/13703/275301

metadata: counts of papers by yer, researcher, institution, province, region and country. scientific fields subfields
metadata in one-credit course as a topic:

publisher – suppliers =- Elsevier processes – Scopus Data

h-index: The h-index is an author-level metric that attempts to measure both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist’s most cited papers and the number of citations that they have received in other publications.

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https://www.brighttalk.com/webcast/9995/275813

Librarians and APIs 101: overview and use cases
Christina Harlow, Library Data Specialist;Jonathan Hartmann, Georgetown Univ Medical Center; Robert Phillips, Univ of Florida

https://zenodo.org/

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Slides | Research data literacy and the library from Library_Connect

 The era of e-science demands new skill sets and competencies of researchers to ensure their work is accessible, discoverable and reusable. Librarians are naturally positioned to assist in this education as part of their liaison and information literacy services.

Research data literacy and the library

Christian Lauersen, University of Copenhagen; Sarah Wright, Cornell University; Anita de Waard, Elsevier

https://www.brighttalk.com/webcast/9995/226043

Data Literacy: access, assess, manipulate, summarize and present data

Statistical Literacy: think critically about basic stats in everyday media

Information Literacy: think critically about concepts; read, interpret, evaluate information

data information literacy: the ability to use, understand and manage data. the skills needed through the whole data life cycle.

Shield, Milo. “Information literacy, statistical literacy and data literacy.” I ASSIST Quarterly 28. 2/3 (2004): 6-11.

Carlson, J., Fosmire, M., Miller, C. C., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research faculty. Portal: Libraries & the Academy, 11(2), 629-657.

data information literacy needs

embedded librarianship,

Courses developed: NTRESS 6600 research data management seminar. six sessions, one-credit mini course

http://guides.library.cornell.edu/ntres6600
BIOG 3020: Seminar in Research skills for biologists; one-credit semester long for undergrads. data management organization http://guides.library.cornell.edu/BIOG3020

lessons learned:

  • lack of formal training for students working with data.
  • faculty assumed that students have or should have acquired the competencies earlier
  • students were considered lacking in these competencies
  • the competencies were almost universally considered important by students and faculty interviewed

http://www.datainfolit.org/

http://www.thepress.purdue.edu/titles/format/9781612493527

ideas behind data information literacy, such as the twelve data competencies.

http://blogs.lib.purdue.edu/dil/the-twelve-dil-competencies/

http://blogs.lib.purdue.edu/dil/what-is-data-information-literacy/

Johnston, L., & Carlson, J. (2015). Data Information Literacy : Librarians, Data and the Education of a New Generation of Researchers. Ashland: Purdue University Press.  http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dnlebk%26AN%3d987172%26site%3dehost-live%26scope%3dsite

NEW ROLESFOR LIbRARIANS: DATAMANAgEMENTAND CURATION

the capacity to manage and curate research data has not kept pace with the ability to produce them (Hey & Hey, 2006). In recognition of this gap, the NSF and other funding agencies are now mandating that every grant proposal must include a DMP (NSF, 2010). These mandates highlight the benefits of producing well-described data that can be shared, understood, and reused by oth-ers, but they generally offer little in the way of guidance or instruction on how to address the inherent issues and challenges researchers face in complying. Even with increasing expecta-tions from funding agencies and research com-munities, such as the announcement by the White House for all federal funding agencies to better share research data (Holdren, 2013), the lack of data curation services tailored for the “small sciences,” the single investigators or small labs that typically comprise science prac-tice at universities, has been identified as a bar-rier in making research data more widely avail-able (Cragin, Palmer, Carlson, & Witt, 2010).Academic libraries, which support the re-search and teaching activities of their home institutions, are recognizing the need to de-velop services and resources in support of the evolving demands of the information age. The curation of research data is an area that librar-ians are well suited to address, and a num-ber of academic libraries are taking action to build capacity in this area (Soehner, Steeves, & Ward, 2010)

