Catch up on our previous computing education research seminars
All our online research seminars are available below to watch and share after they take place. You can also download the slides that were presented using the links below.
Cross-disciplinary computing series (May 2022 – Nov 2022)
|3 May 2022||Adding a teaspoon of computing to history and mathematics classes||Mark Guzdial (University of Michigan)|
Adding a teaspoon of computing to history and mathematics classes (3 May 2022)
Mark Guzdial (University of Michigan)
Participation in computer science classes is disappointing if our goal is “CS for All”. In both the US and the UK, evidence suggests that computer science classes are under-subscribed. We might now suspect that the “All” are unlikely to ever take a “CS” class. If we want more students to experience and learn about CS, we may have to take the “CS” to where the “All” are. Task-specific programming (TSP) languages are designed to be highly usable, rapidly learned (less than 10 minutes, typically), and matched specifically to learning activities that non-CS teachers want in their classrooms. These are “Teaspoon languages” (playing off the TSP abbreviation), because they add a teaspoon of computing into other subjects. We have developed prototype Teaspoon languages now for social studies, language arts, and mathematics classes. The strategy is complementary to approaches to grow CS recruitment and enrolment. Can we develop student awareness, appreciation, and self-efficacy in CS in other classes? Our design approach is novel for involving non-CS teachers in participatory design of new languages with a high degree of usability. We talk with non-CS teachers about what they might want with computing, build prototype tools for them, and expect withering criticism. That’s how we’re learning to build CS that works for All. In this talk, Mark demonstrated multiple Teaspoon languages and invited participants to play with them.
Mark Guzdial is a Professor in Computer Science & Engineering at the University of Michigan. He studies how people learn computing and how to improve that learning. He was one of the founders of the International Computing Education Research conference. He worked for a dozen years in public policy as one of the leads on the Expanding Computing Education Pathways (ECEP) Alliance, helping 16 US states and Puerto Rico to improve and broaden their computing education. He is a Fellow of the ACM.
Watch the seminar: Coming soon!
Artificial intelligence, machine learning and data science series (Sep 2021 – Mar 2022)
Click here for a collated list of useful resources and links related to AI, ML and data science, shared during the seminars
|1 Mar 2022
||Democratizing AI education with and for families||Stefania Druga (University of Washington)|
|1 Feb 2022
||Teaching youth to use AI to tackle the Sustainable Development Goals||Tara Chklovski (Technovation)|
|11 Jan 2022
||Teaching Artificial Intelligence in K-12||Dave Touretzky (Carnegie Mellon University, AI4K12 Initiative) and Fred Martin (University of Massachusetts Lowell, AI4K12 Initiative)|
|7 Dec 2021
||What is it about AI that makes it useful for teachers and learners?||Rose Luckin (University College London)|
|16 Nov 2021
||Special panel session||Tabitha Goldstaub (UK AI Council), Chris Philp (UK Minister for Tech and the Digital Economy), Philip Colligan (Raspberry Pi Foundation), Danielle Belgrave (DeepMind), Alice Ashby (Brighton University) and Caitlin Glover (Sandon School, Chelmsford)|
|2 Nov 2021
||ML education for K-12: emerging trajectories||Matti Tedre and Henriikka Vartiainen (University of Eastern Finland)|
|5 Oct 2021
||Exploring the data-driven world: Teaching AI and ML from a data-centric perspective||Carsten Schulte, Yannik Fleischer and Lukas Höper (Paderborn University)|
|7 Sept 2021
||AI Ethics and Engagement with Children and Young People||Mhairi Aitken (The Alan Turing Institute)|
Democratizing AI education with and for families (1 Mar 2022)
Stefania Druga (University of Washington)
Children are now growing up with AI and we are slowly transitioning from a digital generation to an AI generation. Since 2017 Stefania has conducted research to explore how children interact with and make sense of the growing collection of “smart” inter-connected playthings in the world around them. Her findings uncover how children, as they play with these new devices, develop new ways of thinking about intelligence, emotion, and social interaction. She and her co-researchers also proposed guidelines and curriculum for teachers and parents to best support youth to develop a critical understanding of algorithmic bias and demystify AI capabilities. In this seminar, Stefania presented findings from the most recent international studies they conducted and also presented their open-source education tools such as Cognimates and curriculum.
