at a session on the umbrella concept of “mixed reality” (abbreviated XR) here Thursday, attendees had some questions for the panel’s VR/AR/XR evangelists: Can these tools help students learn? Can institutions with limited budgets pull off ambitious projects? Can skeptical faculty members be convinced to experiment with unfamiliar technology?
Yale has landed on a “hub model” for project development — instructors propose projects and partner with students with technological capabilities to tap into a centralized pool of equipment and funding. (My note: this is what I suggest in my Chapter 2 of Arnheim, Eliot & Rose (2012) Lib Guides)
Several panelists said they had already been getting started on mixed reality initiatives prior to the infusion of support from Educause and HP, which helped them settle on a direction
While 3-D printing might seem to lend itself more naturally to the hard sciences, Yale’s humanities departments have cottoned to the technology as a portal to answering tough philosophical questions.
institutions would be better served forgoing an early investment in hardware and instead gravitating toward free online products like Unity, Organon and You by Sharecare, all of which allow users to create 3-D experiences from their desktop computers.
XR technologies encompassing 3D simulations, modeling, and production.
This project sought to identify
current innovative uses of these 3D technologies,
how these uses are currently impacting teaching and learning, and
what this information can tell us about possible future uses for these technologies in higher education.
p. 5 Extended reality (XR) technologies, which encompass virtual reality (VR) and augmented reality (AR), are already having a dramatic impact on pedagogy in higher education. XR is a general term that covers a wide range of technologies along a continuum, with the real world at one end and fully immersive simulations at the other.
p. 6The Campus of the Future project was an exploratory evaluation of 3D technologies for instruction and research in higher education: VR, AR, 3D scanning, and 3D printing. The project sought to identify interesting and novel uses of 3D technology
p. 7 HP would provide the hardware, and EDUCAUSE would provide the methodological expertise to conduct an evaluation research project investigating the potential uses of 3D technologies in higher education learning and research.
The institutions that participated in the Campus of the Future project were selected because they were already on the cutting edge of integrating 3D technology into pedagogy. These institutions were therefore not representative, nor were they intended to be representative, of the state of higher education in the United States. These institutions were selected precisely because they already had a set of use cases for 3D technology available for study
p. 9 At some institutions, the group participating in the project was an academic unit (e.g., the Newhouse School of Communications at Syracuse University; the Graduate School of Education at Harvard University). At these institutions, the 3D technology provided by HP was deployed for use more or less exclusively by students and faculty affiliated with the particular academic unit.
p. 10 definitions
there is not universal agreement on the definitions of these
terms or on the scope of these technologies. Also, all of these technologies
currently exist in an active marketplace and, as in many rapidly changing markets, there is a tendency for companies to invent neologisms around 3D technology.
A 3D scanner is not a single device but rather a combination of hardware and
software. There are generally two pieces of hardware: a laser scanner and a digital
camera. The laser scanner bounces laser beams off the surface of an object to
determine its shape and contours.
p. 11 definitions
Virtual reality means that the wearer is completely immersed in a computer
simulation. Several types of VR headsets are currently available, but all involve
a lightweight helmet with a display in front of the eyes (see figure 2). In some
cases, this display may simply be a smartphone (e.g., Google Cardboard); in other
cases, two displays—one for each eye—are integrated into the headset (e.g., HTC
Vive). Most commercially available VR rigs also include handheld controllers
that enable the user to interact with the simulation by moving the controllers
in space and clicking on finger triggers or buttons.
p. 12 definitions
Augmented reality provides an “overlay” of some type over the real world through
the use of a headset or even a smartphone.
