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Can We Please Stop Talking About Generations as if They Are a Thing?

Millennials are not all narcissists and boomers are not inherently selfish. The research on generations is flawed.
DAVID COSTANZA
APRIL 13, 2018 9:00 AM

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SIVA VAIDHYANATHAN, 2008. https://www.chronicle.com/article/Generational-Myth/32491 Generational Myth
My note: Siva raised this issue from a sociologist point of view as soon as in 2008. Before him, Prensky’s “digitally natives” ideas was already criticized.
Howe and Strauss; Millennials books contributed to the overgeneralizations. https://en.wikipedia.org/wiki/Strauss%E2%80%93Howe_generational_theory
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We spend a lot of time debating the characteristics of generations—are baby boomers really selfish and entitledare millennials really narcissists, and the latest, has the next generation (whatever it is going to be called) already been ruined by cellphones? Many academics—and many consultants—argue that generations are distinct and that organizations, educators, and even parents need to accommodate them. These classifications are often met with resistance from those they supposedly represent, as most people dislike being represented by overgeneralizations, and these disputes only fuel the debate around this contentious topic.

In short, the science shows that generations are not a thing.

It is important to be clear what not a thing means. It does not mean that people today are the same as people 80 years ago or that anything else is static. Times change and so do people. However, the idea that distinct generations capture and represent these changes is unsupported.

What is a generation? Those who promote the concept define it as a group of people who are roughly the same age and who were influenced by a set of significant events. These experiences supposedly create commonalities, making those in the group more similar to each other and more different from other groups now and from groups of the same age in the past.

In line with the definition, there is a commonly held perception that people growing up around the same time and in the same place must have some sort of universally shared set of experiences and characteristics. It helps that the idea of generations intuitively makes sense. But the science does not support it. In fact, most of the research findings showing distinct generations are explained by other causes, have serious scientific flaws, or both.

For example, millennials score lower on job satisfaction than Gen Xers, but are millennials really a less satisfied generation? Early in their careers, Xers were also less satisfied than baby boomers.

Numerous booksarticles, and pundits have claimed that millennials are much more narcissistic than young people in the past.
on average, millennials are no more narcissistic now than Xers or boomers were when they were in their 20s, and one study has even found they might be less so than generations past. While millennials today may be more narcissistic than Xers or boomers are today, that is because young people are pretty narcissistic regardless of when they are young. This too is an age effect.

Final example. Research shows that millennials joining the Army now show more pride in their service than boomers or Xers did when they joined 20-plus years ago. Is this a generational effect? Nope. Everyone in the military now shows more pride on average than 20 years ago because of 9/11. The terrorist attack increased military pride across the board. This is known as a period effect and it doesn’t have anything to do with generations.

Another problem—identifying true generational effects is methodologically very hard. The only way to do it would be to collect data from multiple longitudinal panels. Individuals in the first panel would be measured at the start of the study and then in subsequent years with new panels added every year thereafter, allowing assessment of whether people were changing because they were getting older (age effects), because of what was happening around them (period effects), or because of their generation (cohort effects). Unfortunately, such data sets pretty much do not exist. Thus, we’re never really able to determine why a change occurred.

According to one national-culture model, people from the United States are, on average, relatively individualistic, indulgent, and uncomfortable with hierarchical order.
My note: RIchard Nisbett sides with Hofstede and Minkov: http://blog.stcloudstate.edu/ims/2016/06/14/cultural-differences/
Conversely, people from China are generally group-oriented, restrained, and comfortable with hierarchy. However, these countries are so large and diverse that they each have millions of individuals who are more similar to the “averages” of the other country than to their own.

Given these design and data issues, it is not surprising that researchers have tried a variety of different statistical techniques to massage (aka torture) the data in an attempt to find generational differences. Studies showing generational differences have used statistical techniques like analysis of variance (ANOVA) and cross-temporal meta-analysis (CTMA), neither of which is capable of actually attributing the differences to generations.

The statistical challenge derives from the problem we have already raised—generations (i.e., cohorts) are defined by age and period. As such, mathematically separating age, period, and cohort effects is very difficult because they are inherently confounded with one another. Their linear dependency creates what is known as an identification problem, and unless one has access to multiple longitudinal panels like I described above, it is impossible to statistically isolate the unique effect of any one factor.

First, relying on flawed generational science leads to poor advice and bad decisions. An analogy: Women live longer than men, on average. Why? They engage in fewer risky behaviors, take better care of themselves, and have two X chromosomes, giving them backups in case of mutations. But if you are a man and you go to the doctor and ask how to live longer, she doesn’t tell you, “Be a woman.” She says eat better, exercise, and don’t do stupid stuff. Knowing the why guides the recommendation.

Now imagine you are a manager trying to retain your supposedly job-hopping, commitment-averse millennial employees and you know that Xers and boomers are less likely to leave their jobs. If you are that manager, you wouldn’t tell your millennial employees to “be a boomer” or “grow older” (nor would you decide to hire boomers or Xers rather than millennials—remember that individuals vary within populations). Instead, you should focus on addressing benefits, work conditions, and other factors that are reasons for leaving.

Second, this focus on generational distinctions wastes resources. Take the millennials-as-commitment-averse-job-hoppers stereotype. Based on this belief, consultants sell businesses on how to recruit and retain this mercurial generation. But are all (or even most) millennials job-hopping commitment avoiders? Survey research shows that millennials and Xers at the same point in their careers are equally likely to stay with their current employer for five or more years (22 percent v. 21.8 percent). It makes no sense for organizations to spend time and money changing HR policies when employees are just as likely to stick around today as they were 15 years ago.

Third, generations perpetuate stereotyping. Ask millennials if they are narcissistic job-hoppers and most of them will rightly be offended. Treat boomers like materialistic achievement seekers and see how it affects their work quality and commitment. We finally are starting to recognize that those within any specific group of people are varied individuals, and we should remember those same principles in this context too. We are (mostly) past it being acceptable to stereotype and discriminate against women, minorities, and the disabled. Why is it OK to do so to millennials or boomers?

The solutions are fairly straightforward, albeit challenging, to implement. To start, we need to focus on the why when talking about whether groups of people differ. The reasons why any generation should be different have only been generally discussed, and the theoretical mechanism that supposedly creates generations has not been fully fleshed out.

Next, we need to quit using these nonsensical generations labels, because they don’t mean anything. The start and end years are somewhat arbitrary anyway. The original conceptualization of social generations started with a biological generational interval of about 20 years, which historians, sociologists and demographers (for one example, see Strauss and Howe, 1991) then retrofitted with various significant historical events that defined the period.

