Respondents on the 2016 POD Membership Survey indicated a strong need for learning center management and leadership skills. This session, facilitated by four center directors from very different institutions, responds to this need. Session participants will examine: 1) management and leadership responsibilities, especially in the context of continual change; 2) strategic alignment of the center’s work with institutional mission; and 3) evaluation of center work and demonstration of impact. Participants will leave with an individualized professional development plan, practical tools, and guiding questions that enable them to seek out relevant sessions and colleagues during the conference.
In this workshop, we explore powerful model (Symposium) for engaging faculty in campus initiatives and supporting them to take a more active role in leading during times of change. We have successfully used symposium to broaden faculty participation in change initiatives, connecting this work to what matters most to faculty and providing avenues for more inclusive collaboration across disciplines and divisions. Much of the workshop will be devoted to helping participants (1) identify areas where they can lead change on their campuses and (2) develop a draft plan for using symposium to increase faculty engagement in these efforts.
Faculty are often unable to complete a proper learner analysis because they know little about the students that comprise their classlist. At our university, we have been surveying incoming students for five years to collect enhanced demographic data and for the past two years have been sharing aggregate, anonymous data with faculty. Resources have been provided on how to make sense of the data for teaching purposes. In this study, we conducted focus groups with faculty to learn how they have used the data and resources and also to find out what additional data would further support their teaching. (My note: big data in education, as discussed by Nancy Sims keynote at LITA Nov, 2018)
Summative peer review of teaching (SPRT) is used in many higher education institutions. Unfortunately, the evaluative “power” of SPRT for making high-stakes career decisions can be limited due to lack of meaningful criteria and faculty resistance (Chism, 2008). To address this situation, our teaching and learning centre engaged in a collaborative culture-change initiative to develop a rubric for SPRT that would serve the University-wide committee with responsibility for final recommendation on matters of promotion and tenure. In this session, we discuss our collaborative process, debrief challenges and how we addressed and/or anticipated these, and share the SPRT rubric. (My Note: CETL)
This session will introduce participants to the gamification of faculty development through an interactive small group design scenario that asks participants to take a traditional faculty development experience and then gamify it using the gamification design framework [1]. Gamification involves the use of game design elements and experiences in non-gaming environments. When applied in faculty development settings, gamification has the potential to encourage faculty engagement and motivation and can lead to behavioral change that can impact their teaching. (My note: ask me; i have been trying to educate CETL directors for the past four years on this opportunity)
What new organizational models and practices do instructional design teams need to adopt?
How can instructional designers best make use of the increasing amount of learning data that is available?
What kinds of evidence-based practices make the most sense for instructional designers?
What are some professional development approaches that provide structure for instructional designers to share their mutual areas of expertise, while focusing on key areas of professional growth?
Learning technologists and instructional designers
Campus teaching and learning center directors and staff
Faculty members and instructors
Senior teaching and learning administrators (e.g., deans, provost office staff)
Librarians
Presentation Sessions: Sessions designed to provide an overview of specific topic areas and successful emerging approaches related to the focus session theme immediately followed by opportunities to interact one-on-one with session presenters.
Project Rounds: A series of institutional cases/examples presented in a sequential, fast-paced format exploring a single project, emerging technology, or campus initiative. Project rounds will be followed by an opportunity for separate discussion with each of the presenters.
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 difficulties.
Technical issues can generally be divided into two broad categories: hardware
problems and software problems. There is, of course, a common third category:
human error.
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
Sejnowski, T. J. (2018). The Deep Learning Revolution. Cambridge, MA: The MIT Press.
How deep learning―from Google Translate to driverless cars to personal cognitive assistants―is changing our lives and transforming every sector of the economy.
The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.
Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies.
Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution(out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence.
Machine learning is a very large field and goes way back. Originally, people were calling it “pattern recognition,” but the algorithms became much broader and much more sophisticated mathematically. Within machine learning are neural networks inspired by the brain, and then deep learning. Deep learning algorithms have a particular architecture with many layers that flow through the network. So basically, deep learning is one part of machine learning and machine learning is one part of AI.
