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.
International Data Corporation says it expects the number of AI jobs globally to grow 16% this year.
a new report released Wednesday, IBM found the majority (85%) of AI professionals think the industry has become more diverse over recent years
3,200 people surveyed across North America, Europe and India, 86% said they are now confident in AI systems’ ability to make decisions without bias.
A plurality of men (46%) said they became interested in a tech career in high school or earlier, while a majority of women (53%) only considered it a possible path during their undergraduate degree or grad school.
Algorithmic test proctoring’s settings have discriminatory consequences across multiple identities and serious privacy implications.
While racist technology calibrated for white skin isn’t new (everything from photography to soap dispensers do this), we see it deployed through face detection and facial recognition used by algorithmic proctoring systems.
While some test proctoring companies develop their own facial recognition software, most purchase software developed by other companies, but these technologies generally function similarly and have shown a consistent inability to identify people with darker skin or even tell the difference between Chinese people. Facial recognition literally encodes the invisibility of Black people and the racist stereotype that all Asian people look the same.
As Os Keyes has demonstrated, facial recognition has a terrible history with gender. This means that a software asking students to verify their identity is compromising for students who identify as trans, non-binary, or express their gender in ways counter to cis/heteronormativity.
These features and settings create a system of asymmetric surveillance and lack of accountability, things which have always created a risk for abuse and sexual harassment. Technologies like these have a long history of being abused, largely by heterosexual men at the expense of women’s bodies, privacy, and dignity.
my note: I am repeating this for years
Sean Michael Morris and Jesse Stommel’s ongoing critique of Turnitin, a plagiarism detection software, outlines exactly how this logic operates in ed-tech and higher education: 1) don’t trust students, 2) surveil them, 3) ignore the complexity of writing and citation, and 4) monetize the data.
Technological Solutionism
Cheating is not a technological problem, but a social and pedagogical problem.
Our habit of believing that technology will solve pedagogical problems is endemic to narratives produced by the ed-tech community and, as Audrey Watters writes, is tied to the Silicon Valley culture that often funds it. Scholars have been dismantling the narrative of technological solutionism and neutrality for some time now. In her book “Algorithms of Oppression,” Safiya Umoja Noble demonstrates how the algorithms that are responsible for Google Search amplify and “reinforce oppressive social relationships and enact new modes of racial profiling.”
Anna Lauren Hoffmann, who coined the term “data violence” to describe the impact harmful technological systems have on people and how these systems retain the appearance of objectivity despite the disproportionate harm they inflict on marginalized communities.
This system of measuring bodies and behaviors, associating certain bodies and behaviors with desirability and others with inferiority, engages in what Lennard J. Davis calls the Eugenic Gaze.
Higher education is deeply complicit in the eugenics movement. Nazism borrowed many of its ideas about racial purity from the American school of eugenics, and universities were instrumental in supporting eugenics research by publishing copious literature on it, establishing endowed professorships, institutes, and scholarly societies that spearheaded eugenic research and propaganda.
Information literacies (media literacy, Research Literacy, digital literacy, visual literacy, financial literacy, health literacy, cyber wellness, infographics, information behavior, trans-literacy, post-literacy)
Information Literacy and academic libraries
Information Literacy and adult education
Information Literacy and blended learning
Information Literacy and distance learning
Information Literacy and mobile devices
Information Literacy and Gamification
Information Literacy and public libraries
Information Literacy in Primary and Secondary Schools
Information Literacy and the Knowledge Economy
Information Literacy and Lifelong Learning
Information Literacy and the Information Society
Information Literacy and the Multimedia Society
Information Literacy and the Digital Society
Information Literacy in the modern world (e.g trends, emerging technologies and innovation, growth of digital resources, digital reference tools, reference services).
The future of Information Literacy
Workplace Information Literacy
Librarians as support to the lifelong learning process
Digital literacy, Digital Citizenship
Digital pedagogy and Information Literacy
Information Literacy Needs in the Electronic Resource Environment
Integrating Information Literacy into the curriculum
Putting Information Literacy theory into practice
Information Literacy training and instruction
Instructional design and performance for Information Literacy (e.g. teaching practice, session design, lesson plans)
Information Literacy and online learning (e.g. self-paced IL modules, online courses, Library Guides)
Information Literacy and Virtual Learning Environments
Supporting users need through library 2.0 and beyond
Digital empowerment and reference work
Information Literacy across the disciplines
Information Literacy and digital preservation
Innovative IL approaches
Student engagement with Information Literacy
Action Literacy
Information Literacy, Copyright and Intellectual Property
Information Literacy and Academic Writing
Media and Information Literacy – theoretical approaches (standards, assessment, collaboration, etc.)
The Digital Competence Framework 2.0
Information Literacy theory (models, standards, indicators, Moscow Declaration etc.)
Information Literacy and Artificial intelligence
Information Literacy and information behavior
Information Literacy and reference services: cyber reference services, virtual reference services, mobile reference services
Information Literacy cultural and contextual approaches
Information Literacy and Threshold concepts
Information Literacy evaluation and assessment
Information Literacy in different cultures and countries including national studies
Information Literacy project management
Measuring in Information Literacy instruction assessment
New aspects of education/strategic planning, policy, and advocacy for Information Literacy in a digital age
Information Literacy and the Digital Divide
Policy and Planning for Information Literacy
Branding, promotion and marketing for Information Literacy
Cross –sectorial; and interdisciplinary collaboration and partnerships for Information Literacy
Leadership and Governance for Information Literacy
Strategic planning for IL
Strategies in e-learning to promote self-directed and sustainable learning in the area of Information Literacy skills.