The problem with this is twofold. First, such events do not occur in nice, neat 20-year intervals. Second, not everyone agrees on what the key events were for each generation, so the start and end dates also move around depending on what people think they were. One review found that start and end dates for boomers, Xers, and millennials varied by as many as nine years, and often four to five, depending on the study and the researcher. As with the statistical problem, how can distinct generations be a thing if simply defining when they start and when they end varies so much from study to study?

In the end, the core scientific problem is that the pop press, consultants, and even some academics who are committed to generations don’t focus on the whys. They have a vested interest in selling the whats (Generation Me has reportedly sold more than 115,000 copies, and Google “generations consultants” and see how many firms are dedicated to promulgating these distinctions), but without the science behind them, any prescriptions are worthless or even harmful

David Costanza is an associate professor of organizational sciences at George Washington University and a senior consortium fellow for the U.S. Army Research Institute. He researches, teaches, and consults in the areas of generations, leadership, culture, and organizational performance.

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more on the topic in this IMS blog
http://blog.stcloudstate.edu/ims?s=millennials

publish metrics ranking and citation info

EdTech Research – Where to Publish, How to Share (Part 2): Journal Metrics, Rankings and Citation Information

EdTech Research – Where to Publish, How to Share (Part 1): Journal Overview

electronic journals

International Review of Research in Open and Distributed Learning (IRRODL)

Publisher / Organization: Athabasca University Press

Year founded: 2000

Description: The International Review of Research in Open and Distributed Learning disseminates original research, theory, and best practice in open and distributed learning worldwide.

First Monday

Publisher / Organization: The University of Illinois at Chicago- University Library

Year founded: 1996

Description: First Monday is among the very first open access journals in the EdTech field. The journal’s subject matter encompasses the full range of Internet issues, including educational technologies, social media and web search. Contributors are urged via author guidelines to use simple explanations and less complex sentences and to be mindful that a large proportion of their readers are not part of academia and do not have English as a first language.

URL: http://firstmonday.org/

International Journal of Educational Technology in Higher Education(ETHE)

Publisher / Organization: Springer (from 2013)

Academic Management: University of Catalonia (UOC)

Year founded: 2004

Description: This journal aims to: provide a vehicle for scholarly presentation and exchange of information between professionals, researchers and practitioners in the technology-enhanced education field; contribute to the advancement of scientific knowledge regarding the use of technology and computers in higher education; and inform readers about the latest developments in the application of information technologies (ITs) in higher education learning, training, research and management.

URL: https://educationaltechnologyjournal.springeropen.com/

Online Learning (formerly JOLT / JALN)

Publisher / Organization: Online Learning Consortium

Year founded: 1997

Description: Online Learning promotes the development and dissemination of new knowledge at the intersection of pedagogy, emerging technology, policy, and practice in online environments. The journal has been published for over 20 years as the Journal of Asynchronous Learning Networks (JALN) and recently merged with the Journal of Online Learning and Teaching (JOLT).

URL: https://olj.onlinelearningconsortium.org/

Journal of Educational Technology & Society

Publisher / Organization: International Forum of Educational Technology & Society

Year founded:1998

Description: Educational Technology & Society seeks academic articles on the issues affecting the developers of educational systems and educators who implement and manage these systems. Articles should discuss the perspectives of both communities – the programmers and the instructors. The journal is currently still accepting submissions for ongoing special issues, but will cease publication in the future as the editors feel that the field of EdTech is saturated with high quality publications.

URL: http://www.ds.unipi.gr/et&s/index.php

Australasian Journal of Educational Technology

Publisher / Organization: Ascilite (Organization) & PKP Publishing Services Network

Year founded: 1985

Description: The Australasian Journal of Educational Technology aims to promote research and scholarship on the integration of technology in tertiary education, promote effective practice, and inform policy. The goal is to advance understanding of educational technology in post-school education settings, including higher and further education, lifelong learning, and training.

URL: https://ajet.org.au/index.php/AJET

Print Journals

The Internet and Higher Education

Publisher / Organization: Elsevier Ltd.

YEAR FOUNDED: 1998

DESCRIPTION: The Internet and Higher Education is devoted to addressing contemporary issues and future developments related to online learning, teaching, and administration on the Internet in post-secondary settings. Articles should significantly address innovative deployments of Internet technology in instruction and report on research to demonstrate the effects of information technology on instruction in various contexts in higher education.

URL: https://www.journals.elsevier.com/the-internet-and-higher-education

British Journal of Educational Technology

Publisher / Organization: British Educational Research Association (BERA)

YEAR FOUNDED: 1970

DESCRIPTION: The journal publishes theoretical perspectives, methodological developments and empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.

LINK: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8535

Computers & Education

Publisher / Organization: Elsevier Ltd.

Year founded: 1976

Description: Computers & Education aims to increase knowledge and understanding of ways in which digital technology can enhance education, through the publication of high quality research, which extends theory and practice.

URL: https://www.journals.elsevier.com/computers-and-education/

Tech Trends

Publisher / Organization: Springer US

Year founded: 1985

Description: TechTrends targets professionals in the educational communication and technology field. It provides a vehicle that fosters the exchange of important and current information among professional practitioners. Among the topics addressed are the management of media and programs, the application of educational technology principles and techniques to instructional programs, and corporate and military training.

URL: https://link.springer.com/journal/11528

International Journal on E-Learning (IJEL)

Year founded: 2002

Description: Advances in technology and the growth of e-learning to provide educators and trainers with unique opportunities to enhance learning and teaching in corporate, government, healthcare, and higher education. IJEL serves as a forum to facilitate the international exchange of information on the current research, development, and practice of e-learning in these sectors.

Led by an Editorial Review Board of leaders in the field of e-Learning, the Journal is designed for the following audiences: researchers, developers, and practitioners in corporate, government, healthcare, and higher education. IJEL is a peer-reviewed journal.

URL: http://www.aace.org/pubs/ijel/

Journal of Computers in Mathematics and Science Teaching (JCMST)

Year founded: 1981

Description: JCMST is a highly respected scholarly journal which offers an in-depth forum for the interchange of information in the fields of science, mathematics, and computer science. JCMST is the only periodical devoted specifically to using information technology in the teaching of mathematics and science.

URL: https://www.aace.org/pubs/jcmst/

Just as researchers build reputation over time that can be depicted (in part) through quantitative measures such as h-index and i10-index, journals are also compared based on the number of citations they receive..