December 2012 at the NIPS meeting, which is the biggest AI conference. There, [computer scientist] Geoff Hinton and two of his graduate students showed you could take a very large dataset called ImageNet, with 10,000 categories and 10 million images, and reduce the classification error by 20 percent using deep learning.Traditionally on that dataset, error decreases by less than 1 percent in one year. In one year, 20 years of research was bypassed. That really opened the floodgates.
The inspiration for deep learning really comes from neuroscience.
AlphaGo, the program that beat the Go champion included not just a model of the cortex, but also a model of a part of the brain called the basal ganglia, which is important for making a sequence of decisions to meet a goal. There’s an algorithm there called temporal differences, developed back in the ‘80s by Richard Sutton, that, when coupled with deep learning, is capable of very sophisticated plays that no human has ever seen before.
there’s a convergence occurring between AI and human intelligence. As we learn more and more about how the brain works, that’s going to reflect back in AI. But at the same time, they’re actually creating a whole theory of learning that can be applied to understanding the brain and allowing us to analyze the thousands of neurons and how their activities are coming out. So there’s this feedback loop between neuroscience and AI
Summary This short paper lays out an attempt to measure how much activity from Russian state-operated accounts released in the dataset made available by Twitter in October 2018 was targeted at the United Kingdom. Finding UK-related Tweets is not an easy task. By applying a combination of geographic inference, keyword analysis and classification by algorithm, we identified UK-related Tweets sent by these accounts and subjected them to further qualitative and quantitative analytic techniques.
We find:
There were three phases in Russian influence operations : under-the-radar account building, minor Brexit vote visibility, and larger-scale visibility during the London terror attacks.
Russian influence operations linked to the UK were most visible when discussing Islam . Tweets discussing Islam over the period of terror attacks between March and June 2017 were retweeted 25 times more often than their other messages.
The most widely-followed and visible troll account, @TEN_GOP, shared 109 Tweets related to the UK. Of these, 60 percent were related to Islam .
The topology of tweet activity underlines the vulnerability of social media users to disinformation in the wake of a tragedy or outrage.
Focus on the UK was a minor part of wider influence operations in this data . Of the nine million Tweets released by Twitter, 3.1 million were in English (34 percent). Of these 3.1 million, we estimate 83 thousand were in some way linked to the UK (2.7%). Those Tweets were shared 222 thousand times. It is plausible we are therefore seeing how the UK was caught up in Russian operations against the US .
Influence operations captured in this data show attempts to falsely amplify other news sources and to take part in conversations around Islam , and rarely show attempts to spread ‘fake news’ or influence at an electoral level.
On 17 October 2018, Twitter released data about 9 million tweets from 3,841 blocked accounts affiliated with the Internet Research Agency (IRA) – a Russian organisation founded in 2013 and based in St Petersburg, accused of using social media platforms to push pro-Kremlin propaganda and influence nation states beyond their borders, as well as being tasked with spreading pro-Kremlin messaging in Russia. It is one of the first major datasets linked to state-operated accounts engaging in influence operations released by a social media platform.