Project Information Literacy, a nonprofit research institution that explores how college students find, evaluate and use information. It was commissioned by the John S. and James L. Knight Foundation and The Harvard Graduate School of Education.
focus groups and interviews with 103 undergraduates and 37 faculty members from eight U.S. colleges.
To better equip students for the modern information environment, the report recommends that faculty teach algorithm literacy in their classrooms. And given students’ reliance on learning from their peers when it comes to technology, the authors also suggest that students help co-design these learning experiences.
While informed and critically aware media users may see past the resulting content found in suggestions provided after conducting a search on YouTube, Facebook, or Google, those without these skills, particularly young or inexperienced users, fail to realize the culpability of underlying algorithms in the resultant filter bubbles and echo chambers (Cohen, 2018).
Media literacy education is more important than ever. It’s not just the overwhelming calls to understand the effects of fake news or addressing data breaches threatening personal information, it is the artificial intelligence systems being designed to predict and project what is perceived to be what consumers of social media want.
it’s time to revisit the Eight Key Concepts of media literacy with an algorithmic focus.
Literacy in today’s online and offline environments “means being able to use the dominant symbol systems of the culture for personal, aesthetic, cultural, social, and political goals” (Hobbs & Jensen, 2018, p 4).
The upside for businesses is that this new, “anonymized” video no longer gives away the exact identity of a customer—which, Perry says, means companies using D-ID can “eliminate the need for consent” and analyze the footage for business and marketing purposes. A store might, for example, feed video of a happy-looking white woman to an algorithm that can surface the most effective ad for her in real time.
Three leading European privacy experts who spoke to MIT Technology Review voiced their concerns about D-ID’s technology and its intentions. All say that, in their opinion, D-ID actually violates GDPR.
Education scholars have already critiqued PISA as a valid global measure of education quality — but analysts also are skeptical about the selective participation of Chinese students from wealthier schools.
Second, Chinese students, on average, study 55 hours a week — also No. 1 among PISA-participating countries. This was about 20 hours more than students in Finland, the country that PISA declared to have the highest learning efficiency, or reading-test-score points per hour spent studying.
But PISA analysis also revealed that Chinese students are among the least satisfied with their lives.
Students look overseas for a more well-rounded education
Their top destination of choice, by far, is the United States. The 1.1 million or so foreign students in the United States in 2018 included 369,500 Chinese college students
hostility in U.S.-China relations could dampen the appeal of a U.S. education. Britain, in fact, recorded a 30 percent surge in Chinese applicants in 2019, challenging the U.S. global dominance in higher education.
Immigrant students, who made up 23 percent of all U.S. students taking PISA, performed significantly better compared to their native-born peers in the United States than they did on average throughout the OECD countries.
The survey found that 15-year-old students from Beijing, Shanghai, and the eastern provinces of Jiangsu and Zhejiang ranked top for all three core subjects, achieving the highest level 4 rating.
Students from the United States were ranked level 3 for reading and science, and level 2 for math, while teens from Britain scored a level 3 ranking in all three categories.
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Looking for Post-PISA Answers? Here’s What Our Obsession With Test Scores Overlooks
Andreas Schelicher, director of education and skills at the OECD—the Paris-based organization behind PISA wrote that “students who disagreed or strongly disagreed with the statement ‘Your intelligence is something about you that you can’t change very much’ scored 32 points higher in reading than students who agreed or strongly agreed.”
Those results are similar to recent findings published by Carol Dweck, a Stanford education professor who is often credited with making growth mindset a mainstream concept.
“Growth mindset is a very important thing that makes us active learners, and makes us invest in our personal education,” Schleicher states. “If learning isn’t based on effort and intelligence is predetermined, why would anyone bother?”
It’s “absolutely fascinating” to see the relationship between teachers’ enthusiasm, students’ social-emotional wellbeing and their learning outcomes, Schleicher notes. As one example, he noted in his summary report that “in most countries and economies, students scored higher in reading when they perceived their teachers as more enthusiastic, especially when they said their teachers were interested in the subject.”
In other words, happy teachers lead to better results. That’s hardly a surprising revelation, says Scheleicher. But professional development support is one thing that can sometimes be overlooked by policymakers when so much of the focus is on test scores.
Blended Reality, a cross-curricular applied research program through which they create interactive experiences using virtual reality, augmented reality and 3D printing tools. Yale is one of about 20 colleges participating in the HP/Educause Campus of the Future project investigating the use of this technology in higher education.
Interdisciplinary student and professor teams at Yale have developed projects that include using motion capture and artificial intelligence to generate dance choreography, converting museum exhibits into detailed digital replicas, and making an app that uses augmented reality to simulate injuries on the mannequins medical students use for training.
The perspectives and skills of art and humanities students have been critical to the success of these efforts, says Justin Berry, faculty member at the Yale Center for Collaborative Arts and Media and principal investigator for the HP Blended Reality grant.
Artificial intelligence and mixed reality have driven demand in learning games around the world, according to a new report by Metaari. A five-year forecast has predicted that educational gaming will reach $24 billion by 2024, with a compound annual growth rate of 33 percent and a quadrupling of revenues. Metaari is an analyst firm that tracks advanced learning technology.
