Aug
2020
VirtualReality for education in health care
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more of Rob Theriault in this IMS blog
Digital Literacy for St. Cloud State University
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more of Rob Theriault in this IMS blog
President Donald Trump signed an executive order to prohibit US companies from doing business with Tencent, which owns WeChat and nearly 50% of Epic Games, which makes Fortnite and Unreal Engine. Tik Tok has until September 20th to sell to Microsoft or another American company or it, too, will be banned. Is this because some Kpop fans on Tik Tok spoofed a Trump rally in Tulsa?
https://www.chronicle.com/newsletter/teaching/2020-08-13
worked together to analyze scientific information and visually represent it in a way that demonstrated their understanding.
When he tested his students, the scores among those who had created videos and visualizations were about 25 percent higher than those who had done traditional note-taking summaries.
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more on effective presentations in this IMS blog
https://blog.stcloudstate.edu/ims?s=presentations
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more on Apple Glass in this IMS blog
https://blog.stcloudstate.edu/ims?s=apple+glass
https://philonedtech.com/state-of-higher-ed-lms-market-for-us-and-canada-mid-year-2020-edition/
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more on LMS in this IMS blog
https://blog.stcloudstate.edu/ims?s=lms
https://ai.umich.edu/blog/mixr-studios-podcast-13-bryan-alexander/
link to the interview: https://open.spotify.com/episode/2iHzgr6vHYlV3OoYcZam6z
virtual reality as a great medium for storytelling
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More on XR in education in this IMS blog
https://blog.stcloudstate.edu/ims?s=xr+education
Jeremy Nelson, Director of XR Initiative and Bryan Alexander
Study: U.S. adults who mostly rely on social media for news are less informed, exposed to more conspiracies from r/technology
A new report from Pew Research makes an attempt to better understand U.S. adults who get their news largely from social media platforms, and compare their understanding of current events and political knowledge to those who use other sources, like TV, radio and news publications.
Got a new open access article out on the ways AI is embedding in education research. Well-funded precision education experts and learning engineers aim to collect psychodata, brain data and biodata as evidence of the embodied substrates of learning. https://t.co/CbdHReXUiz
— Ben Williamson (@BenPatrickWill) July 24, 2020
https://www.scienceopen.com/document/read?vid=992eaf61-35dd-454e-aa17-f9f8216b381b
This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.
Research across the sciences, humanities and social sciences is increasingly conducted through digital knowledge machines that are reconfiguring the ways knowledge is generated, circulated and used (Meyer and Schroeder, 2015).
Knowledge infrastructures, such as those of statistical institutes or research-intensive universities, have undergone significant digital transformation with the arrival of data-intensive technologies, with knowledge production now enacted in myriad settings, from academic laboratories and research institutes to commercial research and development studios, think tanks and consultancies. Datafied knowledge infrastructures have become hubs of command and control over the creation, analysis and exchange of data (Bigo et al., 2019).
The combination of AI and learning science into an AILSci research assemblage consists of particular forms of scientific expertise embodied by knowledge actors – individuals and organizations – identified by categories including science of learning, AIED, precision education and learning engineering.
Precision education overtly uses psychological, neurological and genomic data to tailor or personalize learning around the unique needs of the individual (Williamson, 2019). Precision education approaches include cognitive tracking, behavioural monitoring, brain imaging and DNA analysis.
Expert power is therefore claimed by those who can perform big data analyses, especially those able to translate and narrate the data for various audiences. Likewise, expert power in education is now claimed by those who can enact data-intensive science of learning, precision education and learning engineering research and development, and translate AILSci findings into knowledge for application in policy and practitioner settings.
the thinking of a thinking infrastructure is not merely a conscious human cognitive process, but relationally performed across humans and socio-material strata, wherein interconnected technical devices and other forms ‘organize thinking and thought and direct action’.
As an infrastructure for AILSci analyses, these technologies at least partly structure how experts think: they generate new understandings and knowledge about processes of education and learning that are only thinkable and knowable due to the computational machinery of the research enterprise.
Big data-based molecular genetics studies are part of a bioinformatics-led transformation of biomedical sciences based on analysing exceptional volumes of data (Parry and Greenhough, 2018), which has transformed the biological sciences to focus on structured and computable data rather than embodied evidence itself.
Isin and Ruppert (2019) have recently conceptualized an emergent form of power that they characterize as sensory power. Building on Foucault, they note how sovereign power gradually metamorphosed into disciplinary power and biopolitical forms of statistical regulation over bodies and populations.
Sensory power marks a shift to practices of data-intensive sensing, and to the quantified tracking, recording and representing of living pulses, movements and sentiments through devices such as wearable fitness monitors, online natural-language processing and behaviour-tracking apps. Davies (2019: 515–20) designates these as ‘techno-somatic real-time sensing’ technologies that capture the ‘rhythms’ and ‘metronomic vitality’ of human bodies, and bring about ‘new cyborg-type assemblages of bodies, codes, screens and machines’ in a ‘constant cybernetic loop of action, feedback and adaptation’.
Techno-somatic modes of neural sensing, using neurotechnologies for brain imaging and neural analysis, are the next frontier in AILSci. Real-time brainwave sensing is being developed and trialled in multiple expert settings.
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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims?s=artificial+intelligence
https://abcnews.go.com/US/alabama-students-throwing-covid-parties-infected-officials/story
What is #HyFlex and when should we use it in #HigherEducation ?
This Thursday Brian Beatty will join the Future Trends Forum to discuss the model.
You can join us for free from 2-3 pm EDT:https://t.co/SUkTnuouKZ#FTTE
— Bryan Alexander (@BryanAlexander) June 22, 2020
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more on hyflex in this IMS blog
https://blog.stcloudstate.edu/ims?s=hyflex