In an active technology marketplace, there is a tendency for new terms to be
invented rapidly and for existing terms to be used loosely. This is currently
happening in the VR and AR market space. The HP VR rig and the HTC Vive
unit are marketed as being immersive, meaning that the user is fully immersed in
a simulation—virtual reality. Many currently available AR headsets, however, are
marketed not as AR but rather as MR (mixed reality). These MR headsets have a
display in front of the eyes as well as a pair of front-mounted cameras; they are
therefore capable of supporting both VR and AR functionality.
p. 13 Implementation
Technical issues can generally be divided into two broad categories: hardware
problems and software problems. There is, of course, a common third category:
p. 15 the technology learning curve
The well-known diffusion of innovations theoretical framework articulates five
adopter categories: innovators, early adopters, early majority, late majority, and
laggards. Everett M. Rogers, Diffusion of Innovations, 5th ed. (New York: Simon and Schuster, 2003).
It is also likely that staff in the campus IT unit or center for teaching and learning already know who (at least some of) these individuals are, since such faculty members are likely to already have had contact with these campus units.
Students may of course also be innovators and early adopters, and in fact
several participating institutions found that some of the most creative uses of 3D technology arose from student projects
p. 30 Zeynep Tufekci, in her book Twitter and Tear Gas
definition: There is no necessary distinction between AR and VR; indeed, much research
on the subject is based on a conception of a “virtuality continuum” from entirely
real to entirely virtual, where AR lies somewhere between those ends of the
spectrum. Paul Milgram and Fumio Kishino, “A Taxonomy of Mixed Reality Visual Displays,” IEICE Transactions on Information Systems, vol. E77-D, no. 12 (1994); Steve Mann, “Through the Glass, Lightly,” IEEE Technology and Society Magazine 31, no. 3 (2012): 10–14.
For the future of 3D technology in higher education to be realized, that
technology must become as much a part of higher education as any technology:
the learning management system (LMS), the projector, the classroom. New
technologies and practices generally enter institutions of higher education as
initiatives. Several active learning classroom initiatives are currently under
way,36 for example, as well as a multi-institution open educational resources
(OER) degree initiative.37
p. 32 Storytelling
Some scholars have argued that all human communication
is based on storytelling;41 certainly advertisers have long recognized that
storytelling makes for effective persuasion,42 and a growing body of research
shows that narrative is effective for teaching even topics that are not generally
thought of as having a natural story, for example, in the sciences.43
p. 33 accessibility
The experience of Gallaudet University highlights one of the most important
areas for development in 3D technology: accessibility for users with disabilities.
p. 34 instructional design
For that to be the case, 3D technologies must be incorporated into the
instructional design process for building and redesigning courses. And for that
to be the case, it is necessary for faculty and instructional designers to be familiar
with the capabilities of 3D technologies. And for that to be the case, it may not be necessary but would certainly be helpful for instructional designers to collaborate closely with the staff in campus IT units who support and maintain this hardware.
Every institution of higher education has a slightly different organizational structure, of course, but these two campus units are often siloed. This siloing may lead to considerable friction in conducting the most basic organizational tasks, such as setting up meetings and apportioning responsibilities for shared tasks. Nevertheless, IT units and centers for teaching and learning are almost compelled to collaborate in order to support faculty who want to integrate 3D technology into their teaching. It is necessary to bring the instructional design expertise of a center for teaching and learning to bear on integrating 3D technology into an instructor’s teaching (My note: and where does this place SCSU?) Therefore, one of the most critical areas in which IT units and centers for teaching and learning can collaborate is in assisting instructors to develop this integration and to develop learning objects that use 3D technology. p. 35 For 3D technology to really gain traction in higher education, it will need to be easier for instructors to deploy without such a large support team.
p. 35 Sites such as Thingiverse, Sketchfab, and Google Poly are libraries of freely
available, user-created 3D models.
ClassVR is a tool that enables the simultaneous delivery of a simulation to
multiple headsets, though the simulation itself may still be single-user.
p. 37 data management:
An institutional repository is a collection of an institution’s intellectual output, often consisting of preprint journal articles and conference papers and the data sets behind them.49 An institutional repository is often maintained by either the library or a partnership between the library and the campus IT unit. An institutional repository therefore has the advantage of the long-term curatorial approach of librarianship combined with the systematic backup management of the IT unit. (My note: leaves me wonder where does this put SCSU)
Sharing data sets is critical for collaboration and increasingly the default for
scholarship. Data is as much a product of scholarship as publications, and there
is a growing sentiment among scholars that it should therefore be made public.50
The European Union‘s General Data Protection Regulation, or GDPR, goes into effect on May 25
The objective of the regulation, which passed in 2016, is to simplify and consolidate rules that companies need to follow in order to protect their data and to return control to EU citizens and residents over their personal information.