Stefania Druga is currently a third-year Ph.D. candidate at the University of Washington Information School. Her research focuses on AI Literacy and the design of new computing platforms for children and parents. She also enjoys designing and building future smart toys and games. She is a Weizenbaum Research Fellow and awardee of the Jacobs Foundation Grant. She was previously a LEGO Papert Fellow during her time as a master’s student at MIT researching with Professor Mitch Resnick and the Scratch team. For more information, please have a look at her projects, papers, or resume.
Watch the seminar:
Teaching youth to use AI to tackle the Sustainable Development Goals (1 Feb 2022)
Tara Chklovski (Technovation)
Technovation is a global technology education nonprofit, empowering girls and underserved communities to tackle local, community problems using mobile and AI technologies. In this talk, Tara shared lessons on how to inspire and support youth to develop innovative solutions to complex real-world problems, in particular identifying areas for climate action.
Tara Chklovski is CEO of Technovation, a nonprofit that has empowered 300,000 participants from underserved communities in 100+ countries to tackle local problems using cutting-edge technologies (mobile and AI). She has been featured in the award-winning documentary Codegirl, and named “the pioneer empowering the incredible tech girls of the future” by Forbes. She has led the Global Online Education Taskforce to address education needs during COVID, the 2019 education track at the UN’s AI for Good Global Summit, presented at the International Joint Conference on AI, and the Global Partnership on AI for Humanity convened by the French Government.
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Teaching Artificial Intelligence in K-12 (11 Jan 2022)
Dave Touretzky (Carnegie Mellon University, AI4K12 Initiative) and Fred Martin (University of Massachusetts Lowell, AI4K12 Initiative)
What should K-12 students know about artificial intelligence, and what should they be able to do with it? The AI4K12 Initiative (AI4K12.org) is a joint project of the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA), with funding from the US National Science Foundation. AI4K21.org is developing national guidelines for teaching AI in K-12. Our work began with the release of a list of “Five Big Ideas in AI”, described in a poster that is now available in 15 languages. The guidelines themselves are organized as a series of progression charts, one for each big idea, covering four grade bands: K-2, 3-5, 6-8, and 9-12.
In this talk, Dave and Fred described some of the key insights into AI that they hope children will acquire, and how they see K-12 AI education evolving over the next few years.
David S. Touretzky is a Research Professor in the Computer Science Department and the Neuroscience Institute at Carnegie Mellon University. He is also the founder and chair of the AI4K12 Initiative (AI4K12.org). Dr. Touretzky’s 40 year research career spans work in knowledge representation, artificial neural networks, computational neuroscience, autonomous mobile robots, and computer science education. He is a Senior Member of Association for the Advancement of Artificial Intelligence, a Fellow of the American Association for the Advancement of Science, and was named a Distinguished Scientist by the Association for Computing Machinery.
Dr. Fred Martin is professor of Computer Science and associate dean for Teaching, Learning, and Undergraduate Studies for the Kennedy College of Sciences at the University of Massachusetts Lowell. Martin’s research group, the Engaging Computing Group, develops and studies novel computational design environments for learners, empowering them to create meaningful, personally satisfying projects.
Martin is presently co-leading an NSF-funded researcher-practitioner partnership, “CS Pathways RPP: A District Ownership-based Approach to Middle School Computer Science” with SUNY Albany and three urban school districts (two in Massachusetts, and one in New York State).
Martin is a past chair of the Computer Science Teachers Association (CSTA), served on Massachusetts’ Digital Literacy and Computer Science Standards Panel, and was a founding member of the AI4K12 Initiative’s steering committee.
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What is it about AI that makes it useful for teachers and learners? (7 Dec 2021)
Rose Luckin (University College London)
There are many ways in which AI can be used to support the teaching and learning process. For example, adaptive tutors and tutoring platforms help deliver one-to-one tutoring in particular subjects, and across the curriculum, voice-activated interfaces allow people to interact without needing to use a keyboard, and recommender systems help teachers to find the most suitable resources for their students quickly and effectively. However, for teachers to know exactly how to use AI with a group of students and for students to know how best to use AI to meet their requirements, they all need to understand something about AI. For this reason Rose and her team have developed the concept of AI Readiness as a framework to support their conversations with teachers about AI and to underpin a training course aimed at providing teachers and students with a contextualised course about AI, specifically designed for people within education and training. The aim is that the course will help increase confidence within teachers and learners and enable them to make better decisions about the way they apply AI in their practice.