Journal of Interactive Learning Research (JILR)

Year founded: 1997

Description: The Journal of Interactive Learning Research (JILR) publishes papers related to the underlying theory, design, implementation, effectiveness, and impact on education and training of the following interactive learning environments: authoring systems, cognitive tools for learning computer-assisted language learning computer-based assessment systems, computer-based training computer-mediated communications, computer-supported collaborative learning distributed learning environments, electronic performance support systems interactive learning environments, interactive multimedia systems interactive simulations and games, intelligent agents on the Internet intelligent tutoring systems, microworlds, virtual reality based learning systems.

URL: http://learntechlib.org/j/JILR/

Journal of Educational Multimedia and Hypermedia (JEMH)

Year founded: 1996

Description: JEMH is designed to provide a multi-disciplinary forum to present and discuss research, development and applications of multimedia and hypermedia in education. It contributes to the advancement of the theory and practice of learning and teaching in environments that integrate images, sound, text, and data.

URL: https://www.aace.org/pubs/jemh/

Journal of Technology and Teacher Education (JTATE)

Publisher / Organization: Society for Information Technology and Teacher Education (SITE)

Year founded: 1997

Description: JTATE serves as a forum for the exchange of knowledge about the use of information technology in teacher education. Journal content covers preservice and inservice teacher education, graduate programs in areas such as curriculum and instruction, educational administration, staff development instructional technology, and educational computing.

URL: https://www.aace.org/pubs/jtate/

Journal on Online Learning Research (JOLR)

Publisher / Organization: Association for the Advancement of Computing in Education (AACE)

YEAR FOUNDED: 2015

DESCRIPTION: The Journal of Online Learning Research (JOLR) is a peer-reviewed, international journal devoted to the theoretical, empirical, and pragmatic understanding of technologies and their impact on primary and secondary pedagogy and policy in primary and secondary (K-12) online and blended environments. JOLR is focused on publishing manuscripts that address online learning, catering particularly to the educators who research, practice, design, and/or administer in primary and secondary schooling in online settings. However, the journal also serves those educators who have chosen to blend online learning tools and strategies in their face-to-face classroom.

URL: https://www.aace.org/pubs/jolr/

 

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part 2

The most commonly used index to measure the relative importance of journals is the annual Journal Citation Reports (JCR). This report is published by Clarivate Analytics (previously Thomson Reuters).

SCImago

SCImago Journal Rank (SJR indicator) measures the influence of journals based on the number of citations the articles in the journal receive and the importance or prestige of the journals where such citations come from. The SJR indicator is a free journal metric which uses an algorithm similar to PageRank and provides an open access alternative to the journal impact factor in the Web of Science Journal Citation Report. The portal draws from the information contained in the Scopus database (Elsevier B.V.).

Google Scholar Journal Rank

Introduced by Google in 2004, Scholar is a freely accessible search engine that indexes the full text or metadata of scholarly publications across an array of publishing formats and disciplines.

Scopus Journal Metrics

Introduced by Elsevier in 2004, Scopus is an abstract and citation database that covers nearly 18,000 titles from more than 5,000 publishers. It offers journal metrics that go beyond just journals to include most serial titles, including supplements, special issues and conference proceedings. Scopus offers useful information such as the total number of citations, the total number of articles published, and the percent of articles cited.

Anne-Wil Harzing:

Citations are not just a reflection of the impact that a particular piece of academic work has generated. Citations can be used to tell stories about academics, journals and fields of research, but they can also be used to distort stories”.

Harzing, A.-W. (2013). The publish or perish book: Your guide to effective and responsible citation analysis. http://harzing.com/popbook/index.htm

ResearchGate

ResearchGate is a social networking site for scientists and researchers to share papers, ask and answer questions, and find collaborators. The community was founded in May 2008. Today it has over 14 million members.

Google Scholar

Google Scholar allows users to search for digital or physical copies of articles, whether online or in libraries. It indexes “full-text journal articles, technical reports, preprints, theses, books, and other documents, including selected Web pages that are deemed to be ‘scholarly. It comprises an estimated 160 million documents.

Academia.edu

Academia.edu is a social-networking platform for academics to share research papers. You can upload your own work, and follow the updates of your peers. Founded in 2008, the network currently has 59 million users, and adding 20 million documents.

ORCID

The ORCHID (Open Researcher and Contributor ID) is a nonproprietary alphanumeric code to uniquely identify scientific and other academic authors and contributors. It provides a persistent identity for humans, similar to content-related entities on digital networks that utilize digital object identifiers (DOIs). The organization offers an open and independent registry intended to be the de facto standard for contributor identification in research and academic publishing.

SCOPUS

The Scopus Author Identifier assigns a unique number to groups of documents written by the same author via an algorithm that matches authorship based on a certain criteria. If a document cannot be confidently matched with an author identifier, it is grouped separately. In this case, you may see more than one entry for the same author.

 

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more on metrics in this iMS blog

http://blog.stcloudstate.edu/ims?s=metrics

Charles Taylor

Taylor, C. (2017). Our evolving agenda. Philosophy & Social Criticism43(3), 274-275. doi:10.1177/0191453716680433

 Neo-Kantian ethics, for its part, tends to separate issues of the good life from what it considers the central questions of justice.

The reigning neo-liberal ideology, and the order it lauds, is meant to produce a maximization of wealth, and hence of means to fulfil our goals, without asking in what ways our frenetic attempts to increase GNP run counter to some of our most important goals: solidarity, the ability to discern and pursue a truly meaningful and fulfilling life, in keeping with our endowment and inclinations. We are either induced to neglect these in favour of playing our part in increasing GNP and/or we never pause to consider questions about what kind of life is best for us and, above all, what we owe to each other in this department

One of the central issues that arises in this context is that of democracy. After 1945, and then 1989, and then again in 2011 with the Arab Spring, we had the sense that democracy was on the march in history. But not only have many of the new departures been disappointing – Russia, Turkey, Egypt – but democracy is beginning to decay in its historic heartlands, where it has been operative for more than a century.

Inequalities are growing; in fact, democracy has been sacrificed to the supposed path of more rapid growth, as defined by neo-liberalism. This has led to a sense of impotence among non-elites, which has meant a drop in electoral participation, which in turn increases the power of money in politics, which leads to an intensified sense of impotence, and so on.

Taylor, C. (1998, October). The Dynamics of Democratic Exclusion. Journal of Democracy. p. 143.