Individuals in the EU will have the right to access or request that companies erase or migrate their data elsewhere. When asked, companies must prove to authorities that they have satisfactory policies and procedures in place to protect their data, or they will face huge fines. How huge? If your company’s not compliant, the fines could be as large as 20 million Euros (about $24 million) or four percent of your annual global revenue, whichever is higher.
“A U.S. tourist who visits Germany for one day and returns to the U.S. has rights under the law if that person used [a service like] Facebook while on the trip,” Alex Stern, an attorney wrote on his firm’s blog.
My notes: this is a 1997 article
the explosion of information is not accompanied by understanding of information.
p. 337 However, if one accepts a definition of information as a process rather than as a thing, then such policies can at best form a framework for the creation of mean- ing by the individuals or groups who are creating information by bring- ing their knowledge to bear on the data available to them
Data acquisition, maintenance and delivery are a vital part of organisational life, but problems arise when we fail to recognise the necessary links to knowledge.
p. 338 However, just teaching users the practi- calities of applications has been seen to be deficient. It leads to an exces- sive focus on ‘how’ to use a particular application rather than on ‘why’ it should be used 13.
p. 379 Information literacy is a stage above computer literacy, the latter usually implying the ability to use a personal computer . My note: some librarians assume that “computer literacy” is the same as “digital literacy” and were trying to convince me that information literacy is succeeding digital literacy, where it is the other way around
p. 380 There are those within the LIS community who warn that librar- ians should not stray into areas that are not appropriate. Behrens points out that the future is likely to see an increased emphasis on a part- nership between librarians and educators. My note another glaring discrepancy between myself and the librarians at SCSU
p. 386 The phrase information literacy has some value in expressing what might need to be done if the aims of information policies are to be made concrete. It points to the need for an emphasis on the awareness of the individual using data of a range of issues. These are not, it has been argued, to be limited to issues of storage and retrieval but have, centrally, to be concerned with issues of definition and meaning. These issues might be tackled in this order: what are the issues in this field surround- ing the nature of knowledge (i.e. how do we formulate questions); how might data be best acquired, stored, etc. in order to answer these ques- tions? (this might well best be paralleled by training in computer literacy); and what factors, both social and individual, place constraints on our ability to use the data?
how data is produced, collected and analyzed. make accessible all kind of data and info
ask good q/s and find good answers, share finding in meaningful ways. this is where digital literacy overshadows information literacy and this the fact that SCSU library does not understand; besides teaching students how to find and evaluate data, I also teach them how to communicate effectively using electronic tools.
connecting people tools and resources and making it easier for everybody. building collaborative, open and interdisciplinary
robust data computational literates. developing workshops, project and events to practice new skills. to position the library as the interdisciplinary nexus
what are data: definition. items of information, facts, traces of content and form. higher level, conception discussion about data in terms of social effects: matadata capturing information about the world, social political and economic changes. move away the mystic conceptions about data. nothing objective about data.
the emergence of IoT – digital meets physical. cyber physical systems. smart objects driven by industry. . proliferation of sensor and device – smart devices.
what does privacy looks like ? what is netneutrality when IoT? library must restructure : collaborate across institutions about collections of data in opien and participatory ways. put IoT in the hands of make and break things (she is maker space aficionado)
make and break things hackathons – use cheap devices such as Arduino and Pi.
data literacy programs with higher level conception exploration; libraries empower the campus in data collection. data science norms, store and share data to existing repositories and even catalogs. commercial services to store and connect data, but very restrictive and this is why libraries must be involved.