In this talk, Rose discussed some examples of the work that she and her team have done with educational organisations using the AI readiness framework and explained the structure of the AI Readiness course they have developed. In the process, Rose explained what it is about AI that makes it useful in education and how to know if the AI you are looking at or interacting with is likely to be useful to you.
Rosemary (Rose) Luckin is Professor of Learner Centred Design at UCL Knowledge Lab. She was named one of the 20 most influential people in education in the Seldon List, 2017. Rose is Founder of EDUCATE Ventures Research Ltd., a London hub for start-ups, researchers and educators developing evidence-based educational technology. She is past president and current treasurer of the International Society for AI in Education and co-founder of the Institute for Ethical AI in Education. Rose’s 2018 book, Machine Learning and Human Intelligence: The Future of Education for the 21st Century describes how AI supports teaching and learning. Prior to joining Knowledge Lab in 2006, Rose was Pro-Vice Chancellor for Teaching and Learning at the University of Sussex.
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ML education for K-12: emerging trajectories (2 Nov 2021)
Matti Tedre and Henriikka Vartiainen (University of Eastern Finland)
Over the past decades, practical applications of machine learning (ML) techniques have shown the potential of data-driven approaches in computing. ML education has been primarily piloted in computing curricula in higher education, but increasingly in K-12 computing education, too. However, despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space.
This talk mapped the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. It situated that research in the broader context of computing education, and described what changes ML necessitates in the classroom. The talk outlined the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning many tenets of rule-based “classical” programming.
Dr. Matti Tedre is a professor of computer science, especially computing education and the philosophy of computer science, at the University of Eastern Finland. His 2019 book “Computational Thinking” (The MIT Press, with P.J. Denning) presented a rich picture of computing’s disciplinary ways of thinking and practicing, and his 2014 book “Science of Computing” (Taylor & Francis / CRC Press) portrayed the conceptual and technical history of computing as a discipline.
Dr. Henriikka Vartiainen is a senior researcher and university lecturer at the University of Eastern Finland, School of Applied Educational Science and Teacher Education. She has also worked as responsible researcher in several multidisciplinary projects focusing on, for example, technology education, co-design in school context, design-oriented pedagogy, and 21st skills. Currently, her research focuses especially on learning Machine Learning through co-design as well as on the ways to support children’s data agency. Her work on design-oriented pedagogy has received The Doctoral Dissertation Award 2014 by The Finnish Educational Research Association (FERA) as well as Young Researcher Award of the UEF in 2015.
Exploring the data-driven world: Teaching AI and ML from a data-centric perspective (5 Oct 2021)
Carsten Schulte, Yannik Fleischer and Lukas Höper (Paderborn University)
The talk raised the question of whether and how AI and ML should be taught differently from other themes in the CS curriculum at school. The tentative answer is that these topics require a paradigm shift for some teachers and that this shift has to do with the changed role of algorithms, of data, and of the societal context. The talk presented three small teaching examples from middle schools to illuminate the possible differences in teaching. The first example drew upon the Matchbox computer and successors like the sweet learning computer to teach the machine learning process, the second was about enactive teaching of Decision Trees, and the third about analysing location data. (Note: please have a fruit, ideally an apple, at hand during the presentation for some interactive elements!)
Dr. Carsten Schulte is a professor of computing education research at Paderborn University, Germany. His work and research interests are the philosophy of computing education and empirical research into teaching-learning processes (including eye movement research). Since 2017, he has been working together with Didactics of Mathematics (Paderborn University) in the ProDaBi project, in which Data Science and Artificial Intelligence are prepared as teaching topics. He is also PI in the collaborative research centre ‘Constructing Explainability’ on explainable AI.
Yannik Fleischer is a PhD student in mathematics education research at Paderborn University, Germany. His main research interest is to develop a concept to teach machine learning methods in school with a focus on decision trees, and to evaluate this by developing and examining teaching materials in practice. Since 2019, he has been supervising year-long project courses on data science in upper secondary and developing, implementing, and evaluating teaching modules for different levels in secondary school, mainly about machine learning with decision trees.
Lukas Höper is a PhD student in computing education research at Paderborn University, Germany. His main research interest is to develop the concept of data awareness for computing education and evaluate this by developing and examining teaching materials in practice. Since 2020, he has been working on data awareness in the ProDaBi project, among other topics on AI and Data Science in schools.