Liberal democracy is a great philosophy of inclusion. It is rule of the people, by the people, and for the people, and today the “people” is taken to mean everybody, without the unspoken restrictions that formerly excluded peasants, women, or slaves. Contemporary liberal democracy offers the spectacle of the most inclusive politics in human history. Yet there is also something in the dynamic of democracy that pushes toward exclusion. This was allowed full rein in earlier democracies, as among the ancient republics, but today is a cause of great malaise.

The basic mode of legitimation of democratic states implies that they are founded on popular sovereignty. Now, for the people to be sovereign, it needs to form an entity and have a personality. This need can be expressed in the following way: The people is supposed to rule; this means that its members make up a decision-making unit, a body that takes joint decisions through a consensus, or at least a majority vote, of agents who are deemed equal and autonomous. It is not “democratic” for some citizens to be under the control of others. This might facilitate decision making, but it is not democratically legitimate.

In other words, a modern democratic state demands a “people” with a strong collective identity. Democracy obliges us to show much more solidarity and much more commitment to one another in our joint political project than was demanded by the hierarchical and authoritarian societies of yesteryear.

Thinkers in the civic humanist tradition, from Aristotle through Hannah Arendt, have noted that free societies require a higher level of commitment and participation than despotic or authoritarian ones. Citizens have to do for themselves, as it were, what the rulers would otherwise do for them. But this will happen only if these citizens feel a strong bond of identification with their political community, and hence with their fellow citizens.

successive waves of immigrants were perceived by many U.S. citizens of longer standing as a threat to democracy and the American way of life. This was the fate of the Irish beginning in the 1840s, and later in the century of immigrants from Southern and Eastern Europe. And of course, the long-established black population, when it was given citizen rights for the first time after the Civil War, was effectively excluded from voting through much of the Old South up until the civil rights legislation of the 1960s.

Multiculturalism and Postmodernism

For although conservatives often lump “postmodernists” and “multiculturalists” together with “liberals,” nothing could be less fair. In fact, the “postmodernists” themselves attack the unfortunate liberals with much greater gusto than they direct against the conser-vatives.

the two do have something in common, and so the targets partly converge. The discourse of the victim-accuser is ultimately rooted in certain philosophical sources that the postmodernists share with procedural liberalism—in particular, a commitment to negative liberty and/or a hostility to the Herder-Humboldt model of the associative bond. That is why policies framed in the language of “postmodernism” usually share certain properties with the policies of their procedural liberal enemies.

The struggle to redefine our political life in order to counteract the dangers and temptations of democratic exclusion will only intensify in the next century (My note: 21st century). There are no easy solutions, no universal formulas for success in this struggle. But at least we can try to avoid falling into the shadow or illusory ways of thinking. This means, first, that we must understand the drive to exclusion (as well as the vocation of inclusion) that democratic politics contains; and second, that we must fight free of some of the powerful philosophical illusions of our age. This essay is an attempt to push our thought a little ahead in both these directions.

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Taylor, C., & And, O. (1994). Multiculturalism: Examining the Politics of Recognition.

 

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Taylor, C. A. (1996). Theorizing Practice and Practicing Theory: Toward a Constructive Analysis of Scientific Rhetorics. Communication Theory (10503293)6(4), 374-387.

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Taylor, C., & Jennings, I. (2005). The Immanent Counter-Enlightenment: Christianity and Morality. South African Journal Of Philosophy24(3), 224-239.

a passage from Paul Bénichou’s fa mous work Mo rales du grand siècle: ‘Hu man kind re presses its mis ery when ever it can; and at the same time for gets that hu mil i at ing mo ral ity by which it had con demned life, and in do ing so had made a vir tue of ne ces sity.2 ’ In this ver sion, the la tent hu man ist mo ral ity suc ceeds in es tab – lish ing it self, and in so do ing helps to throw the theo log i cal-as cetic code onto the scrap heap. On this view, it is as if the hu man ist mo ral ity had al ways been there, wait ing for the chance to over throw its op pres sive pre de ces sor.

The re la tion ship was something like the fol low ing: As long as one lived in the en – chanted world, where the weather-bells chimed, one felt one self to be in a world full of threats, vul ner a ble to black magic in all its forms. In this world God was for most be liev ers the source of a pos i tive power, which was able to de feat the pow ers of evil. God was the chief source of coun ter-, or white, magic. He was the fi nal guar an tor that good would tri umph in this world of man i fold spir its and pow ers. For those com pletely ab sorbed in this world, it was prac ti cally im pos si ble not to be – lieve in God. Not to be lieve would mean de vot ing one self to the devil. A small mi nor – ity of truly re mark able – or per haps truly des per ate – peo ple did in deed do this. But for the vast ma jor ity there was no ques tion whether one be lieved in God or not – the pos i – tive force was as real a fact as the threats it coun ter acted. The ques tion of be lief was a ques tion of trust and mem ber ship rather than one of the ac cep tance of par tic u lar doc – trines. In this sense they were closer to the con text of the gos pels.

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

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





Grant America for Bulgaria

http://www.us4bg.org/areas/education/

Proposal |Project Title

The 21st Century Skills of the Academic Librarian in Bulgaria

Applicant:
Plamen Miltenoff, PhD, MLIS, http://web.stcloudstate.edu/pmiltenoff/faculty/
My experience and connections with the library organizations and professionals from Moldova, Bulgaria and Austria, as well as my 17+ years working at the St. Cloud State University library provides me with an opportunity for comparison and, consequently, proposal for collaborative practices with Bulgarian academic librarians.

Project Duration: one year

Problem Identification: Through the years, my work with faculty and librarians from Shoumen University (http://shu-bg.net/ ), Plovdiv University (https://uni-plovdiv.bg/), New Bulgarian University (https://nbu.bg/),  the American University (https://www.aubg.edu/) and Sofia University (https://www.uni-sofia.bg/) helped me identify differences and similarities in the work of the Bulgarian educational institutions and academia from abroad.

The role of the academic librarian in the educational process is different/limited in Bulgaria compared to the United States. During a collaboration on gamifying library instruction (http://web.stcloudstate.edu/pmiltenoff/bi/), the NBU librarians demonstrated their propensity to shift their campus role close to the campus role of American librarians, yet in general the Bulgarian library guild remains traditional in their view of their responsibilities toward the educational process on campus.

