With AR and especially with what Apple refers to as Mixed Reality (MR), it’s great to be able to see an iPad Pro in front of you, but you need to be able to use it. You have to be able to pick up a virtual object and use it, or otherwise AR is no better than a 3D movie.
Apple’s proposed solution is described in “Manipulation of Virtual Objects using a Tracked Physical Object,” a patent application filed in January 2020 but only revealed this week. It suggests that truly mixing realities, in that the virtual object could be mapped onto an actual object in the real world.
Instructure has made it clear through their own language that they view the student data they aggregated as one of their chief assets, although they have also insisted that they do not use that data improperly. My note: “improperly” is relative and requires defining.
Yet an article published in the Virginia Journal of Law and Technology, titled “Transparency and the Marketplace for Student Data,” pointed out that there is “an overall lack of transparency in the student information commercial marketplace and an absence of law to protect student information.” As such, some students at the University of California are concerned that — despite reassurances to the contrary — their institution’s new financial relationship with Thoma Bravo will mean their personal data can be sold or otherwise misused.
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
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.
Mariya P. Ivancheva, Rebecca Swartz, Neil P. Morris, Sukaina Walji, Bronwen J. Swinnerton, Taryn Coop & Laura Czerniewicz(2020)Conflicting logics of online higher education,British Journal of Sociology of Education,DOI: 10.1080/01425692.2020.1784707
The advent of massive open online courses and online degrees offered via digital platforms has occurred in a climate of austerity. Public universities worldwide face challenges to expand their educational reach, while competing in international rankings, raising fees and generating third-stream income. Online forms of unbundled provision offering smaller flexible low-cost curricular units have promised to disrupt this system. Yet do these forms challenge existing hierarchies in higher education and the market logic that puts pressure on universities and public institutions at large in the neoliberal era? Based on fieldwork in South Africa, this article explores the perceptions of senior managers of public universities and of online programme management companies. Analysing their considerations around unbundled provision, we discuss two conflicting logics of higher education that actors in structurally different positions and in historically divergent institutions use to justify their involvement in public–private partnerships: the logic of capital and the logic of social relevance.
Unbundling – the disaggregation of educational provision and its delivery, often via digital technologies
Luc Boltanski and Laurent Thévenot’s (2006) framework of different orders of justification, connecting them to the sociological literature on institutional logics
We suggest that more explicit and nuanced national and institutional policies need to be produced around unbundled provision, which are cognisant of emerging trends in and dangers to the evolution of unbundling at public universities.
Unbundling the traditional university ‘bundle’ affects not only property, services and facilities, but also administration, evaluation, issuing credentials and even teaching (Wallhaus 2000, 22). This process involves separating educational provision (e.g. degree programmes) into component parts (e.g. courses) for delivery by multiple stakeholders, often using digital approaches (Swinnerton et al. 2018). Universities can unbundle on their own, offering individual credit-bearing modules outside bounded disciplinary curricula, or in partnership with OPM providers, offering MOOCs or credit-bearing courses or programmes. Proponents of unbundling suggest that the disaggregation of television and music production and its re-aggregation as on-demand digital content like Netflix or Spotify could represent a template for universities (Craig 2015; McIntosh 2018).
The introduction of market logic into the sector happens even if higher education is a stratified positional pseudo-market with scarce excludible resources only available to groups with access to a few prestigious institutions; its outcomes and value are difficult to measure in purely economic terms
Under accelerated marketisation, Tomlinson (2018, 714 and 724) argues, higher education is reduced to the latter frame and measured in terms of income generation, employability, consumption and performativity. Building on this framework, and relating it to unbundling, we identify the emergence of two organisational logics of higher education: the logic of social relevance and the logic of capital.
Institutional logics are ‘supra-organizational patterns of activity by which individuals and organizations produce and reproduce their material subsistence … [and] symbolic systems, ways of ordering reality… rendering experience of time and space meaningful’ (Friedland and Alford 1991, 243). Unlike new institutionalism, which remained focused on processes of institutional isomorphism or the replacement of a static single logic by another, the institutional logics perspective offers a more dynamic multi-level view: a plurality of logics coexist in complex interrelations within organisational fields like higher education
the HyFlex model for the fall… reflects a rift between administrators and professors, who are raising alarms over the health risks of teaching in person, and about the logistical, technical, and pedagogical complications of the model itself. Search HyFlex on Facebook and Twitter and you’ll come across comments like this one: “Whoever the hell thought of this is a bean counter, not an educator, and an idiot.”
Teaching experts and others familiar with hybrid teaching say that HyFlex can work, but it requires effective technology, careful planning, instructional support, and creative course design.
“If HyFlex is part of the plan, it has to be done with will faculty participation,” says Brian Beatty, an associate professor of instructional technologies at San Francisco State, who created the model. “Otherwise, if it’s top down and the administration is saying, We’re doing this, then the faculty are saying, But why are we doing this?”
Much of what bothers professors about the push for HyFlex is that so many details about its mechanics remain ill defined. And assumptions about its value seem rooted in a particular idea of teaching, one where the professor stands at the front of a classroom and lectures.
“We are the ones holding the bag if this does not work, or if it’s chaos,” says Michelle Miller, a psychology professor at Northern Arizona University and author of Minds Online: Teaching Effectively With Technology.
Miller is a fan of the original HyFlex model from San Francisco State, but says that colleges need to be mindful that the conditions under which it’s now being adapted — quickly, at scale, and without giving students much choice — will limit its effectiveness.
To work effectively, she says, hybrid teaching requires a lot of support, such as having teaching assistants help manage the complexities of working simultaneously with two different audiences. Otherwise it risks becoming a “lecture-centric, passive consumption view of learning.” That goes against years of hard work faculty members have been doing to make their classrooms more inclusive, active, and engaged.
To help think through pedagogical challenges, faculty groups are testing out teaching strategies, some departments meet weekly to discuss course design, and a student-leadership team is providing feedback and creating online tools to help their peers learn effectively online.Even so, the process has been challenging and frustrating at times for faculty members. Professors are both looking for templates and wanting to maintain control over their courses, which inevitably creates tension with the administration.
LITA listserv exchange on “Raspberry PI Counter for Library Users”
On 7/10/20, 10:05 AM, “lita-l-request@lists.ala.org on behalf of Hammer, Erich F” <lita-l-request@lists.ala.org on behalf of erich@albany.edu> wrote:
Jason,
I think that is a very interesting project. If I understand how it works (comparing reference images to live images), it should still work if a “fuzzy” or translucent filter were placed on the lens as a privacy measure, correct? You could even make the fuzzy video publicly accessible to prove to folks that privacy is protected.
If that’s the case, IMHO, it really is a commercially viable idea and it would have a market far beyond libraries. Open source code and hardware designs and sales of pre-packaged hardware and support. Time for some crowdsource funding! 🙂
My note:
In 2018, following the university president’s call for ANY possible savings, the library administrator was send a proposal requesting information regarding the license for the current library counters and proposing the save the money for the license by creating an in-house Arduino counter. The blueprints for such counter were share (as per another LITA listserv exchange). SCSU Physics professor agreement to lead the project was secured as well as the opportunity for SCSU Physics students to develop the project as part of their individual study plan. The proposal was never addressed neither by the middle nor the upper management.
In April 2020, the company claimed its G Suite for Education products were used by 120 million students and users across the world. More than 100 million use Classroom, its online collaboration and learning management platform. Over 40 million students and educators across the globe now use Chromebooks.