Watch the seminar:
AI Ethics and Engagement with Children and Young People (7 Sept 2021)
Mhairi Aitken (The Alan Turing Institute)
While recent years have brought significant and growing interest in ethical considerations relating to AI and machine learning, CS education or STEM outreach programmes typically focus on technical dimensions. This seminar set out the importance of embedding ethics at the heart of education and youth engagement relating to AI. Embedding ethics throughout all CS education is vital to ensure that future technologies and AI-powered services are designed to maximise the value and societal benefits of AI while avoiding potential negative impacts.
This seminar provided a brief overview and background to current debates around AI ethics, setting out key ethical principles and how they apply to AI, before discussing the ways in which these relate to children and young people. The talk drew on current research being undertaken in the Public Policy Programme at The Alan Turing Institute to illustrate opportunities and approaches to engage children and young people with this important topic. Moreover, it discussed the importance of engaging with children and young people to inform ethical practice.
Dr. Mhairi Aitken is an Ethics Fellow in the Public Policy Programme at The Alan Turing Institute. She is a Sociologist whose research examines social and ethical dimensions of digital innovation particularly relating to uses of data and AI. Mhairi has a particular interest in the role of public engagement in informing ethical data practices. Her past research has focussed in particular on the role of machine learning in finance; governance of data-intensive health research; ethical considerations around secondary uses of health data and; planning and development processes relating to renewable energy projects. Prior to joining the Turing Institute, Mhairi was a Senior Research Associate at Newcastle University. Between 2009 and 2018 Mhairi was a Research Fellow at the University of Edinburgh where she undertook a programme of research and public engagement to explore social and ethical dimensions of data-intensive health research. She held roles as a Public Engagement Research Fellow in both the Farr Institute of Health Informatics Research and the Scottish Health Informatics Programme (SHIP).
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Equity, diversity and inclusion series (Jan 2021 – Jul 2021)
Computing for generative justice: decolonizing the circular economy (13 July 2021)
Ron Eglash (University of Michigan)
Working with African, Native American and Latin American Indigenous cultures, we can see a contradiction to colonial stereotypes of “primitive” societies. Support for egalitarian social relations, circular economies, and ecological biodiversity in these traditional contexts are “proof of concept” for how they could be accomplished. Working with their modern descendants in CS education and community development, we can develop collaborative practices that bring together “bottom-up” cultural traditions such as African fractals, Native American biocomplexity and restorative justice with contemporary computing technologies and frameworks.
Dr. Ron Eglash obtained his B.S. in cybernetics and his M.S. in systems engineering at UCLA. He received his doctorate at UCSC in History of Consciousness under Donna Haraway. Ron was a faculty member in the STS department at RPI for two decades; he is now a Professor in the School of Information at the University of Michigan. He is known for his monograph African Fractals: modern computing and indigenous design; his anthology Appropriating Technology; and his software suite, Culturally Situated Design Tools. His work combines analysis of the social dimensions of science and technology with innovations at the intersections of anti-racist activism and computational design. Collaborating with Indigenous elders and artisans, urban makers, rural crafters and others, his research program on “generative STEM” develops systems that nurture the circulation of value in unalienated form, connecting schools and communities with decolonized forms of sustainable production. Free and open source access to the software and designs, as well as publications, are available at generativejustice.org.
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Why the ‘digital divide’ does not stop at access: understanding the complex interactions between socioeconomic disadvantage and computing education (1 June 2021)
Hayley Leonard and Thom Kunkeler (Raspberry Pi Foundation)
Around the world, young people from socially and economically disadvantaged backgrounds are less likely to have access to a home computer and to computing at school, and are underrepresented in computing-related qualifications and careers. Although national curricula in the four nations of the UK all include some form of mandatory computing in schools, the uptake of computing qualifications and careers amongst those from disadvantaged groups is still low. In this seminar, Hayley and Thom provided an overview of research into socioeconomic disadvantage and computing in the UK and beyond. They also shared some initial results from a qualitative study conducted with young people at risk of educational disadvantage in the UK, focusing on their attitudes towards computing as a discipline and their own digital capabilities.