Project Objectives:

This proposal aims regular discussions among professionals from Bulgarian and American (possibly other nations) librarians to determine the framework regarding librarian’s responsibilities. Are academic librarians faculty members or staff? Do they have teaching or service (or both) responsibilities? What are 20th century academic librarians’ responsibilities are to be preserved? Updated? What are the 21st century responsibilities to be gained? What is the relationship between academic librarians and faculty? What is expected from an academic librarians to ensure learning happens? To benefit faculty’s teaching?
A comparison of academic library structures, job descriptions, models and discourses can lead to deep[er] analysis of existing structures and possible reorganizations to improve the role of the library in particular and the efficiency of the educational institution in general.
Comparisons of topics and syllabi: multiliteraices as successor of information literacy? Is the academic library the hub for technological innovations (e.g makerspaces, 3D printing, virtual reality/augmented reality) and if not, what is the academic library role in the process?
Other relevant topics / issues are expected to transpire during such discourse.

Project Description:

The project is organized in collaboration of synchronous and asynchronous character during the span of one academic year. Three synchronous sessions each semester (six sessions for the entire semester) will provide a forum through e-conferencing tools (e.g. Adobe Connect, WebEx, Skype, Google Hangout etc.) for live discussions and planning. Weekly asynchronous dialog through social media (e.g. blog, Facebook Group, Google Group etc.) will provide the platform/ hub/ forum daily/detailed preparation for the monthly synchronous meetings.

Most valuable feedback through the weekly asynchronous discussions will be voted by participants and three best weekly contributions will be awarded badges. At the end of the academic year, the three contributors with largest collection of badges will be awarded cost for registration fee, travel and lodging to an important European conference regarding libraries and education.

The experience and lessons from the process will be summed up, published and presented at local (Bulgarian), regional (Balkans) and international (European, U.S.) educational conferences and events. Similar cross-cultural experiences and studies will be research and comparison and future collaboration will be sought.

Impact:

  • The use of synchronous tools will provide technological and didactical practice for academic librarians; an experience they later can apply in their service to the campus community.
  • Same with the asynchronous tools / social media
  • The practice and experience of using social media for institutional purposes can help librarians figure out pertinent outreach to the recent and incoming students (Millennials and Gen Y)
  • The use of social media will provide transparency and participatory governing of the process.

Sustainability:

The lessons from such endeavor aim to bring closer collaboration and understanding between academic librarians and campus faculty. Such collaboration can be measured, as well as impact of improved teaching and improved learning. The measurements should convince university administration to further support the continues process of cross-cultural collaboration between academic librarians.

RFID blocking

There Are Plenty Of RFID-Blocking Products, But Do You Need Them?

hackers can access your credit card data wirelessly, through something called radio frequency identification, or RFID

card has a tiny RFID sensor chip. These chips are supposed to make life easier by emitting radio signals for fast identification. The technology helps keep track of livestock and inventory. It makes automatic payment on toll roads and faster scanning of passports possible, and, starting around 2004, brought us contactless payment with certain credit cards.

REI and other companies sell a range of RFID-blocking products and say the number of customers looking for travel bags and credit card sleeves has been growing. That’s despite the fact that the percentage of credit cards with RFID chips in the U.S. is extremely small.

Still, people are worried about electronic pickpocketing — worried enough to strap on RFID-blocking fanny packs, even skinny jeans. In 2014, the San Francisco-based clothing company Betabrand partnered with Norton Security to create the first pair of denim with RFID protected pockets.

Eva Velasquez, president of the Identity Theft Resource Center, says from a consumer perspective, deciding whether to invest in RFID-blocking technology is all about evaluating risk. In the next few years, there will undoubtedly be millions more of these cards on the market.

if you’re worried about e-pickpocketing but don’t want to spend much money, you can make your own blocking wallet or wrap your cards or passport in a thick piece of aluminum foil. According to Consumer Reports, that works as well as most RFID protectors on the market.
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more on cybersecurity in this IMS blog

bibliometrics altmetrics

International Benchmarks for Academic Library Use of Bibliometrics & Altmetrics, 2016-17

ID: 3807768 Report August 2016 115 pages Primary Research Group

http://www.researchandmarkets.com/publication/min3qqb/3807768

The report gives detailed data on the use of various bibliometric and altmetric tools such as Google Scholar, Web of Science, Scimago, Plum Analytics

20 predominantly research universities in the USA, continental Europe, the UK, Canada and Australia/New Zealand. Among the survey participants are: Carnegie Mellon, Cambridge University, Universitat Politècnica de Catalunya the University at Albany, the University of Melbourne, Florida State University, the University of Alberta and Victoria University of Wellington

– 50% of the institutions sampled help their researchers to obtain a Thomsen/Reuters Researcher ID.

ResearcherID provides a solution to the author ambiguity problem within the scholarly research community. Each member is assigned a unique identifier to enable researchers to manage their publication lists, track their times cited counts and h-index, identify potential collaborators and avoid author misidentification. In addition, your ResearcherID information integrates with the Web of Science and is ORCID compliant, allowing you to claim and showcase your publications from a single one account. Search the registry to find collaborators, review publication lists and explore how research is used around the world!

– Just 5% of those surveyed use Facebook Insights in their altmetrics efforts.

 

 

