Webinar: Learning Informatics Lab, University of Minnesota
Replay the talk: https://youtu.be/dcJZeDIMr2I
Learning Informatics
AI • Analytics • Accountability • Agency
Simon Buckingham Shum
Professor of Learning Informatics
Director, Connected Intelligence Centre
University of Technology Sydney
Abstract:
“Health Informatics”. “Urban Informatics”. “Social Informatics”. Informatics offers systemic ways of analyzing and designing the interaction of natural and artificial information processing systems. In the context of education, I will describe some Learning Informatics lenses and practices which we have developed for co-designing analytics and AI with educators and students. We have a particular focus on closing the feedback loop to equip learners with competencies to navigate a complex, uncertain future, such as critical thinking, professional reflection and teamwork. En route, we will touch on how we build educators’ trust in novel tools, our design philosophy of “embracing imperfection” in machine intelligence, and the ways that these infrastructures embody values. Speaking from the perspective of leading an institutional innovation centre in learning analytics, I hope that our experiences spark productive reflection around as the UMN Learning Informatics Lab builds its program.
Biography:
Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, where he serves as inaugural director of the Connected Intelligence Centre. CIC is a transdisciplinary innovation centre, using analytics to provide new insights for university teams, with particular expertise in educational data science. Simon’s career-long fascination with software’s ability to make thinking visible has seen him active in communities including Computer-Supported Cooperative Work, Hypertext, Design Rationale, Scholarly Publishing, Semantic Web, Computational Argumentation, Educational Technology and Learning Analytics. The challenge of visualizing contested knowledge has produced several books: Visualizing Argumentation, Knowledge Cartography, and Constructing Knowledge Art. He has been active over the last decade in shaping the field of Learning Analytics, co-founding the Society for Learning Analytics Research, and catalyzing several strands: Social Learning Analytics, Discourse Analytics, Dispositional Analytics and Writing Analytics. http://Simon.BuckinghamShum.net
Abstract: The emerging configuration of educational institutions, technologies, scientific practices, ethics policies and companies can be usefully framed as the emergence of a new “knowledge infrastructure” (Paul Edwards). The idea that we may be transitioning into significantly new ways of knowing – about learning and learners, teaching and teachers – is both exciting and daunting, because new knowledge infrastructures redefine roles and redistribute power, raising many important questions. What should we see when open the black box powering analytics? How do we empower all stakeholders to engage in the design process? Since digital infrastructure fades quickly into the background, how can researchers, educators and learners engage with it mindfully? This isn’t just interesting to ponder academically: your school or university will be buying products that are being designed now. Or perhaps educational institutions should take control, building and sharing their own open source tools? How are universities accelerating the transition from analytics innovation to infrastructure? Speaking from the perspective of leading an institutional innovation centre in learning analytics, I hope that our experiences designing code, competencies and culture for learning analytics sheds helpful light on these questions.
Keynote Address, International Conference of the Learning Sciences, London Festival of Learning
Transitioning Education’s Knowledge Infrastructure:
Shaping Design or Shouting from the Touchline?
Abstract: Bit by bit, a data-intensive substrate for education is being designed, plumbed in and switched on, powered by digital data from an expanding sensor array, data science and artificial intelligence. The configurations of educational institutions, technologies, scientific practices, ethics policies and companies can be usefully framed as the emergence of a new “knowledge infrastructure” (Paul Edwards).
The idea that we may be transitioning into significantly new ways of knowing – about learning and learners – is both exciting and daunting, because new knowledge infrastructures redefine roles and redistribute power, raising many important questions. For instance, assuming that we want to shape this infrastructure, how do we engage with the teams designing the platforms our schools and universities may be using next year? Who owns the data and algorithms, and in what senses can an analytics/AI-powered learning system be ‘accountable’? How do we empower all stakeholders to engage in the design process? Since digital infrastructure fades quickly into the background, how can researchers, educators and learners engage with it mindfully? If we want to work in “Pasteur’s Quadrant” (Donald Stokes), we must go beyond learning analytics that answer research questions, to deliver valued services to frontline educational users: but how are universities accelerating the analytics innovation to infrastructure transition?
Wrestling with these questions, the learning analytics community has evolved since its first international conference in 2011, at the intersection of learning and data science, and an explicit concern with those human factors, at many scales, that make or break the design and adoption of new educational tools. We are forging open source platforms, links with commercial providers, and collaborations with the diverse disciplines that feed into educational data science. In the context of ICLS, our dialogue with the learning sciences must continue to deepen to ensure that together we influence this knowledge infrastructure to advance the interests of all stakeholders, including learners, educators, researchers and leaders.
Speaking from the perspective of leading an institutional analytics innovation centre, I hope that our experiences designing code, competencies and culture for learning analytics sheds helpful light on these questions.
UCL joint Institute of Education (London Knowledge Lab) & UCL Interaction Centre seminar, 20th April 2016. Replay: https://youtu.be/0t0IWvcO-Uo
Algorithmic Accountability & Learning Analytics
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
ABSTRACT. As algorithms pervade societal life, they are moving from the preserve of computer science to becoming the object of far wider academic and media attention. Many are now asking how the behaviour of algorithms can be made “accountable”. But why are they “opaque” and to whom? As this vital discussion unfolds in relation to Big Data in general, the Learning Analytics community must articulate what would count as meaningful questions and satisfactory answers in educational contexts. In this talk, I propose different lenses that we can bring to bear on a given learning analytics tool, to ask what it would mean for it to be accountable, and to whom. From a Human-Centred Informatics perspective, it turns out that algorithmic accountability may be the wrong focus.