Dr. Hayley Leonard is a Research Scientist at the Raspberry Pi Foundation. She was previously a primary school teacher and a lecturer in Psychology, where her research focused on how different factors affected children’s development and learning, especially those with special educational needs. At the Raspberry Pi Foundation, her work aims to understand factors affecting effective teaching and learning in computing education. She is particularly interested in issues of diversity and inclusion, and how best to help young people to access and fully engage with computing.
Thom Kunkeler is a Research Assistant at the Raspberry Pi Foundation. Prior to this, he graduated from the University of Amsterdam with a Research Masters in Social Sciences. His earlier research focused on socioeconomic inequality, racism and police violence, with a Masters thesis detailing social movements and political change during the civic unrest of 2014 in Ferguson, Missouri. At the Raspberry Pi Foundation, his research interests translate into understanding inequity in computing education, access to digital technologies, and the development of digital capital among young people.
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Physical programming inclusive of young children with visual disabilities (4 May 2021)
Cecily Morrison (Microsoft Research)
A large number of programming languages have been developed specifically to help young children learn to code inside and outside of school, but these are not accessible to children with visual disabilities. This session covered the lessons learned from designing and evaluating a physical programming language for teaching computational thinking and basic coding skills to children ages 7 – 11 regardless of level of vision (Project Torino/Code Jumper). In doing so, Cecily called out how tactile-spatial skills enhance coding abilities for all; inclusive education as more than subject matter learning; and the challenges of evaluating the efficacy of a new programming language.
Cecily Morrison, Ph.D. is a Principal Researcher at Microsoft Research Cambridge. Her recent work focuses on designing inclusive experiences for people who are blind or low vision. She co-led the team that designed Code Jumper and she is currently engaged in developing assistive agent technology in Project Tokyo. She has recently been named on the Queen’s Birthday Honours list for services to inclusive design.
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Including all learners in K-12 CS education through Universal Design for Learning (20 Apr 2021)
Maya Israel (University of Florida)
If we are truly committed to including all students in K-12 CS education, we must first reject the idea that we should plan instruction for the average learner and then modify that instruction for all those “other” learners. Our instructional practices, instead, should be flexible enough to account for the wide range of learner variability in today’s classrooms. This session provided research findings, lessons learned, and examples of how the Universal Design for Learning (UDL) framework can be used to design inclusive CS instructional activities that are accessible and engaging to a wide range of learners.
Maya Israel, Ph.D. is an associate professor of Educational Technology in the School of Teaching and Learning at the University of Florida. She is also the research director of the Creative Technology Research Lab. Prior to entering higher education, Dr. Israel was a special education teacher. Her research focuses on strategies for supporting academically diverse learners’ meaningful engagement in science, technology, engineering, and mathematics (STEM) with emphases on computer science education and Universal Design for Learning (UDL). She is currently PI on several grants including a recently funded National Research Foundation project that brings together researchers and educational leaders to address ways to make computer science education more inclusive to students with disabilities. Lastly, Dr. Israel works with multiple school districts on systemic and classroom strategies to more equitably include students with disabilities in K-12 computer science education initiatives.
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Designing STEM learning environments to support computational algorithmic thinking and Black girls: a possibility model for changing hegemonic narratives and disrupting STEM neoliberal projects (2 Mar 2021)
Jakita O. Thomas (Auburn University)
When interrogating the purpose and outcomes of STEM learning, important questions that come to bear are “STEM learning for what?” “For whom?” “How?” and “To what ends?” My research with Dr. Nicole Joseph (Vanderbilt University) and Dr. Yolanda Rankin (Florida State University) emerged from the desire to problematize national discourse about the neoliberal STEM project in the U.S. We sought to complicate this discourse because the argument that STEM learning has only economic ends, as it has been constructed globally, is complicated when thinking about Black girls’ and women’s experiences. In this talk, Jakita shared findings from a larger seven-year longitudinal between-subjects research study that explored the development of computational algorithmic thinking (CAT) capabilities in Black girls as they engaged in iterative game design for social change. The findings suggest that the ways in which Supporting Computational Algorithmic Thinking (SCAT) was intentionally designed affords Black girls with opportunities to radically shape their identities as producers, innovators, and disruptors of deficit perspectives.
Dr. Jakita O. Thomas is a Philpott Westpoint Stevens Associate Professor of Computer Science and Software Engineering at Auburn University in Auburn, AL, and Director of the CUltuRally & SOcially Relevent (CURSOR) Computing Lab.