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

social media and altmetrics

Sugimoto, C. R., Work, S., Larivière, V., & Haustein, S. (2016). Scholarly use of social media and altmetrics: a review of the literature. Retrieved from https://arxiv.org/abs/1608.08112
https://arxiv.org/ftp/arxiv/papers/1608/1608.08112.pdf
One of the central issues associated with altmetrics (short for alternative metrics) is the identification of communities engaging with scholarly content on social media (Haustein, Bowman, & Costas, 2015; Neylon, 2014; Tsou, Bowman, Ghazinejad, & Sugimoto, 2015) . It is thus of central importance to understand the uses and users of social media in the context of scholarly communication.
most identify the following major categori es: social networking, social bookmarking, blogging, microblogging, wikis , and media and data sharing (Gu & Widén -Wulff, 2011; Rowlands, Nicholas, Russell, Canty, & Watkinson, 2011; Tenopir et al., 2013) . Some also conside r conferencing, collaborative authoring, scheduling and meeting tools (Rowlands et al., 2011) or RSS and online documents (Gu & Widén -Wulff, 2011; Tenopir et al., 2013) as social media. The landscape of social media, as well as that of altmetrics, is constantly changing and boundaries with othe r online platforms and traditional metrics are fuzzy. Many online platforms cannot be easily classified and more traditional metrics , such as downloads and mentions in policy documents , have been referred to as altmetrics due to data pr ovider policies.
the Use of social media platforms for by researchers is high — ranging from 75 to 80% in large -scale surveys (Rowlands et al., 2011; Tenopir et al., 2013; Van Eperen & Marincola, 2011) .
but
less than 10% of scholars reported using Twitter (Rowlands et al., 2011) , while 46% used ResearchGate (Van Noorden, 2014) , and more than 55% use d YouTube (Tenopir et al., 2013) —it is necessary to discuss the use of various types of social media separately . Furthermore, there i s a distinction among types of us e, with studies showing higher uses of social media for dissemination, consumption, communication , and promotion (e.g., Arcila -Calderón, Piñuel -Raigada, & Calderín -Cruz, 2013; Van Noorden, 2014) , and fewer instances of use for creation (i.e., using social media to construct scholarship) (British Library et al., 2012; Carpenter, Wetheridge, Tanner, & Smith, 2012; Procter et al., 2010b; Tenopir et al., 2013) .
Frequently mentioned social platforms in scholarly communication research include research -specific tools such as Mendeley, Zotero, CiteULike, BibSonomy, and Connotea (now defunct) as well as general tools such as Delicious and Digg (Hammond, Hannay, Lund, & Scott, 2005; Hull, Pettifer, & Kell, 2008; Priem & Hemminger, 2010; Reher & Haustein, 2010) .
Social data sharing platforms provide an infrastructure to share various types of scholarly objects —including datasets, software code, figures, presentation slides and videos —and for users to interact with these objects (e.g., comment on, favorite, like , and reuse ). Platforms such as Figshare and SlideShare disseminate scholars’ various types of research outputs such as datasets, figures, infographics, documents, videos, posters , or presentation slides (Enis, 2013) and displays views, likes, and shares by other users (Mas -Bleda et al., 2014) . GitHub provides for uploading and stor ing of software code, which allows users to modify and expand existing code (Dabbish, Stuart, Tsay, & Herbsleb, 2012) , which has been shown to lead to enhanced collaboratio n among developers (Thung, Bissyande, Lo, & Jiang, 2013) . As w ith other social data sharing platforms, usage statistics on the number of view and contributions to a project are provided (Kubilius, 2014) . The registry of research data repositories, re3data.org, ha s indexed more than 1,200 as of May 2015 2 . However, only a few of these repositories (i.e. , Figshare, SlideShare and Github) include social functionalities and have reached a certain level of participation from scholars (e.g., Begel, Bosch, & Storey, 2013; Kubilius, 2014) .
Video provide s yet another genre for social interaction and scholarly communication (Kousha, Thelwall, & Abdoli, 2012; Sugimoto & Thelwall, 2013) . Of the various video sharing platforms, YouTube, launched in 2005, is by far the most popular
A study of UK scholars reports that the majority o f respondents engaged with video for scholarly communication purposes (Tenopir et al., 2013) , yet only 20% have ever created in that genre. Among British PhD students, 17% had used videos and podcasts passively for research, while 8% had actively contributed (British Library et al., 2012) .
Blogs began in the mid -1990s and were considered ubiquitous by the mid- 200 0s (Gillmor, 2006; Hank, 2011; Lenhart & Fox, 2006; Rainie, 2005) . Scholarly blogs emerged during this time with their own neologisms (e.g., blogademia , blawgosphere , bloggership) and body of research (Hank, 2011) and were considered to change the exclusive structure of scholarly communication
Technorati, considered t o be on e of the largest ind ex of blogs, deleted their entire blog directory in 2014 3 . Individual blogs are also subject to abrupt cancellations and deletions, making questionable the degree to which blogging meets the permanence criteria of scholarly commu nication (Hank, 2011) .
ResearchBlogging.org (RB) — “an aggregator of blog posts referencing peer -reviewed research in a structured manner” (Shema, Bar -Ilan, & Thelwall, 2015, p. 3) — was launched in 2007 and has been a fairly stable structure in the scholarly blogging environment. RB both aggregates and —through the use of the RB icon — credentials scholarly blogs (Shema et al., 2015) . The informality of the genre (Mewburn & Thomson, 2013) and the ability to circumve nt traditional publishing barr iers has led advocates to claim that blogging can invert traditional academic power hierarchies (Walker, 2006) , allow ing people to construct scholarly identities outside of formal institutionalization (Ewins, 2005; Luzón, 2011; Potter, 2012) and democratize the scientific system (Gijón, 2013) . Another positive characteristic of blogs is their “inherently social” nature (Walker, 2006, p. 132) (see also Kjellberg, 2010; Luzón, 2011 ). Scholars have noted the potential for “communal scholarship” (Hendrick, 2012) made by linking and commenting, calling the platform “a new ‘third place’ for academic discourse” (Halavais, 2006, p. 117) . Commenting functionalities were seen as making possible the “shift from public understanding to public engagement with science” (Kouper, 2010, p. 1) .
Studies have also provided evidence of high rate s of blogging among certain subpopulations: for example, approximately one -third of German university staff (Pscheida et al., 2013) and one fifth of UK doctoral students use blogs (Carpenter et al., 2012) .
Academics are not only producers, but also consumers of blogs: a 2007 survey of medical bloggers foundthat the large majority (86%) read blogs to find medical news (Kovic et al., 2008)

Mahrt and Puschmann (2014) , who defined science blogging as “the use of blogs for science communication” (p. 1). It has been similarly likened to a sp ace for public intellectualism (Kirkup, 2010; Walker, 2006) and as a form of activism to combat perceived biased or pseudoscience (Riesch & Mendel, 2014. Yet, there remains a tension between science bloggers and science journalists, with many science journals dismissing the value of science blogs (Colson, 2011)

.
while there has been anecdotal evidence of the use of blogs in promotion and tenure (e.g., (Podgor, 2006) the consensus seem s to suggest that most institutions do not value blogging as highly as publishing in traditional outlets, or consider blogging as a measure of service rather than research activity (Hendricks, 2010, para. 30) .
Microblogging developed out of a particular blogging practice, wherein bloggers would post small messages or single files on a blog post. Blogs that focused on such “microposts” were then termed “tumblelogs” and were described as “a quick and dirty stream of consciousness” kind of blogging (Kottke, 2005, para. 2)
most popular microblogs are Twitter (launched in 2006), tumblr (launched in 2007), FriendFeed (launched in 2007 and available in several languages), Plurk (launched in 2008 and popular in Taiwan), and Sina Weibo (launched in 2009 and popular in China).
users to follow other users, search tweets by keywords or hashtags, and link to other media or other tweets
.

