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.
more on AI in this IMS blog
SoLAR Webinar “Learning analytics adoption in Higher Education: Reviewing six years of experience at Open University UK”
presented by Prof. Bart Rienties from the Open University, The United Kingdom.
To register, go to https://www.eventbrite.com.au/e/learning-analytics-adoption-in-higher-education-reviewing-six-years-of-experience-at-open-registration-105611406560
Time and date: Thursday, Jun 11, 2020, 5:00 PM – 6:00 PM Central European time
(11:00 AM–12:00 PM Eastern US time, 8:00 AM–9:00 AM Pacific US time, 4:00 PM–5:00 PM London, UK Time)
Location: Zoom (meeting URL provided in the registration email)
more on learning analytics in this IMS blog
Learning analytics, student satisfaction, and student performance at the UK Open University
Rienties and his team linked 151 modules (courses) and 111,256 students with students’ behaviour, satisfaction and performance at the Open University UK, using multiple regression models.
There is little correlation between student course evaluations and student performance
The design of the course matters
Student feedback on the quality of a course is really important but it is more useful as a conversation between students and instructors/designers than as a quantitative ranking of the quality of a course. In fact using learner satisfaction as a way to rank teaching is highly misleading. Learner satisfaction encompasses a very wide range of factors as well as the teaching of a particular course.
this research provides quantitative evidence of the importance of learning design in online and distance teaching. Good design leads to better learning outcomes. We need a shift in the power balance between university and college subject experts and learning designers resulting in the latter being treated as at least equals in the teaching process.
more on learning analytics in this IMS blog
Chad, K., & Anderson, H. (2017). The new role of the library in teaching and learning outcomes
(p. ). Higher Education Library Technology. https://doi.org/10.13140/rg.2.2.14688.89606/1
p. 4 “Modern university libraries require remote access for large numbers of concurrent users, with fewer authentication steps and more flexible digital rights management (DRM) to satisfy student demand”. They found the most frequent problem was that core reading list titles were not available to libraries as e-books.
p. 5 Overcoming the “textbook taboo”
In the US, academic software firm bepress notes that, in response to increased student textbook costs: “Educators, institutions, and even state legislators are turning their attention toward Open Educational Resources (OER)” in order to save students money while increasing engagement and retention. As a result bepress has developed its infrastructure to host and share OER within and across institutions.21 The UMass Library Open Education Initiative estimates it has saved the institution over $1.3 million since its inception in 2011. 22 Other textbook initiatives include SUNY Open Textbooks, developed by the State University of New York Libraries, which has already published 18 textbooks, and OpenStax, developed by Rice University.
p.5. sceptics about OER rapid progress still see potential in working with publishers.
Knowledge Unlatched 23 is an example of this kind of collaboration: “We believe that by working together libraries and publishers can create a sustainable route to Open Access for scholarly books.” Groups of libraries contribute to fund publication though a crowdfunding platform. The consortium pays a fixed upfront fee for the publisher to publish the book online under a Creative Commons license.
p.6.Technology: from library systems to educational technology.The rise of the library centric reading list system
big increase in the number of universities in the UK, Australia and New Zealand deploying library reading lists solutions.The online reading list can be seen as a sort of course catalogue that gives the user a (sometimes week-by-week) course/module view on core resources and provides a link to print holdings information or the electronic full text. It differs significantly from the integrated library system (ILS) ‘course reserve’ module, notably by providing access to materials beyond the items in the library catalogue. Titles can be characterised, for example as ‘recommended’ or ‘essential’ reading and citations annotated.
Reading list software brings librarians and academics together into a system where they must cooperate to be effective. Indeed some librarians claim that the reading list system is a key library tool for transforming student learning.
