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.
Tuesday, June 16, 2020 from 1:00 PM to 2:00 PM (CDT)
This event will be an expert panel considering research in/about VR. The experts in the panel are; Sam Reno, Géraldine Perriguey, Anthony Chaston PhD and Evelien Ydo who all have presented for the research track before (biographies below, see the EDVR YouTube channel for their previous presentations). The event will be highly interactive, where the audience is welcomed to introduce topics and questions for the panel to discuss. At the end of the event there will be some time to network as well.
The Educators in VR Research Team features researchers from across the spectrum of VR/AR/XR research and development, coming together to share their knowledge, techniques, and research and learn from each other. Join us to discuss the possibilities and potential of research in VR. We host regular meetups and workshops for discussion and learning.
Qualitative researchers: Does anyone have any general pointers on conducting qualitative work in this environment other than doing interviews or focus groups over Zoom? Example: I (normally) do a lot of participant observation work. Where and how will I do this or do it as well as I have done it?
At this moment, my focus is all on teaching. But if this situation becomes more prolonged, I need to figure out how to keep the research going too.
Webinar: tools for collaborative research and early discovery
Librarians have been at the forefront in promoting open access publishing options and informing their researchers about the open access landscape. Open access is increasingly recognized as embedded within the larger framework of open science. Consequently, library and librarian roles are expanding into new areas such as open data, open educational resources and open infrastructure.
In this webinar, Elsevier product managers will present tools that enable more inclusive, collaborative and transparent research.
•The library as publisher of OERs and OA journals with Digital Commons
•Open access content discovery in ScienceDirect and Scopus
•Open journal metrics: CiteScore, SNIP and SJR
•Publishing research outputs openly in Mendeley Data and SSRN
ELI Webinar | Reading & Digesting Scholarly Research: Tips to Save Time While Increasing Understanding
Tuesday, February 26 | 1:00p.m. – 2:00p.m. ET | Online
Reading and digesting scholarly research can be challenging when new journal issues, reports, and books are being released every day. Join Katie Linder, director of research for Oregon State University Ecampus, to learn some tips that will help you find scholarly research that’s applicable to your work, read that research more efficiently, evaluate the quality of scholarly research, and decide on the applicability of the research you’re reading to your day-to-day work. You’ll also have the opportunity to ask any questions you might have about reading and digesting scholarly research.
Find the scholarly research that is of most importance to your work
Read scholarly research efficiently
Evaluate the quality of scholarly research
Decide when and how to apply scholarly research results in your work
a quick list of some items to consider when you are researching:
Keep a research journal. It can be virtually or on paper, or both. keep track of all of the following: different section(s) with all of your ideas and thoughts, what you have accomplished, your goals on what you want to accomplish in the project in increments, your references and citations, your research meeting notes, your literature review notes, and topics that you wish to explore in further research. Also, write down everything: what you used, when you used it, what you tested on what day.
Stick with your research, even when things don’t go as planned or if you get unexpected results.
Research may feel awkward and confusing at first, even in the literature review phase when you are trying to find a research question or researching more methods that will help you in your research.
Don’t over think your research. have a clear, concise research question that you can always go back to stay on task. But, if you get side-tracked, right down any other related research questions that you can potentially go back to for future studies.
Always have a research mentor.
Use your resources that are in front of you. The internet is your friend. if you are having trouble finding a topic, article, or technical issue, seek out help, starting with a simple Google search. You can connect with research and reference librarians, freelance writers, ghost writers, potential references, authors and editors, local libraries and their catalogs
Using Social Media for Research – November 16 12:00 – 1:00 p.m.
1314 Social Sciences
Professor Lee-Ann Kastman Breuch (Writing Studies) and Michael Beckstrand (Mixed-Methods Research Associate, LATIS) will discuss how to retrieve, prepare, and analyze social media data for research projects. Using two case studies, Lee-Ann will share examples of a grounded theory analysis of blog, Twitter, and Facebook data. Michael will speak about the technical aspects of retrieving and managing social media data. Pizza will be provided. Learn more and register here.
This event is part of the 2018-19 Research Development Friday Roundtable Series organized by the CLA Research Development Team.