REIMAgININg AN ExISTINg ROLEOF LIbRARIANS: TEAChINg INFORMATION LITERACY SkILLS

By combining the use-based standards of information literacy with skill development across the whole data life cycle, we sought to support the practices of science by develop-ing a DIL curriculum and providing training for higher education students and research-ers. We increased ca-pacity and enabled comparative work by involving several insti-tutions in developing instruction in DIL. Finally, we grounded the instruction in the real-world needs as articu-lated by active researchers and their students from a variety of fields

Chapter 1 The development of the 12 DIL competencies is explained, and a brief compari-son is performed between DIL and information literacy, as defined by the 2000 ACRL standards.

chapter 2 thinking and approaches toward engaging researchers and students with the 12 competencies, a re-view of the literature on a variety of educational approaches to teaching data management and curation to students, and an articulation of our key assumptions in forming the DIL project.

Chapter 3 Journal of Digital Curation. http://www.ijdc.net/

http://www.dcc.ac.uk/digital-curation

http://blog.stcloudstate.edu/ims/2017/10/19/digital-curation-2/

http://blog.stcloudstate.edu/ims/2016/12/06/digital-curation/

chapter 4 because these lon-gitudinal data cannot be reproduced, acquiring the skills necessary to work with databases and to handle data entry was described as essential. Interventions took place in a classroom set-ting through a spring 2013 semester one-credit course entitled Managing Data to Facilitate Your Research taught by this DIL team.

chapter 5 embedded librar-ian approach of working with the teaching as-sistants (TAs) to develop tools and resources to teach undergraduate students data management skills as a part of their EPICS experience.
Lack of organization and documentation presents a bar-rier to (a) successfully transferring code to new students who will continue its development, (b) delivering code and other project outputs to the community client, and (c) the center ad-ministration’s ability to understand and evalu-ate the impact on student learning.
skill sessions to deliver instruction to team lead-ers, crafted a rubric for measuring the quality of documenting code and other data, served as critics in student design reviews, and attended student lab sessions to observe and consult on student work

chapter 6 Although the faculty researcher had created formal policies on data management practices for his lab, this case study demonstrated that students’ adherence to these guidelines was limited at best. Similar patterns arose in discus-sions concerning the quality of metadata. This case study addressed a situation in which stu-dents are at least somewhat aware of the need to manage their data;

chapter 7 University of Minnesota team to design and implement a hybrid course to teach DIL com-petencies to graduate students in civil engi-neering.
stu-dents’ abilities to understand and track issues affecting the quality of the data, the transfer of data from their custody to the custody of the lab upon graduation, and the steps neces-sary to maintain the value and utility of the data over time.

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

faculty support group meetings

The lecture-capture / course-capture group meets on October 5, 2017, 3PM in Miller Center 205:
http://blog.stcloudstate.edu/coursecapture/2017/09/12/fall-2017-meeting-support-group/

The online/hybrid teaching group meeting will be determined after taking this poll:
https://doodle.com/poll/cq3g5zpei6dfwcme

(as per blog announcement: http://blog.stcloudstate.edu/blendedonline/2017/09/12/first-meeting-for-fall-2017/)

blended learning implementation

Critical Factors for Implementing Blended Learning in Higher Education.

Available from: https://www.researchgate.net/publication/318191000_Critical_Factors_for_Implementing_Blended_Learning_in_Higher_Education [accessed Jul 6, 2017].

Definition of Blended learning

Blended learning is in one dimension broadly defined as “The convergence of online and face-to-face Education” as in the study by Watson (2008). At the same time it is important to also include the dimension of technology and media use as it has been depicted in the multimodal conceptual model in Figure 1 below. This conceptual model was proposed and presented in an article published by Picciano (2009). Critical Factors for Implementing Blended Learning in Higher Education.