BIO. Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 to direct the new Connected Intelligence Centre. Prior to that he was at The Open University’s Knowledge Media Institute 1995-2014. He brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI (PhD, York) where he worked with Rank Xerox Cambridge EuroPARC on Design Rationale. He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and with Al Selvin wrote Constructing Knowledge Art (2015). He is active in the emerging field of Learning Analytics and is a co-founder of the Society for Learning Analytics Research, Compendium Institute and Learning Emergence network.
How smart are smart classrooms? Evaluating International Evidence@cristobalcobo
There has been a considerable progress in integrating technological innovations to facilitate the learning process. This has a potentially important implications on student’s learning process as well as the role of teachers. SMART Classroom is a machine-assisted educational platform developed in Korea that allows learners to study at their own pace while teachers play a role as advisers, coaches and facilitators. Artificial intelligence allows for identification of optimal lessons based on learning algorithms and patterns of individual learning. The session will showcase an example of a framework of Korean education policies and an initiative of smart classroom, and how it has contributed to improving the learning quality and reducing the education gap in Korea.
@cristobalcobo
https://cristobalcobo.net
‘Openness’ and ‘Open Education’ in the Global Digital Economy: An Emerging Paradigm of Social Production
Introduction
2. The Emerging Open Education Paradigm
3. The History of ‘Openness’ in Education: From the Open Classroom to OCW
4. Bergson, Popper, Soros and the Open Society
The New Paradigm of Social Production
Conclusions
Abstract: The emerging configuration of educational institutions, technologies, scientific practices, ethics policies and companies can be usefully framed as the emergence of a new “knowledge infrastructure” (Paul Edwards). The idea that we may be transitioning into significantly new ways of knowing – about learning and learners, teaching and teachers – is both exciting and daunting, because new knowledge infrastructures redefine roles and redistribute power, raising many important questions. What should we see when open the black box powering analytics? How do we empower all stakeholders to engage in the design process? Since digital infrastructure fades quickly into the background, how can researchers, educators and learners engage with it mindfully? This isn’t just interesting to ponder academically: your school or university will be buying products that are being designed now. Or perhaps educational institutions should take control, building and sharing their own open source tools? How are universities accelerating the transition from analytics innovation to infrastructure? Speaking from the perspective of leading an institutional innovation centre in learning analytics, I hope that our experiences designing code, competencies and culture for learning analytics sheds helpful light on these questions.
Keynote Address, International Conference of the Learning Sciences, London Festival of Learning
Transitioning Education’s Knowledge Infrastructure:
Shaping Design or Shouting from the Touchline?
Abstract: Bit by bit, a data-intensive substrate for education is being designed, plumbed in and switched on, powered by digital data from an expanding sensor array, data science and artificial intelligence. The configurations of educational institutions, technologies, scientific practices, ethics policies and companies can be usefully framed as the emergence of a new “knowledge infrastructure” (Paul Edwards).
The idea that we may be transitioning into significantly new ways of knowing – about learning and learners – is both exciting and daunting, because new knowledge infrastructures redefine roles and redistribute power, raising many important questions. For instance, assuming that we want to shape this infrastructure, how do we engage with the teams designing the platforms our schools and universities may be using next year? Who owns the data and algorithms, and in what senses can an analytics/AI-powered learning system be ‘accountable’? How do we empower all stakeholders to engage in the design process? Since digital infrastructure fades quickly into the background, how can researchers, educators and learners engage with it mindfully? If we want to work in “Pasteur’s Quadrant” (Donald Stokes), we must go beyond learning analytics that answer research questions, to deliver valued services to frontline educational users: but how are universities accelerating the analytics innovation to infrastructure transition?
Wrestling with these questions, the learning analytics community has evolved since its first international conference in 2011, at the intersection of learning and data science, and an explicit concern with those human factors, at many scales, that make or break the design and adoption of new educational tools. We are forging open source platforms, links with commercial providers, and collaborations with the diverse disciplines that feed into educational data science. In the context of ICLS, our dialogue with the learning sciences must continue to deepen to ensure that together we influence this knowledge infrastructure to advance the interests of all stakeholders, including learners, educators, researchers and leaders.
Speaking from the perspective of leading an institutional analytics innovation centre, I hope that our experiences designing code, competencies and culture for learning analytics sheds helpful light on these questions.
UCL joint Institute of Education (London Knowledge Lab) & UCL Interaction Centre seminar, 20th April 2016. Replay: https://youtu.be/0t0IWvcO-Uo
Algorithmic Accountability & Learning Analytics
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
ABSTRACT. As algorithms pervade societal life, they are moving from the preserve of computer science to becoming the object of far wider academic and media attention. Many are now asking how the behaviour of algorithms can be made “accountable”. But why are they “opaque” and to whom? As this vital discussion unfolds in relation to Big Data in general, the Learning Analytics community must articulate what would count as meaningful questions and satisfactory answers in educational contexts. In this talk, I propose different lenses that we can bring to bear on a given learning analytics tool, to ask what it would mean for it to be accountable, and to whom. From a Human-Centred Informatics perspective, it turns out that algorithmic accountability may be the wrong focus.
BIO. Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 to direct the new Connected Intelligence Centre. Prior to that he was at The Open University’s Knowledge Media Institute 1995-2014. He brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI (PhD, York) where he worked with Rank Xerox Cambridge EuroPARC on Design Rationale. He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and with Al Selvin wrote Constructing Knowledge Art (2015). He is active in the emerging field of Learning Analytics and is a co-founder of the Society for Learning Analytics Research, Compendium Institute and Learning Emergence network.
How smart are smart classrooms? Evaluating International Evidence@cristobalcobo
There has been a considerable progress in integrating technological innovations to facilitate the learning process. This has a potentially important implications on student’s learning process as well as the role of teachers. SMART Classroom is a machine-assisted educational platform developed in Korea that allows learners to study at their own pace while teachers play a role as advisers, coaches and facilitators. Artificial intelligence allows for identification of optimal lessons based on learning algorithms and patterns of individual learning. The session will showcase an example of a framework of Korean education policies and an initiative of smart classroom, and how it has contributed to improving the learning quality and reducing the education gap in Korea.