linked data and dark data
linked data – draw connections around online data most of the data are locked. linked data uses metadata to link related information in ways computers can understand.
libraries take advantage of link data. link data opportunity for semantics, natural language processing etc. if hidden data is relative to our communities, it is a library responsibility to provide it. community data practitioners
massive data, which cannot be analyzed by relational processing. data not yield significant findings. might be valuable for researchers: one persons trash is another persons’ treasure. preserving data and providing access to info. collaborate with researchers across disciplines and assist decide what is worth keeping and what discarding and how to study.
rich learning experience working with lined and dark data enable fresh perspective and learning how to work with data architecture. data literacy programming.
In the age of Big Data, there is an abundance of free or cheap data sources available to libraries about their users’ behavior across the many components that make up their web presence. Data from vendors, data from Google Analytics or other third-party tracking software, and data from user testing are all things libraries have access to at little or no cost. However, just like many students can become overloaded when they do not know how to navigate the many information sources available to them, many libraries can become overloaded by the continuous stream of data pouring in from these sources. This session will aim to help librarians understand 1) what sorts of data their library already has (or easily could have) access to about how their users use their various web tools, 2) what that data can and cannot tell them, and 3) how to use the datasets they are collecting in a holistic manner to help them make design decisions. The presentation will feature examples from the presenters’ own experience of incorporating user data in decisions related to design the Bethel University Libraries’ web presence.
lack of fear, changing the mindset.
deep collaboration both within and cross-consortia
don’t rely on vendor solutions. changing mindset
development = oppty (versus development as “work”)
private higher education is PALNI
3d virtual picture of disastrous areas. unlock the digital information to be digitally accessible to all people who might be interested.
they opened the maps of Katmandu for the local community and they were coming up with the strategies to recover. democracy in action
i can’t stop thinking that the keynote speaker efforts are mere follow up of what Naomi Klein explains in her Shock Doctrine: http://www.naomiklein.org/shock-doctrine: a government country seeks reasons to destroy another country or area and then NGOs from the same country go to remedy the disasters
A question from a librarian from the U about the use of drones. My note: why did the SCSU library have to give up its drone?
Douglas County Library model. too resource intensive to continue
Marmot Library Network
ILS integrated library system – shared with other counties, same sever for the entire consortium. they have a programmer, viewfind, open source, discovery player, he customized viewfind community to viewfind plus. instead of using the ILS public access catalogue, they are using the Vufind interface
Caiifa Enki. public library – single access collection. they purchase ebooks from the publisher and they are using also the viewfind interface. but not integrated with the library catalogs. Kansas public library went from OverDrive to Viewfind. CA State library is funding for the time being this effort.
Harper Collins is too cumbersome and the reason to avoid working with them.
security issues. some of the material sent over ftp and immediately moved to sftp
decisions – use of internal resources only, if now – amazon
programmer used for the pilot. contracted programmers. lack of the ability to see the large picture. eventually hired a full time person, instead of outsourcing. RDA compliant MARC.
ONIX, spreadsheet MARC.
Decision about who to start with : public or academic.
attempt to keep pricing down –
own agreement with the customers, separate from the agreement with the Publisher
current development: web-based online reading, shared-consortial collections and SIP2 authentication
This presentation will begin on Thursday, July 28, 2016 at 11:00 AM Pacific Daylight Time.
Audience members may arrive 15 minutes in advance of this time.
Public sector data centers have unprecedented challenges and opportunities, and tomorrow’s demands remain uncertain. We know stakeholders, students, and citizens are all demanding more (e.g. modern services, innovative applications, cost-cutting efficiency), putting even greater strain on an organization’s infrastructure and expertise. It’s up to IT to make it all happen, and there’s simply no “one size fits all” solution to optimize data center efficiency. But can hyperconvergence help?