 

online face to face hybrid

 

 

 

Several studies that argue for the need to focus on pedagogy and learning objectives and not solely on technology (Hoffinan, 2006; Garrison & Vaughan, 2008; Al amm ary et al., 2014; McGee & Reis, 2012; Shand, Glassett Farrelly & Costa, 2016). Other findings in this study are that technology still is a critical issue (So & Brush, 2008; Fleming, Becker & Newton, 2017), not least in developing regions (AI Busaidi & Al-Shihi, 2012; Raphae1 & Mtebe, 2016), and also the more positive idea of technology as a supporting factor for innovative didactics and instructional design to satisfy the needs in heterogeneous student groups (Picciano, 2009). Critical Factors for Implementing Blended Learning in Higher Education.

Critical factors:

  1. technology
  2. didactics –  pedagogy, instructional design and the teacher role
  3. Course outcomes – learning outcomes and learner satisfaction
  4. collaboration and social presence
  5. course design
  6. the heritage from technology enhanced distance courses
  7. multimodal overload
  8. trends and hypes
  9. economy

Blended learning perspectives

  1. the university perspective
  2. the Learner perspective
  3. the Teacher perspective
  4. the Global perspective

 

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more on blended learning in this IMS blog
http://blog.stcloudstate.edu/ims?s=blended+learning

Blended Synchronous Learning Environment

Wang, Q., Quek, C., & Hu, H. (2017). Designing and Improving a Blended Synchronous Learning Environment : An Educational Design Research. International Review of Research in Open and Distributed Learning, 18(3), 99-118

http://www.irrodl.org/index.php/irrodl/article/view/3034/4142

Definition: blended synchronous learning has attracted much attention and it is often labelled with synchronous hybrid learning (Cain & Henriksen 2013); synchronous blended learning (Okita, 201 3 ); multi – access learning (Irvine, Code, & Richards, 2013); or simultaneous delivery of course s to on – campus and off – campus students (White et al ., 2010). Adapted from the definition given by Bower , Dalgarno, Kennedy, Lee, and Kenney (2015), blended synchronous learning in this paper is defined as a learning method that enables online students to participate in classroom learning activities simultaneously via comput er – mediated communication technologies such as video conferencing . By following this approach , on – campus students attend F2F le ssons in the physical classroom. M eanwhile, online students who are situated at multiple sites participate in the identical class room learning activities via two – way video conferencing in real time .

With regard to  educational benefits , blended synchronous learning can help to establish rich teaching presence, social presence, and cognitive presence ( Garrison, Anderson, & Archer, 200 0 ; Szeto, 2015 ). A BSLE provides a mimic classroom environment (White et al. , 2010) , where teachers ’ direct instruction and facilitation can be easily carried out a nd the teaching presence is hence naturally established.

TPR presentation

Presentation to TPR (Technology and Pedagogy Roundtable), April 19, 2017
WSB 335 | short link: http://tinyurl.com/tprIMS

My name is Plamen Miltenoff and I am faculty (http://web.stcloudstate.edu/pmiltenoff/faculty/) with InforMedia Services (http://blog.stcloudstate.edu/ims/free-tech-instruction/):

https://www.facebook.com/InforMediaServices/
https://twitter.com/SCSUtechinstruc
https://plus.google.com/u/0/115966710162153290760

Through the years, I am working with the application of educational technologies in the curriculum process.

During my work and research, I notice an important discussion in the community of higher education:

http://blog.stcloudstate.edu/ims/2017/04/03/use-of-laptops-in-the-classroom/

The topic of the use of electronic devices, being that laptops, and more recently smartphones, tablets 2in1 laptops (or hybrid laptops) has been a disputable issue among instructors.

Under the tutelage of TPR, I am offering to facilitate a campus-wide discussion on the use of electronic devices in the classroom. The short-range goal of such discussion is to provide a platform for SCSU instructors to share their pedagogical experience in handling the use of electronic devices in the classroom.

The long-range goal of such discussion will be to start a conversation among SCSU faculty about the didactic of educational technology; going beyond just learning technology and start building practices for successful use of technology for teaching and learning.

 

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