@cristobalcobo
https://cristobalcobo.net
‘Openness’ and ‘Open Education’ in the Global Digital Economy: An Emerging Paradigm of Social Production
Introduction
2. The Emerging Open Education Paradigm
3. The History of ‘Openness’ in Education: From the Open Classroom to OCW
4. Bergson, Popper, Soros and the Open Society
The New Paradigm of Social Production
Conclusions
This presentation examines two articles related to topics on assistive technology and ethics, “Teaching Assistive Technology through Wikis and Embedded Video” by Oliver Dreon Jr. and Nanette I. Dietrich, and “When Dealing with Human Subjects: Balancing Ethical and Pratical Matters in the Field” by Michael A Evans and Liesl M. Combs. Topics covered in this presentation include defining/history of assistive technology, wikis & video, YouTube, and ethical issues surrounding assistive technologies.
Building large-scale evidence for education (the case of Plan Ceibal, Uruguay)@cristobalcobo
Keynote “Innovations and initiatives”. Education World Forum 2018.The Department for Education (DfE) and the British Council, London
At the Education World Forum #London #EWF18 #EFF19
@cristobalcobo
@fundacionceibal
This is the large version. A very cut down version was presented at my Inaugural Lecture on 5 March 2014, Bristol, UK which is now on YouTube: make some coffee and take a peek? https://www.youtube.com/watch?v=HWnyfqOxR6E
Web Observatories, e-Research and the Importance of Collaboration. WST 2014 Webinar series, 20th March 2014
See Web Science Trust http://webscience.org/
The aim of this project is to provide a contextualised, social and historical account of urban education, focusing on systems and beliefs that contribute to the construction of the surrounding discourses.
Another aim of this project is to scaffold the trainee teachers’ understanding of what is possible with mobile learning in terms of filed trips.
Can social media and mobile devices be used to design transformative, augmented contexts for learning?#somobnet #lmlg(1 of 6 guiding principles http://slidesha.re/GYYP7X). One Day Seminar at CLTT
University of British Columbia – Vancouver (CA) – April 16, 2012
Literacy session: Hindsight, Insight and Foresight John Cook. Workshop 'Technology-enhanced learning in the context of technological, societal and cultural transformations' Alpine Rendez-Vous, within the framework of the STELLAR Network of Excellence. December 3-4, Garmisch-Partenkirchen, Bavaria, Germany. #telc09 #stellar2009,
I am NOT the author of this book. The author is Dr. George Siemens and it has a Creative Commons License. You can download it for reference. Thank you.
This presentation examines two articles related to topics on assistive technology and ethics, “Teaching Assistive Technology through Wikis and Embedded Video” by Oliver Dreon Jr. and Nanette I. Dietrich, and “When Dealing with Human Subjects: Balancing Ethical and Pratical Matters in the Field” by Michael A Evans and Liesl M. Combs. Topics covered in this presentation include defining/history of assistive technology, wikis & video, YouTube, and ethical issues surrounding assistive technologies.
Building large-scale evidence for education (the case of Plan Ceibal, Uruguay)@cristobalcobo
Keynote “Innovations and initiatives”. Education World Forum 2018.The Department for Education (DfE) and the British Council, London
At the Education World Forum #London #EWF18 #EFF19
@cristobalcobo
@fundacionceibal
This is the large version. A very cut down version was presented at my Inaugural Lecture on 5 March 2014, Bristol, UK which is now on YouTube: make some coffee and take a peek? https://www.youtube.com/watch?v=HWnyfqOxR6E
Web Observatories, e-Research and the Importance of Collaboration. WST 2014 Webinar series, 20th March 2014
See Web Science Trust http://webscience.org/
The aim of this project is to provide a contextualised, social and historical account of urban education, focusing on systems and beliefs that contribute to the construction of the surrounding discourses.
Another aim of this project is to scaffold the trainee teachers’ understanding of what is possible with mobile learning in terms of filed trips.
Can social media and mobile devices be used to design transformative, augmented contexts for learning?#somobnet #lmlg(1 of 6 guiding principles http://slidesha.re/GYYP7X). One Day Seminar at CLTT
University of British Columbia – Vancouver (CA) – April 16, 2012
Literacy session: Hindsight, Insight and Foresight John Cook. Workshop 'Technology-enhanced learning in the context of technological, societal and cultural transformations' Alpine Rendez-Vous, within the framework of the STELLAR Network of Excellence. December 3-4, Garmisch-Partenkirchen, Bavaria, Germany. #telc09 #stellar2009,
I am NOT the author of this book. The author is Dr. George Siemens and it has a Creative Commons License. You can download it for reference. Thank you.
learning in a networked world: the role of social media and augmented learning.
Keynote presentation to the New Educator Program Hedley Beare Centre for Teaching and Learning 23-25 August 2011
ABSTRACT : Computational social science (CSS) is an academic discipline that combines the traditional social sciences with computer science. While social scientists provide research questions, data sources, and acquisition methods, computer scientists contribute mathematical models and computational tools. CSS uses computationally methods and statistical tools to analyze and model social phenomena, social structures, and human social behavior. The purpose of this paper is to provide a brief introduction to computational social science.