Conference chatter (backchanneling) is another widely studied area in the realm of scholarly microblogging. Twitter use at conferences is generally carried out by a minority of participants

Wikis are collaborative content management platforms enabled by web browsers and embedded markup languages.
Wikipedia has been advocated as a replacement for traditional publishing and peer review models (Xia o & Askin, 2012) and pleas have been made to encourage experts to contribute (Rush & Tracy, 2010) . Despite this, contribution rates remain low — likely hindered by the lack of explicit authorship in Wikipedia, a cornerstone of the traditional academic reward system (Black, 2008; Butler, 2008; Callaway, 2010; Whitworth & Friedman, 2009) . Citations to scholarly documents —another critical component in the reward system —are increasingly being found i n Wikiped ia entries (Bould et al., 2014; Park, 2011; Rousidis et al., 2013) , but are no t yet seen as valid impact indicators (Haustein, Peters, Bar -Ilan, et al., 2014) .
The altmetrics manifesto (Priem et al., 2010, para. 1) , altmetrics can serve as filters , which “reflect the broad, rapid impact of scholarship in this burgeoning ecosystem”.
There are also a host of platforms which are being used informally to discuss and rate scholarly material. Reddit, for example, is a general topic platform where users can submit, discuss and rate online content. Historically, mentions of scientific journals on Reddit have been rare (Thelwall, Haustein, et al., 2013) . However, several new subreddits —e.g., science subreddit 4 , Ask Me Anything sessions 5 –have recently been launched, focusing on the discussion of scientific information. Sites like Amazon (Kousha & Thelwall, 2015) and Goodreads (Zuccala, Verleysen, Cornacchia, & Engels, 2015) , which allow users to comment on and rate books, has also been mined as potential source for the compilation of impact indicators
libraries provide services to support researchers’ use of social media tools and metrics (Lapinski, Piwowar, & Priem, 2013; Rodgers & Barbrow, 2013; Roemer & Borchardt, 2013). One example is Mendeley Institutional Edition, https://www.elsevier.com/solutions/mendeley/Mendeley-Institutional-Edition, which mines Mendeley documents, annotations, and behavior and provides these data to libraries (Galligan & Dyas -Correia, 2013) . Libraries can use them for collection management, in a manner similar to other usage data, such as COUNTER statistics (Galligan & Dyas -Correia, 2013) .
Factors affecting social media use; age, academic rank and status, gender, discipline, country and language,

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h-index

http://guides.library.cornell.edu/c.php?g=32272&p=203391
https://en.wikipedia.org/wiki/H-index

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

biometric authentication online ed

Wiklund, M., Mozelius, P., Westing, T., & Norberg, L. (2016). Biometric Belt and Braces for Authentication in Distance Education. Retrieved from https://www.researchgate.net/publication/309548915_Biometric_Belt_and_Braces_for_Authentication_in_Distance_Education
Abstract
a need for new techniques to handle the problem in online environments. To achieve zero cheating is hard (or impossible) without repelling not only cheaters but also those students who do not cheat, where a zero ‐ tolerance emphasis also would risk inhibiting students’ intrinsic motivation. Several studies indicate that existing virtual learning environments do not provide the features needed to control that the intended student is the one taking the online exam. Biometric Belt and Braces for Authentication in Distance Education.
One approach to prevent student’s dishonesty is the university code of honour. This is a set of rules describing what actions are not permitted and the consequences for students taking such actions. Another way of preventing cheating is the use of proctors during written exams. Even while using such codes of honour and proctors, universities still have found many students to cheat. Biometric Belt and Braces for Authentication in Distance Education.
Neutralisation is the phenomenon when a person rationalises his or her dishonest behaviour with arguments like “I can do this because the work load within this course is just too overwhelming” or “I can do this because I have a half ‐ time job on the side which gives me less study time than the other students have”. By doing so the student puts the blame for cheating on external factors rather than on himself, and also protects himself from the blame of others (Haines et al. 1986). This neutralises the behavior in the sense that the person’s feelings of shame are reduced or even eliminated. Haines et al. (1986 Biometric Belt and Braces for Authentication in Distance Education.
Simply asking participants to read a code of honour when they had the opportunity to cheat reduced dishonesty. Also whether one signed the code of honour or just read it influenced cheating. The Shu et al. (2011) study suggests that opportunity and knowledge of ethical standards are two factors that impact students’ ethical decision about cheating. This is in line with the results in (McCabe, Trevino and Butterfield 2001), showing that if students regularly are reminded of the university’s code of honour, they are less likely to cheat Biometric Belt and Braces for Authentication in Distance Education.
For an online course setting, Gearhart (2001) suggest that teachers should develop a guideline for “good practices”.
In online examination there are reports of students hiring other persons to increase their scores (Flior & Kowalski, 2010) and there is a need for new enhanced authentication tools (Ullah, Xiao & Lilley, 2012). For companies and Internet environments the process of authentication is often completed through the use of logon identification with passwords and the assumption of the password to guarantee that the user is authentic (Ramzan, 2007), but logins and passwords can be borrowed (Bailie & Jortberg, 2009). The discussion on how to provide enhanced authentication in online examination has led to many suggested solutions; four of them are: Biometric Belt and Braces for Authentication in Distance Education.
  • Challenge Questions: with questions based on third ‐ party data ƒ
  • Face ‐ to ‐ Face Proctored Exam: with government or institution issued identification ƒ
  • Web Video Conference Proctor: audio and video conference proctoring via webcam and screen monitoring service with live, certified proctors ƒ
  • Biometrics and Web Video Recording: with unique biometrics combined with the recording of student in exam via webcam

An idea for online courses is that assessment should not only be a one way process where the students get grades and feedback. The examination process should also be a channel for students’ feedback to teachers and course instructors (Mardanian & Mozelius, 2011). New online methods could be combined with traditional assessment in an array of techniques aligned to the learning outcomes (Runyon and Von Holzen, 2005). Examples of summative and formative assessment in an online course could be a mix of: Biometric Belt and Braces for Authentication in Distance Education.

  • Multiple choice questions (MCQ) tests, automatically corrected in a virtual learning environment ƒ
  • Term papers or essays analysed by the course instructors ƒ
  • Individual or group assignments posted in digital drop ‐ boxes ƒ
  • Oral or written tests conducted in the presence of the instructor or through videoconferences (Dikli, 2003)

Authors’ suggestion is a biometric belt and braces model with a combination of scanned facial coordinates and voice recognition, where only a minimum of biometric data has to be stored. Even if the model is based on biometrics with a medium to low grade of uniqueness and permanence, it would be reliable enough for authentication in online courses if two (or more) types of biometrics are combined with the presented dialogue based examination using an interaction/obser ‐ vation process via web cameras. Biometric Belt and Braces for Authentication in Distance Education.