Higher education institutions, particularly those in Australia, New Zealand and some other parts of Europe (including the UK) are more likely to operate a reading list model, supplying students with a (sometimes long) list of recommended titles.
p.8. E-book platforms (discusses only UK)
p.9. Data: library management information to learning analytics
“Strong digital leadership is a key feature of effective educational organisations and its absence can be a significant barrier to progress. The digital agenda is therefore a leadership issue”. 48 (Rebooting learning for the digital age: What next for technology-enhanced higher education? Sarah Davies, Joel Mullan, Paul Feldman. Higher Education Policy Institute (HEPI) Report 93. February 2017. )
A merging of LibTech and EdTech
The LITA discussion is under RE: [lita-l] Anyone Running Multiple Discovery Layers?
more on academic library in this IMS blog
4 K-12 Ed Tech Trends to Watch in 2018
Analytics, virtual reality, makerspaces and digital citizenship top the minds of education experts for the year.
The EDUCAUSE Learning Initiative has just launched its 2018 Key Issues in Teaching and Learning Survey, so vote today: http://www.tinyurl.com/ki2018.
Each year, the ELI surveys the teaching and learning community in order to discover the key issues and themes in teaching and learning. These top issues provide the thematic foundation or basis for all of our conversations, courses, and publications for the coming year. Longitudinally they also provide the way to track the evolving discourse in the teaching and learning space. More information about this annual survey can be found at https://www.educause.edu/eli/initiatives/key-issues-in-teaching-and-learning.
ACADEMIC TRANSFORMATION (Holistic models supporting student success, leadership competencies for academic transformation, partnerships and collaborations across campus, IT transformation, academic transformation that is broad, strategic, and institutional in scope)
ACCESSIBILITY AND UNIVERSAL DESIGN FOR LEARNING (Supporting and educating the academic community in effective practice; intersections with instructional delivery modes; compliance issues)
ADAPTIVE TEACHING AND LEARNING (Digital courseware; adaptive technology; implications for course design and the instructor’s role; adaptive approaches that are not technology-based; integration with LMS; use of data to improve learner outcomes)
COMPETENCY-BASED EDUCATION AND NEW METHODS FOR THE ASSESSMENT OF STUDENT LEARNING (Developing collaborative cultures of assessment that bring together faculty, instructional designers, accreditation coordinators, and technical support personnel, real world experience credit)
DIGITAL AND INFORMATION LITERACIES (Student and faculty literacies; research skills; data discovery, management, and analysis skills; information visualization skills; partnerships for literacy programs; evaluation of student digital competencies; information evaluation)
EVALUATING TECHNOLOGY-BASED INSTRUCTIONAL INNOVATIONS (Tools and methods to gather data; data analysis techniques; qualitative vs. quantitative data; evaluation project design; using findings to change curricular practice; scholarship of teaching and learning; articulating results to stakeholders; just-in-time evaluation of innovations). here is my bibliographical overview on Big Data (scroll down to “Research literature”: http://blog.stcloudstate.edu/ims/2017/11/07/irdl-proposal/ )
EVOLUTION OF THE TEACHING AND LEARNING SUPPORT PROFESSION (Professional skills for T&L support; increasing emphasis on instructional design; delineating the skills, knowledge, business acumen, and political savvy for success; role of inter-institutional communities of practices and consortia; career-oriented professional development planning)
FACULTY DEVELOPMENT (Incentivizing faculty innovation; new roles for faculty and those who support them; evidence of impact on student learning/engagement of faculty development programs; faculty development intersections with learning analytics; engagement with student success)
GAMIFICATION OF LEARNING (Gamification designs for course activities; adaptive approaches to gamification; alternate reality games; simulations; technological implementation options for faculty)
INSTRUCTIONAL DESIGN (Skills and competencies for designers; integration of technology into the profession; role of data in design; evolution of the design profession (here previous blog postings on this issue: http://blog.stcloudstate.edu/ims/2017/10/04/instructional-design-3/); effective leadership and collaboration with faculty)