Key Words: computational social science, social-computational systems, social simulation models, agent-based models
Keynote presentation of Yannis Dimitriadis at Intelligent Tutoring Systems 2022: Human-Centered Learning Analytics: Designing for balanced human and computational agency
Innovating Pedagogy 2020. Innovation Report 8
Exploring new forms of teaching, learning and assessment, to
guide educators and policy makers. Institute of Educational Technology, The Open University
International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Keynote Address, Expanding Horizons 2012, Macquarie University
http://staff.mq.edu.au/teaching/workshops_programs/expanding_horizons
"Learning Analytics": unprecedented data sets and live data streams about learners, with computational power to help make sense of it all, and new breeds of staff who can talk predictive models, pedagogy and ethics. This means rather different things to different people: unprecedented opportunity to study, benchmark and improve educational practice, at scales from countries and institutions, to departments, individual teachers and learners. "Benchmarking" may trigger dystopic visions of dumbed down proxies for 'real teaching and learning', but an emu response is no good. For educational institutions, our calling is to raise the quality of debate, shape external and internal policy, and engage with the companies and open communities developing the future infrastructure. How we deploy these new tools rests critically on assessment regimes, what can be logged and measured with integrity, and what we think it means to deliver education that equips citizens for a complex, uncertain world.
The Generative AI System Shock, and some thoughts on Collective Intelligence ...Simon Buckingham Shum
Keynote Address: Team-based Learning Collaborative Asia Pacific Community (TBLC-APC) Symposium (“Impact of emerging technologies on learning strategies”) 8-9 February 2024, Sydney https://tbl.sydney.edu.au
Slides from my contribution to the panel convened by Jeremy Roschelle at the International Society for the Learning Sciences: Engaging Learning Scientists in Policy Challenges: AI and the Future of Learning
Deliberative Democracy as a strategy for co-designing university ethics aro...Simon Buckingham Shum
Buckingham Shum, S. (2021). Deliberative Democracy as a strategy for co-designing university ethics around analytics and AI in education. AARE2021: Australian Association for Research in Education, 28 Nov. – 2 Dec. 2021
Deliberative Democracy as a Strategy for Co-designing University Ethics Around Analytics and AI in Education
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
Universities can see an increasing range of student and staff activity as it becomes digitally visible in their platform ecosystems. The fields of Learning Analytics and AI in Education have demonstrated the significant benefits that ethically responsible, pedagogically informed analysis of student activity data can bring, but such services are only possible because they are undeniably a form of “surveillance”, raising legitimate questions about how the use of such tools should be governed.
Our prior work has drawn on the rich concepts and methods developed in human-centred system design, and participatory/co-design, to design, deploy and validate practical tools that give a voice to non-technical stakeholders (e.g. educators; students) in shaping such systems. We are now expanding the depth and breadth of engagement that we seek, looking to the Deliberative Democracy movement for inspiration. This is a response to the crisis in confidence in how typical democratic systems engage citizens in decision making. A hallmark is the convening of a Deliberative Mini-Public (DMP) which may work at different scales (organisation; community; region; nation) and can take diverse forms (e.g. Citizens’ Juries; Citizens’ Assemblies; Consensus Conferences; Planning Cells; Deliberative Polls). DMP’s combination of stratified random sampling to ensure authentic representation, neutrally facilitated workshops, balanced expert briefings, and real support from organisational leaders, has been shown to cultivate high quality dialogue in sometimes highly conflicted settings, leading to a strong sense of ownership of the DMP's final outputs (e.g. policy recommendations).
This symposium contribution will describe how the DMP model is informing university-wide consultation on the ethical principles that should govern the use of analytics and AI around teaching and learning data.
March 2021 • 24/7 Instant Feedback on Writing: Integrating AcaWriter into yo...Simon Buckingham Shum
Slides accompanying the monthly UTS educator briefing https://cic.uts.edu.au/events/24-7-instant-feedback-on-writing-integrating-acawriter-into-your-teaching-18-march/
What difference could instant feedback on draft writing make to your students? Over the last 5 years the Connected Intelligence Centre has been developing and piloting an automated feedback tool for academic writing (AcaWriter), working closely with academics across several faculties. The research portal documents how educators and students engage with this kind of AI, and what we’ve learnt about integrating it into teaching and assessment.
In May, AcaWriter was launched to all students along with an information portal. Now we want to start upskilling academics, tutors and learning technologists, in a monthly session to give you the chance to learn about AcaWriter, and specifically, good practices for integrating it into your subject. CIC can support you, and we hope you may be interested in co-designing publishable research.
AcaWriter handles several different ‘genres’ of writing, including reflective writing (e.g. a Reflective Essay; Reflective Blogs/Journals on internships/work-placements) and analytical writing (e.g. Argumentative Essays; Research Abstracts & Introductions). This briefing will demo AcaWriter, and show it can be embedded in student activities. We hope this sparks ideas for your own teaching, which we can discuss in more detail.
ICQE20: Quantitative Ethnography Visualizations as Tools for ThinkingSimon Buckingham Shum
Slides for this keynote talk to the 2nd International Conference on Quantitative Ethnography
http://simon.buckinghamshum.net/2021/02/icqe2020-keynote-qe-viz-as-tools-for-thinking/
24/7 Instant Feedback on Writing: Integrating AcaWriter into your TeachingSimon Buckingham Shum
https://cic.uts.edu.au/events/24-7-instant-feedback-on-writing-integrating-acawriter-into-your-teaching-2-dec/
What difference could instant feedback on draft writing make to your students? Over the last 5 years the Connected Intelligence Centre has been developing and piloting an automated feedback tool for academic writing (AcaWriter), working closely with academics across several faculties. The research portal documents how educators and students engage with this kind of AI, and what we’ve learnt about integrating it into teaching and assessment.
In May, AcaWriter was launched to all students along with an information portal. Now we want to start upskilling academics, tutors and learning technologists, in a monthly session to give you the chance to learn about AcaWriter, and specifically, good practices for integrating it into your subject. CIC can support you, and we hope you may be interested in co-designing publishable research.