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

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more on proctoring and detecting cheating:

http://www.wgu.edu/blogpost/innocent-red-flags-caught-by-online-exam-proctors

voices from the other side:
http://infoproc.blogspot.com/2013/04/how-to-cheat-online-exam-proctoring.html

https://campustechnology.com/articles/2016/04/06/how-students-try-to-bamboozle-online-proctors.aspx

http://www.usnews.com/education/online-education/articles/2014/06/17/think-twice-before-cheating-in-online-courses

photo sharing and libraries

Photo-sharing Site as Library Tool : A Web-based Survey
peer-reviewed article for Digital Library Perspectives: https://mc.manuscriptcentral.com/dlp

opportunity to user to develop a sense of ownership over the library resources.

Photo-sharing sites  already  have  taken  sharp  inroads  into  the  field  of  teaching-learnin encouraging a shift from teacher-led approach to user centred engagement (Kawka, et al,2012).

Introducing photo-sharing sites and integrating with other social networking sites, libraries are now making their web presence outside the “traditional web platform”. With facility of online  managing  and  sharing  of  digital  images,  photo-sharing  sites  enable  users  to  get remotely connected with others and interact with comment links. Photo-sharing sites that are commonly being used by libraries are Flickr (www.flickr.com), Instagram (instagram.com), Pinterest  (in.pinterest.com),   Photobucket   (photobucket.com),   Picasa   (picasa.google.com), SmugMug (www.smugmug.com), etc (Bradley, 2007; Kroski, 2008; Salomon, 2013).

The results showed that blog and RSS are among the mostly used applications and web 0 applications are associated with overall website quality,  particularly to  the  service  quality.

Stvilia and  Jörgensen  (2010) suggests that  controlled  vocabulary  terms  may  be

37 complemented with those user generated tags which users feel more comfortable with for information The study also reflects a growing interest among the user community to be involved in “social content creation and sharing communities in creating and enhancing the metadata of their photo collections to make the collections more accessible and visible”.

page 7-8.
2.1 Steps to increase accessibility to photo-sharing sites
a)  Improve visibility: To make photo-sharing sites of the library easily visible, a direct link to library homepage is essential

p. 9
2.2 Purposes of using photo-sharing sites
a)   Organising library tour
b) Community building
c)   Tool for digitisation
d) Grabbing the users at their own place
e)   Integrating  Feeds  with  other  application
f)   Displaying new arrivals : Newly added books
g)   Sharing news & events and publicize library activities
h)   Archives of exhibits
i) Portal for academic and research activity:   Photo-sharing sites may serve as platform tofoster teaching learning activity, particularly for those who may use these image resource sites for academic purpose
j)  Experimentation : Being a relatively new approach to users service, these tools may be introduced on experimental basis to examine their proper utilisation before final implementation
k) Miscellaneous :  Public  library  can  reach  out  to  the  community  physically,  offer service to  the  traditionally  underserved,  homebound  or  people  with  disability, implement programmes  to  include  marginalised  section  of  the  community  and showcase its mobile outreach efforts in photo-sharing sites.

page 12-13
Before  going  to  integrate  photo-sharing  site,  a  library  should  set  the  strategic  objectives i.e., what purposes are to be served. “Purpose can provide clarity of vision when creating policies or  guidelines”  (Garofalo,  2013,  p.28).  The above discussedrange  of  purposes  may  help  librarians  to  develop  better  understanding  to  makeinformed  selection  of  photo-sharing  utility and  the  nature  of  images  to  be  posted through it. Goal setting should precede consideration of views of a sizeable section of all library stakeholders to know beforehand what they expect from the library.
•    Once the purposes are outlined, a library should formulate policy/ guideline for photo-sharing practices, based on user requirement, staff resource, available time component and technological support base. Policy offers a clear guideline for the users and staff to decide the kind of images to be posted.   A guideline is indeed essential for the optimum use of photo-sharing site. It also delineates the roles and responsibilities of the staff concerned and ensures regular monitoring of the posts. Policy may highlight fair use guidelines and allow re-use of images within the scope of copy-right.
•    A best way to start is integrating an app, involving simple design with fewer images and let users be familiarized with the system. During the course of development more and more apps may be added, with more images to be posted to serve variety of purposes, depending on the institutional resource and user demand.
•    Accessibility to photo-sharing site largely depends on its visibility to the audience. Icon  of  photo-sharing  utility  prominently  located  on  website  will  increase  the presentation of its visual identity. Library may set links to photo-sharing sites at home page or at drilled-down page.
•    Being  an  emerging  technology,  photo-sharing  site  needs  adequate  exposure  for optimum usage. Annotations associated to photo-sharing site will give an idea about the online tool and will guide users to better harness the application.
•    Photo-sharing sites allow images to be organised in a variety of way. Categorising image resources under various topical headings at one location will improve resource identification and frees one from extensive searching.
•    Regular posting of engaging images to photo-sharing site from the library and follow- up will attract users to tag and share images and strengthen community involvement with active user participation.
•    “Social and informal photographs” of library staff will make them more approachable and strengthen patron-staff relationship.
•    Library should seek user comments and suggestions to improve current photo-sharing application  and  to  incorporate  fresh  element  to  library  service  provision.  User feedback may be considered as a tool to evaluate the effectiveness of existing photo- sharing practices.
•    To  popularise  the  effort,  usual  promotional  media  like  physical  and  online  signs/ displays  apart,   library  may  use   social   media  marketing  platforms   like   blogs, Facebook, Twitter, etc., and increase awareness of photo-sharing tools.
•    Imparting technology training may develop necessary knowledge; improve skill, and change the attitude and mindset of library professionals to handle issues related to using this web-based powered-tools and repurpose existing accessibility settings.
•    To provide quick link to photo-sharing site from anywhere in the web, a library may use add-ons / plug-ins to embed image sharing tools.
•    Photo-sharing site may be implemented to satisfy multiple approach options of users. A section  of  users  use  photo-sharing site  to have  a glimpse  of  the  newly arrived documents, highlights of catalogue, rare books, etc.   Some others may use it to find images of historical importance with context. New users may find it attractive to pay

 

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