ACRL e-Learning webcast series: Learning Analytics – Strategies for Optimizing Student Data on Your Campus
This three-part webinar series, co-sponsored by the ACRL Value of Academic Libraries Committee, the Student Learning and Information Committee, and the ACRL Instruction Section, will explore the advantages and opportunities of learning analytics as a tool which uses student data to demonstrate library impact and to identify learning weaknesses. How can librarians initiate learning analytics initiatives on their campuses and contribute to existing collaborations? The first webinar will provide an introduction to learning analytics and an overview of important issues. The second will focus on privacy issues and other ethical considerations as well as responsible practice, and the third will include a panel of librarians who are successfully using learning analytics on their campuses.
Webcast One: Learning Analytics and the Academic Library: The State of the Art and the Art of Connecting the Library with Campus Initiatives
March 29, 2016
Learning analytics are used nationwide to augment student success initiatives as well as bolster other institutional priorities. As a key aspect of educational reform and institutional improvement, learning analytics are essential to defining the value of higher education, and academic librarians can be both of great service to and well served by institutional learning analytics teams. In addition, librarians who seek to demonstrate, articulate, and grow the value of academic libraries should become more aware of how they can dovetail their efforts with institutional learning analytics projects. However, all too often, academic librarians are not asked to be part of initial learning analytics teams on their campuses, despite the benefits of library inclusion in these efforts. Librarians can counteract this trend by being conversant in learning analytics goals, advantages/disadvantages, and challenges as well as aware of existing examples of library successes in learning analytics projects.
Learn about the state of the art in learning analytics in higher education with an emphasis on 1) current models, 2) best practices, 3) ethics, privacy, and other difficult issues. The webcast will also focus on current academic library projects and successes in gaining access to and inclusion in learning analytics initiatives on their campus. Benefit from the inclusion of a “short list” of must-read resources as well as a clearly defined list of ways in which librarians can leverage their skills to be both contributing members of learning analytics teams, suitable for use in advocating on their campuses.
student’s opinion of this process
benefits: self-assessment, personal learning, empwerment
analytics and data privacy – students are OK with harvesting the data (only 6% not happy)
8 in 10 are interested in personal dashboard, which will help them perform
Big Mother vs Big Brother: creepy vs helpful. tracking classes, helpful, out of class (where on campus, social media etc) is creepy. 87% see that having access to their data is positive
recognize metrics, assessment, analytics, data. visualization, data literacy, data science, interpretation
INSTRUCTION DEPARTMENT – N.B.
determine who is the key leader: director of institutional research, president, CIO
who does analyics services: institutional research, information technology, dedicated center
analytic maturity: data drivin, decision making culture; senior leadership commitment,; policy supporting (data ollection, accsess, use): data efficacy; investment and resourcefs; staffing; technical infrastrcture; information technology interaction
student success maturity: senior leader commited; fudning of student success efforts; mechanism for making student success decisions; interdepart collaboration; undrestanding of students success goals; advising and student support ability; policies; information systems
developing learning analytics strategy
understand institutional challenges; identify stakeholders; identify inhibitors/challenges; consider tools; scan the environment and see what other done; develop a plan; communicate the plan to stakeholders; start small and build
ways librarians can help
idenfify institu partners; be the partners; hone relevant learning analytics; participate in institutional analytics; identify questions and problems; access and work to improve institu culture; volunteer to be early adopters;
questions to ask: environmental scanning
do we have a learning analytics system? does our culture support? leaders present? stakeholders need to know?
questions to ask: Data
questions to ask: Library role
learning analytics & the academic library: the state of the art of connecting the library with campus initiatives
7 Things You Should Know About First-Generation Learning Analytics. Published:
causation versus correlation studies. speakers claims that it is difficult to establish causation argument. institutions try to predict as accurately as possible via correlation, versus “if you do that it will happen what.”
How do you present the idea of your research and intertwine it with data in a cohesive, interesting way? Join us in a short session to learn effective communication through infographics using data visualization and design.
Location: Miller Center 205
Wednesday, February 18 2-2:45pm
Thursday, March 19 11-11:45am
Tuesday, April 14, 10-10:45am
Thursday, April 30, 10-10:45am