AcaWriter handles several different ‘genres’ of writing, including reflective writing (e.g. a Reflective Essay; Reflective Blogs/Journals on internships/work-placements) and analytical writing (e.g. Argumentative Essays; Research Abstracts & Introductions).
This briefing will demo AcaWriter, and show it can be embedded in student activities. We hope this sparks ideas for your own teaching, which we can discuss in more detail.
An introduction to argumentation for UTS:CIC PhD students (with some Learning Analytics examples, but potentially of wider interest to students/researchers)
Despite AI’s potential for beneficial use, it creates important risks for Australians. AI, big data, and AI-informed decision making can cause exclusion, discrimination, skill loss, and economic impact; and can affect privacy, security of critical infrastructure and social well-being. What types of technology raise particular human rights concerns? Which human rights are particularly implicated?
Towards Collaboration Translucence: Giving Meaning to Multimodal Group DataSimon Buckingham Shum
Vanessa Echeverria, Roberto Martinez-Maldonado, and Simon Buck- ingham Shum.. 2019. Towards Collaboration Translucence: Giving Meaning to Multimodal Group Data. In Proceedings of ACM CHI conference (CHI’19). ACM, New York, NY, USA, Paper 39, 16 pages. https://doi.org/10.1145/3290605.3300269
Collocated, face-to-face teamwork remains a pervasive mode of working, which is hard to replicate online. Team members’ embodied, multimodal interaction with each other and artefacts has been studied by researchers, but due to its complexity, has remained opaque to automated analysis. However, the ready availability of sensors makes it increasingly affordable to instrument work spaces to study teamwork and groupwork. The possibility of visualising key aspects of a collaboration has huge potential for both academic and professional learning, but a frontline challenge is the enrichment of quantitative data streams with the qualitative insights needed to make sense of them. In response, we introduce the concept of collaboration translucence, an approach to make visible selected features of group activity. This is grounded both theoretically (in the physical, epistemic, social and affective dimensions of group activity), and contextually (using domain-specific concepts). We illustrate the approach from the automated analysis of healthcare simulations to train nurses, generating four visual proxies that fuse multimodal data into higher order patterns.
Panel held at LAK13: 3rd International Conference on Learning Analytics & Knowledge
http://simon.buckinghamshum.net/2013/03/lak13-edu-data-scientists-scarce-breed
Educational Data Scientists: A Scarce Breed
The Educational Data Scientist is currently a poorly understood, rarely sighted breed. Reports vary: some are known to be largely nocturnal, solitary creatures, while others have been reported to display highly social behaviour in broad daylight. What are their primary habits? How do they see the world? What ecological niches do they occupy now, and will predicted seismic shifts transform the landscape in their favour? What survival skills do they need when running into other breeds? Will their numbers grow, and how might they evolve? In this panel, the conference will hear and debate not only broad perspectives on the terrain, but will have been exposed to some real life specimens, and caught glimpses of the future ecosystem.
Kirsty Kitto, Simon Buckingham Shum, and Andrew Gibson. (2018). Embracing Imperfection in Learning Analytics. In Proceedings of LAK18: International Conference on Learning Analytics and Knowledge, March 5–9, 2018, Sydney, NSW, Australia, pp.451-460. (ACM, New York, NY, USA). https://doi.org/10.1145/3170358.3170413
Open Access: http://simon.buckinghamshum.net/2018/01/embracing-imperfection-in-learning-analytics
Abstract: Learning Analytics (LA) sits at the confluence of many contributing disciplines, which brings the risk of hidden assumptions inherited from those fields. Here, we consider a hidden assumption derived from computer science, namely, that improving computational accuracy in classification is always a worthy goal. We demonstrate that this assumption is unlikely to hold in some important educational contexts, and argue that embracing computational “imperfection” can improve outcomes for those scenarios. Specifically, we show that learner-facing approaches aimed at “learning how to learn” require more holistic validation strategies. We consider what information must be provided in order to reasonably evaluate algorithmic tools in LA, to facilitate transparency and realistic performance comparisons.
Opening to the inaugural workshop on Learning Analytics in Schools held at LAK18: International Conference on Learning Analytics & Knowledge, Sydney. http://lak18.solaresearch.org
Prof. Simon Buckingham Shum
Prof. Ruth Deakin Crick
Summer@UTS Workshop, 8th Feb. 2018
Connected Intelligence Centre
https://utscic.edu.au/event/resilience-complexity
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
3. 3
informatics
We need an overarching lens for designing
effective, usable, ethical learning technology
You’ve made a wise choice! J
4. 4
Kristen Nygaard
August 27, 1926 – August 10, 2002
Internationally acknowledged as the co-inventor
with Ole-Johan Dahl of object-oriented programming
and the programming language Simula.
He also was a pioneer of participatory design and the
“Scandinavian school of systems development”.
http://kristennygaard.no
5. 6
“Informatics is the science that has as its
domain information processes and related
phenomena in artifacts, society and nature”.
Nygaard, K. (1986): Program Development as a Social Activity, Invited Lecture, Proceedings of the IFIP 10th World Computer Congress,
INFORMATION PROCESSING 86, Dublin. Elsevier Science Publishers, pp. 189-198. Available at: https://ojs.ruc.dk/index.php/pdc/article/view/97/89
6. 7
No management hand-waving about valuing
“user participation”
Let’s get specific: who are we talking about?
Nygaard, K. (1986): Program Development as a Social Activity, Invited Lecture, Proceedings of the IFIP 10th World Computer Congress,
INFORMATION PROCESSING 86, Dublin. Elsevier Science Publishers, pp. 189-198. Available at: https://ojs.ruc.dk/index.php/pdc/article/view/97/89
7. 8
How will such users get to “participate”?