Conclusion
This report outlines the ways in which accounts linked to the Russian Internet ResearchAgency (IRA) carried out influence operations on social media and the ways their operationsintersected with the UK.The UK plays a reasonably small part in the wider context of this data. We see two possibleexplanations: either influence operations were primarily targeted at the US and British Twitterusers were impacted as collate, or this dataset is limited to US-focused operations whereevents in the UK were highlighted in an attempt to impact US public, rather than a concertedeffort against the UK. It is plausible that such efforts al so existed but are not reflected inthis dataset.Nevertheless, the data offers a highly useful window into how Russian influence operationsare carried out, as well as highlighting the moments when we might be most vulnerable tothem.Between 2011 and 2016, these state-operated accounts were camouflaged. Through manualand automated methods, they were able to quietly build up the trappings of an active andwell-followed Twitter account before eventually pivoting into attempts to influence the widerTwitter ecosystem. Their methods included engaging in unrelated and innocuous topics ofconversation, often through automated methods, and through sharing and engaging withother, more mainstream sources of news.Although this data shows levels of electoral and party-political influence operations to berelatively low, the day of the Brexit referendum results showed how messaging originatingfrom Russian state-controlled accounts might come to be visible – on June 24th 2016, we believe UK Twitter users discussing the Brexit Vote would have encountered messages originating from these accounts.As early as 2014, however, influence operations began taking part in conversations aroundIslam, and these accounts came to the fore during the three months of terror attacks thattook place between March and June 2017. In the immediate wake of these attacks, messagesrelated to Islam and circulated by Russian state-operated Twitter accounts were widelyshared, and would likely have been visible in the UK.The dataset released by Twitter begins to answer some questions about attempts by a foreignstate to interfere in British affairs online. It is notable that overt political or electoralinterference is poorly represented in this dataset: rather, we see attempts at stirring societaldivision, particularly around Islam in the UK, as the messages that resonated the most overthe period.What is perhaps most interesting about this moment is its portrayal of when we as socialmedia users are most vulnerable to the kinds of messages circulated by those looking toinfluence us. In the immediate aftermath of terror attacks, the data suggests, social mediausers were more receptive to this kind of messaging than at any other time.
It is clear that hostile states have identified the growth of online news and social media as aweak spot, and that significant effort has gone into attempting to exploit new media toinfluence its users. Understanding the ways in which these platforms have been used tospread division is an important first step to fighting it.Nevertheless, it is clear that this dataset provides just one window into the ways in whichforeign states have attempted to use online platforms as part of wider information warfare
and influence campaigns. We hope that other platforms will follow Twitter’s lead and release
similar datasets and encourage their users to proactively tackle those who would abuse theirplatforms.
Digital Fluency: Preparing Learners for 21st Century Digital Citizenship Eighty-five percent of the jobs available in 2030 do not yet exist. How does higher education prepare our learners for careers that don’t yet exist? One opportunity is to provide our students with opportunities to grow their skills in creative problem solving, critical thinking, resiliency, novel thinking, social intelligence, and excellent communication skills. Instructional designers and faculty can leverage the framework of digital fluency to create opportunities for learners to practice and hone the skills that will prepare them to be 21st-century digital citizens. In this session, join a discussion about several fluencies that comprise the overarching framework for digital fluency and help to define some of your own.
Dr. Jennifer Sparrow, Senior Director for Teaching and Learning with Technology and Affiliate Assistant Professor of Learning, Design, and Technology at Penn State. The webinar will take place on Friday, November 9th at 11am EST/4pm UTC (login details below)
how DF is different from DLiteracy? enable students define how new knowledge can be created through technology. Not only read and write, but create poems, stories, if analogous w learning a language. slide 4 in https://www.slideshare.net/aidemoreto/vr-library
communication fluency. be able to choose the correct media. curiosity/failure fluency; creation fluency (makerspace: create without soldering, programming, 3Dprinting. PLA filament-corn-based plastic; Makers-in-residence)
immersive fluency: video 360, VR and AR. enable student to create new knowledge through environments beyond reality. Immersive Experiences Lab (IMEX). Design: physical vs virtual spaces.
Data fluency: b.book. how to create my own textbook
rubrics and sample projects to assess digital fluency.
What is Instructional Design 2.0 or 3.0? deep knowledge and understanding of faculty development. second, once faculty understands the new technology, how does this translate into rework of curriculum? third, the research piece; how to improve to be ready for the next cycle. a partnership between ID and faculty.
At WMU, the Libraries is partnering with our central OIT to host a VR lab in the main library. My partnering co-director, Kevin, is really the subject matter expert but I’m managing a lot of the day-to-day operations. Kevin is programming and experimenting with all kinds of hardware but we decided to use Oculus Rifts in our lab primarily because of the greater durability of the hand controllers (compared especially to the Vive). We’re getting all of our games through the Oculus store and have plans to expand into Steam or another provider but haven’t done so yet. We currently have 40+ titles available for gaming and educational purposes. We also teach content creation using Unity, Maya, Blender, and a handful of other tools.
China has started ranking citizens with a creepy ‘social credit’ system — here’s what you can do wrong, and the embarrassing, demeaning ways they can punish you