INTEGRATED PLANNING AND ADVISING FOR STUDENT SUCCESS (Change management and campus leadership; collaboration across units; integration of technology systems and data; dashboard design; data visualization (here previous blog postings on this issue: http://blog.stcloudstate.edu/ims?s=data+visualization); counseling and coaching advising transformation; student success analytics)
LEARNING ANALYTICS (Leveraging open data standards; privacy and ethics; both faculty and student facing reports; implementing; learning analytics to transform other services; course design implications)
LEARNING SPACE DESIGNS (Makerspaces; funding; faculty development; learning designs across disciplines; supporting integrated campus planning; ROI; accessibility/UDL; rating of classroom designs)
MICRO-CREDENTIALING AND DIGITAL BADGING (Design of badging hierarchies; stackable credentials; certificates; role of open standards; ways to publish digital badges; approaches to meta-data; implications for the transcript; Personalized learning transcripts and blockchain technology (here previous blog postings on this issue: http://blog.stcloudstate.edu/ims?s=blockchain)
MOBILE LEARNING (Curricular use of mobile devices (here previous blog postings on this issue:
MULTI-DIMENSIONAL TECHNOLOGIES (Virtual, augmented, mixed, and immersive reality; video walls; integration with learning spaces; scalability, affordability, and accessibility; use of mobile devices; multi-dimensional printing and artifact creation)
NEXT-GENERATION DIGITAL LEARNING ENVIRONMENTS AND LMS SERVICES (Open standards; learning environments architectures (here previous blog postings on this issue: http://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/; social learning environments; customization and personalization; OER integration; intersections with learning modalities such as adaptive, online, etc.; LMS evaluation, integration and support)
ONLINE AND BLENDED TEACHING AND LEARNING (Flipped course models; leveraging MOOCs in online learning; course development models; intersections with analytics; humanization of online courses; student engagement)
OPEN EDUCATION (Resources, textbooks, content; quality and editorial issues; faculty development; intersections with student success/access; analytics; licensing; affordability; business models; accessibility and sustainability)
PRIVACY AND SECURITY (Formulation of policies on privacy and data protection; increased sharing of data via open standards for internal and external purposes; increased use of cloud-based and third party options; education of faculty, students, and administrators)
WORKING WITH EMERGING LEARNING TECHNOLOGY (Scalability and diffusion; effective piloting practices; investments; faculty development; funding; evaluation methods and rubrics; interoperability; data-driven decision-making)
learning and teaching in this IMS blog
ACRL e-Learning webcast series: Learning Analytics – Strategies for Optimizing Student Data on Your Campus
Webcast One: Learning Analytics and the Academic Library: The State of the Art and the Art of Connecting the Library with Campus Initiatives
March 29, 2016
Webcast Two: Privacy and the Online Classroom: Learning Analytics, Ethical Considerations, and Responsible Practice
April 13, 2016
Webcast Three: Moving Beyond Counts and Check Marks: Bringing the Library into Campus-Wide Learning Analytics Programs
May 11, 2016
If Facebook can tweak our emotions and make us vote, what else can it do?
Google’s chief executive has expressed concern that we don’t trust big companies with our data – but may be dismayed at Facebook’s latest venture into manipulation
Please consider the information on Power, Privacy, and the Internet and details on ethics and big data in this IMS blog entry:http://blog.stcloudstate.edu/ims/2014/07/01/privacy-and-surveillance-obama-advisor-john-podesta-every-country-has-a-history-of-going-over-the-line/
Please consider the SCSU Research Ethics and the IRB (Institutional Review Board) document:
For more information, please contact the SCSU Institutional Review Board : http://www.stcloudstate.edu/irb/default.asp
The Facebook Conundrum: Where Ethics and Science Collide
The field of learning analytics isn’t just about advancing the understanding of learning. It’s also being applied in efforts to try to influence and predict student behavior.
Learning analytics has yet to demonstrate its big beneficial breakthrough, its “penicillin,” in the words of Reich. Nor has there been a big ethical failure to creep lots of people out.
“There’s a difference,” Pistilli says, “between what we can do and what we should do.”