Nygaard, K. (1986): Program Development as a Social Activity, Invited Lecture, Proceedings of the IFIP 10th World Computer Congress,
INFORMATION PROCESSING 86, Dublin. Elsevier Science Publishers, pp. 189-198. Available at: https://ojs.ruc.dk/index.php/pdc/article/view/97/89
8. “Informatics is a distinct scientific discipline,
characterised by its own concepts, methods,
body of knowledge and open issues.
It covers the foundations of computational
structures, processes, artefacts and
systems; and their software designs, their
applications, and their impact on society”
https://www.informatics-europe.org
This laid the foundations for Informatics today…
9. “Informatics concerns itself with the study of living,
working and building in a digital world. Wherever
technology touches people, it must be designed with
ultimate care. This requires mastery of technological
knowhow and a deep appreciation of the social,
cultural and organizational forces at work.”
https://www.informatics.uci.edu/explore/chairs-welcome
This laid the foundations for Informatics today…
10. 12
learning
informatics
Note that the terms Education(al) Informatics have been proposed, and some definitions of this are similar, albeit
limited to educational institutions — I prefer a broader focus on lifelong/lifewide learning.
Levy, P., Ford, N., Foster, J., Madden, A., Miller, D., Nunes, M. B., et al. (2003). Educational informatics: An emerging research agenda. Journal
of Information Science, 29(4), 298-210. [reprint]
Collins, J.W., and Weiner, S.A..(2010). Proposal for the creation of a subdiscipline: Education informatics. Teachers College Record 112, no. 10:
2523–2536. [reprint]
11. 13
learning informatics
How does this lens translate into deploying Analytics/AI for learning?
Design for educator and learner agency
Design socio-technical systems
Design for imperfect computational models
LA/AIED System Integrity > algorithmic accountability
13. 15
What is Learning Analytics?
Learning
student engagement
teaching practice
curriculum design
instructional design
pedagogy
assessment
epistemology
…
Analytics
data
statistics
classification
machine learning
text processing
visualisation
predictive models
…
this is not a straightforward dialogue!
14. 16
A key circle is missing…
Human
Factors
stakeholder involvement
participatory design cycles
user interface design
privacy and ethics
end-user evaluation
organisational strategy
staff training
17. 19
So where does Artificial Intelligence fit in?
AI
now the computer
has greater agency
e.g.
“adaptive learning”: tune the task
to each learner’s current ability
give personalised feedback based
on the learner’s progress
chatbots/avatars
AI
now the computer can
sense more of the human world
e.g.
speech, gestures, posture,
physiology, facial expression…
+ mobile and sensor data:
use of physical tools,
location, other apps…
20. Framework @UTS for educators to co-design Analytics/AI
à augment teaching practice
PhD by Antonette Shibani: http://simon.buckinghamshum.net/2019/11/congratulations-dr-antonette-shibani
Shibani, A., Knight, S. and Buckingham Shum, S. (2019). Contextualizable Learning Analytics Design: A Generic Model, and Writing Analytics Evaluations. Proc. 9th International
Conference on Learning Analytics & Knowledge (LAK19). ACM Press, NY, pp. 210-219. DOI: https://doi.org/10.1145/3303772.3303785. Eprint: https://tinyurl.com/lak19clad
Student
Task
Design
Feedback
& User
Interface
Features
in the
Data
Educators
Analytics/AI
designers
Assessment
24. Raising data/learning analytics literacy
Disseminating innovations in Learning Analytics to the UTS community
Briefings • Hands-on Training • Teaching into degree programs
CIC Events archive: https://cic.uts.edu.au/events
25. CIC’s organisational positioning
VC
DVC
Research
Faculty
School
Centre
Academics
DVC
Education
CIC Student
Support
Learning
Tech
DVC
Operations
IT
BI
Analytics
LMS
Analytics
Provost
CDO
(Schematic to show key groups CIC collaborates with)
HYBRID INNOVATION/SERVICES CENTRE:
Launched 2014 after 3 years cross-university
consultation à Connected Intelligence Strategy
Reporting to DVC (Education & Students)
Analytics R&D in a non-faculty centre:
academics, PhDs, full-stack & UX developers,
data scientists, professional admin support,
interns
Academic and training programs:
§ Transdisciplinary Master of Data Science &
Innovation (2015-18)
§ PhD program in Learning Analytics (2016-)
§ Online and blended data literacy (2015-20)
Deploy IT-approved systems UTS-wide
26. CIC skillset
Board Room
VC/DVCs/Deans/Directors
Common Room
Academic staff
Server Room
IT Division
Interpersonal skills
+
Education, Learning Design, Interface
Design, Programming, Web Development,
Text Analytics, Machine Learning,
Statistics, Visualisation, Decision-Support,
Sensemaking, Creativity & Risk,
Participatory Design
27. Organizational architecture for hybrid analytics
innovation + service centres
29
A comparison of the drivers behind the creation of 2
university analytics innovation centres, and the org
structures that enable impact:
University of Technology Sydney’s
Connected Intelligence Centre
University of Michigan’s
Digital Innovation Greenhouse
EDUCAUSE Review
https://er.educause.edu/articles/2018/3/architecting-for-learning-analytics-innovating-for-sustainable-impact
28. 30
Design for educator
and learner agency
PhDs by Carlos Alvarez-Prieto and Vanessa Echeverria:
http://simon.buckinghamshum.net/2020/09/congratulations-dr-carlos-prieto-alvarez
http://simon.buckinghamshum.net/2020/05/congratulations-dr-vanessa-echeverria
Learning Informatics Principle 2
30. Who did what, when in a nursing simulation? Team Timeline for evidence-based debriefings
Patient’s state changes
Nurses 1-3
Uses a device
Administers medication
Personalised feedback on high performance teamwork
Echeverria, V., Martinez-Maldonado, R. and Buckingham Shum, S. (2019). Towards Collaboration Translucence: Giving Meaning to Multimodal Group Data. In Proceedings of ACM Conference on Human
Factors in Computing (CHI’19). ACM: NY. Paper 39, pp. 1-16. https://doi.org/10.1145/3290605.3300269 Open Access Eprint: http://bit.ly/chi19utscic
31. Co-design techniques to engage students and
staff in designing multimodal learning analytics
Prieto-Alvarez, C.G., Martinez-Maldonado, R. and Buckingham Shum, S. (2018).
Mapping Learner-Data Journeys: Evolution of a Visual Co-design Tool. Proceedings
of the 30th Australian Conference on Computer-Human Interaction (OzCHI’18),
Melbourne, Australia, Dec. 2018, ACM, New York, NY, USA, pp. 205–214.
DOI: https://doi.org/10.1145/3292147.3292168
32. MODELLING THE SEMANTICS OF LOCATION
Extensive consultation with educators informed the modelling of 5
key “zones of interest” to help interpret positional data
Echeverria, V., Martinez-Maldonado, R. and
Buckingham Shum, S. (2019). Towards
Collaboration Translucence: Giving
Meaning to Multimodal Group Data.
In Proceedings CHI 2019. Paper 39, 1-16.
(May 4-9, 2019, Glasgow, UK)
https://doi.org/10.1145/3290605.3300269
33. Making multimodal streams meaningful for
nursing student team feedback
From multimodal logs to curriculum outcomes
Embodied strategies
Actions and procedures
Communication with patient
Changes in emotional arousal
# and length of utterances by the patient
# and length of utterances by nurses
Presence in meaningful zones
Wrist acceleration intensity
Actions registered by the manikin
Electrodermal activity peaks
Critical procedures
Distance to the patient and the trolley
Interactions with objects
Teamwork communication
Proximity to patient/objects
Intensity of physical activity
Physical
Social
Epistemic
Affective
Dimensions of
collaboration
Multimodal
observations
Higher-order
constructs
Patient-centred
care
&
Teamwork
1
2
Curriculum
outcomes
CHI’19: https://doi.org/10.1145/3290605.3300269
34. Making multimodal streams meaningful for
nursing student team feedback
From multimodal logs to curriculum outcomes
Embodied strategies
Actions and procedures
Communication with patient
Changes in emotional arousal
# and length of utterances by the patient
# and length of utterances by nurses
Presence in meaningful zones
Wrist acceleration intensity
Actions registered by the manikin
Electrodermal activity peaks
Critical procedures
Distance to the patient and the trolley
Interactions with objects
Teamwork communication
Proximity to patient/objects
Intensity of physical activity
Physical
Social
Epistemic
Affective
Dimensions of
collaboration
Multimodal
observations
Higher-order
constructs
Patient-centred
care
&
Teamwork
1
2
Constructs for collaborative activity
(from ACAD Framework)
Curriculum
outcomes
CHI’19: https://doi.org/10.1145/3290605.3300269
35. Making multimodal streams meaningful for
nursing student team feedback
From multimodal logs to curriculum outcomes
Embodied strategies
Actions and procedures
Communication with patient
Changes in emotional arousal
# and length of utterances by the patient
# and length of utterances by nurses
Presence in meaningful zones
Wrist acceleration intensity
Actions registered by the manikin
Electrodermal activity peaks
Critical procedures
Distance to the patient and the trolley
Interactions with objects
Teamwork communication
Proximity to patient/objects
Intensity of physical activity
Physical
Social
Epistemic
Affective
Dimensions of
collaboration
Multimodal
observations
Higher-order
constructs
Patient-centred
care
&
Teamwork
1
2
Constructs for collaborative activity
(from ACAD Framework)
Curriculum
outcomes
Multimodal data sources
CHI’19: https://doi.org/10.1145/3290605.3300269
36. 38
Toolkit available http://ladeck.utscic.edu.au
LA-DECK: card-based co-design tool for LA
Prieto-Alvarez, C.G., Martinez-Maldonado, R. and Buckingham Shum, S. (2020). LA-DECK: A Card-Based Learning Analytics Co-Design Tool. Proc.10th
International Conference on Learning Analytics and Knowledge, Frankfurt, March 2020, ACM. 10 pages. DOI: https://doi.org/10.1145/3375462.3375476
37. 39
LA-DECK: card-based co-design tool for LA
Prieto-Alvarez, C.G., Martinez-Maldonado, R. and Buckingham Shum, S. (2020). LA-DECK: A Card-Based Learning Analytics Co-Design Tool. Proc.10th
International Conference on Learning Analytics and Knowledge, Frankfurt, March 2020, ACM. 10 pages. DOI: https://doi.org/10.1145/3375462.3375476
38. Automated
formative
feedback on
reflective writing
Knight, S., Shibani, A., Abel, S., Gibson, A., Ryan,
P., Sutton, N., Wight, R., Lucas, C., Sándor, Á.,
Kitto, K., Liu, M., Mogarkar, R. & Buckingham
Shum, S. (2020). AcaWriter: A learning analytics
tool for formative feedback on academic writing.
Journal of Writing Research, 12, (1), 141-186.
https://doi.org/10.17239/jowr-2020.12.01.06
39. Participatory prototyping with educators
to build trust in the NLP
http://heta.io/how-can-writing-analytics-researchers-rapidly-codesign-feedback-with-educators
Learning Analytics researchers work with
academics (3 hour workshop)
Goal: calibrate the parser detecting affect in
reflective writing, working through sample texts
Rapid prototyping with a Python notebook, then
integrated into full application for further testing
40. Design for imperfect
computational models
Kirsty Kitto, Simon Buckingham Shum, and Andrew Gibson. (2018). Embracing Imperfection in Learning Analytics. In Proceedings LAK18: International Conference on
Learning Analytics and Knowledge, March 5–9, 2018, Sydney, NSW, Australia, pp.451-460. (ACM, New York, NY, USA). https://doi.org/10.1145/3170358.3170413
Learning Informatics Principle 3
41. We must equip graduates with the
distinctive qualities that will keep
them in jobs that won’t be
automated…
…but if we want to use LA/AIED in
such teaching and learning,
tracking/assessing such
competencies will be imperfect
42. The Navajo rug
“In a Navajo rug there is always an imperfection
woven into the corner. And interestingly
enough, it’s where “the Spirit moves in and out
of the rug.” The pattern is perfect and then
there’s one part of it that clearly looks like a
mistake . . .
Perfection is not the elimination of imperfection.
[…] Perfection, rather, is the ability to
incorporate imperfection!
[…] You either incorporate imperfection,
or you fall into denial.”
http://exhibitions.kelsey.lsa.umich.edu/less-than-perfect/navajo.php
Richard Rohr (2011). Breathing Under Water: Spirituality and the Twelve Steps.
Cincinnati, OH: Franciscan Media
See also: https://medium.com/bedolabs/success-through-imperfection-c3ef21cb32ed
43. Cultivating the disposition and capacity to
“learn how to learn”
Reflection and metacognition are among
the highest order outcomes that we aspire
to cultivate in learners — the desire and
skills to observe one’s thoughts, emotions
and actions, and glean insights
Reflection on machine intelligence is part of
this: critical engagement with AI is now a
lifelong learning competency
Tracking such competencies is currently at
the edge of A.I.
Learning Analytics in such
contexts will in principle have a
high degree of
imperfection
44. Authentic learning à C21/LLL competencies
Embodied, skilled performance:
an important part of the learning experience is
physically embodied (e.g. inspecting a forest; a
social services risk assessment) it’s impossible
to tightly control what will happen, as well as
making outcomes far harder to digitally
monitor.
Wicked problems (Horst Rittel):
problems with no correct answer, and no
stopping rules — even the definition of the
problem is contested
Learning Analytics in such
contexts will in principle have a
high degree of
imperfection
45. Authentic learning à C21/LLL competencies
Socially and psychologically complex
performance: scenarios where the outcome
is emergent in nature, a function of many
drivers that result in unpredictable and/or
unique outcomes (e.g. a social worker client
interview; conflict resolution)
Deep reflection: the sense that a learner
makes of their experience, or a shift in
worldview, which by definition is not accessible
to the machine, but to which a machine might
have partial access
Learning Analytics in such
contexts will in principle have a
high degree of
imperfection
Can we design for this?
47. Imperfect Learning Analytics à cognitive dissonance
49
Kirsty Kitto, Simon Buckingham Shum, and Andrew Gibson. (2018). Embracing Imperfection in Learning Analytics. In Proceedings LAK18: International Conference on
Learning Analytics and Knowledge, March 5–9, 2018, Sydney, NSW, Australia, pp.451-460. (ACM, New York, NY, USA). https://doi.org/10.1145/3170358.3170413
“…as D’Mello and Graesser [15] demonstrate, it is when the
student experiences dissonance because the
analytics fail to match their expectations that
they are likely to reflect on why they think the
machine is wrong. We believe that this form of critical
questioning is more likely to happen if the student has been given
an underlying reason to be a little distrustful of the classifier.”
48. 50
Gavriel Salomon, David Perkins and Tamar Globerson (1991).
Partners in cognition: extending human intelligence with
intelligent technologies. Educational Researcher 20, 3 (1991), 2–9.
Learners’ engagement with intelligent
technology should not be mindless.
On the contrary we should design for…
“nonautomatic, effortful and
thus metacognitively guided
processes”
49. “Embracing Imperfection in Learning Analytics”
51
1. Robust learning design
ensures that the activity
involving automated feedback
is meaningful whether or not
the technology always works
2. Explicit encouragement
— in student briefings, and in
the user interface — to push
back if they disagree with
the feedback
50. Cultivating “automated feedback literacy”
“Feedback literacy” (Boud)
feedback ≠ transmitting information:
systemic implications for learning
design and assessment
As analytics/AI enable increasing
amounts of automated feedback,
learners must now develop a new
literacy: knowing how much to trust the
machine’s judgement, and being able to
push back when they disagree
http://bit.ly.daffi2020
53. Growing public literacy around algorithmic bias and
the need for accountability is to be welcomed
55
…but is there anything distinctive about algorithms for teaching
and learning that shapes how we frame “accountability”?
54. Stakeholders and key transitions
in designing a Learning Analytics system
Ethical Principles
Educational/Learning
Sciences researcher
Learning Theory
Algorithm
Learning Analytics
Researcher
Educator
Learner
Learning Outcomes
Educational Insights
Programmer
Software, Hardware
User Interface
Data
55. So the algorithm is just one ingredient. A better name might be
LA/AIED System Integrity
Remind me, why should I trust this system?
Because… <argument>
61. 63ICLS 2018 keynote: http://simon.buckinghamshum.net/2018/06/icls2018-keynote
Elsewhere I have argued that this constitutes
a transition in education’s knowledge infrastructure
and discuss different theory à analytics mappings
64. 66
learning informatics
How does this lens translate into deploying Analytics/AI for learning?
Design for educator and learner agency
Design socio-technical systems
Design for imperfect computational models
LA/AIED System Integrity > algorithmic accountability
65. Balancing and
aligning the
elements
User Experience
Learning Design
Organisational
Strategy
PhotobySeanStrattononUnsplashhttps://unsplash.com/photos/ObpCE_X3j6U
T
R
U
S
T
Stakeholder Agency