Dr. Thomas received a Bachelor of Science degree in Computer & Information Science with a minor in Mathematics from Spelman College in 1999. In 2006, Dr. Thomas was conferred a Ph.D. in Computer Science with a specialization in the Learning Sciences and Technology from the Georgia Institute of Technology in Atlanta, GA.
Dr. Thomas is a recipient of the National Science Foundation’s Faculty Early Career Development (CAREER) Award (2012 – 2019). She is also a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) (2016).
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Equity-focused teaching in K-12 CS: strategies for teachers, teacher educators, and districts (2 February 2021)
Tia Madkins (University of Texas at Austin), Nicol R. Howard (University of Redlands) and Shomari Jones (Bellevue School District)
As school communities increase computer science (CS) learning opportunities, it is especially important they also meet the needs of minoritized students. Using equity-focused teaching practices in CS learning environments is one way to build upon assets-based approaches (seeing students’ ways of knowing as strengths rather than deficits) and better serve minoritized students. In this seminar, Tia, Nicol and Shomari discussed equity-focused teaching practices, such as culturally relevant pedagogy or culturally responsive teaching, and how educators can engage them in CS learning environments. They shared research perspectives and practical examples related to instruction, teacher education, and district-level initiatives. The goals for this session were for educators, teacher educators, and school and/or district personnel to better understand equity-focused teaching practices and how to support equity-focused initiatives in their local communities to create more inclusive learning environments for minoritized students.
Tia C. Madkins, Ph.D. is an assistant professor in STEM Education and Department of Curriculum and Instruction in the College of Education and a faculty research affiliate with the Population Research Center and the Center for the Study of Race and Democracy at The University of Texas at Austin. Her research focuses on supporting teachers to design inclusive STEAM + computing classrooms and engage equity-focused pedagogies with minoritized students, especially Black girls.
Nicol R. Howard, Ph.D. is an assistant professor and co-director of the Race in Education Analytics Learning Lab, REAL Lab, in the School of Education at the University of Redlands where her research foci are STEM and computer science equity and parent involvement. Dr Howard’s concern for equity in education has led to publications such as Terms of engagement: Redefining parental involvement and STEM Identity for Black girls and a recent co-authored book entitled Coding+Math: Strengthen K-5 Math Skills with Computer Science.
As Director of Equity and Strategic Engagement for the Bellevue School District, Shomari Jones, is charged with leading staff in thoughtful exploration of institutionalized racism and its impact on student learning. Through providing professional learning experiences and strategic support, educators in Bellevue develop the will, skill, knowledge, and capacity to eliminate racial disparities and achieve system-wide equity and excellence for kids and their families.
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Computing education for underrepresented groups (5 January 2021)
Peter Kemp (King’s College London) and Billy Wong (University of Reading)
The change in the English national curriculum, which removed ICT and replaced it with computing, has coincided with a steep decline in digital education provision for some of the most vulnerable groups in society. Using national datasets and field work, this talk took an intersectional approach to look at trends in student participation and attainment. Peter and Billy attempted to outline reasons using literature and research that draws on psychological as well as sociological perspectives.
Dr. Peter Kemp is Lecturer in Computing Education at King’s College London, where he runs the PGCE in Computing. His research interests are centered around digital equity, digital arts education, curriculum design, and creativity and computing. His Ph.D. looked at the intersection of the computing and media studies subject domains in the development of student digital creativity. He has published multiple reports on the changing landscape of computing education in England. In his spare time he helps run 3Dami, a non-profit organisation that teaches 3D digital animation to school students.
Dr. Billy Wong is an Associate Professor at the Institute of Education, University of Reading. His areas of research are educational identities and inequalities, especially in the context of higher education and STEM education. His publications have explored the changing views and experiences of university students and staff, as well as young people’s science and career aspirations. He was part of the team which developed the concept of ‘science capital’. He is author of Science Education, Career Aspirations and Minority Ethnic Students (2016, by Palgrave) and his forthcoming book, The ideal student: Deconstructing expectations in higher education, will be published in April 2021 (Open University Press).
Watch Peter and Billy’s seminar:
2020 seminars (May 2020 – Dec 2020)
|1 Dec 2020
||The role of block-based programming in computer science education||David Weintrop (University of Maryland)|
|3 Nov 2020
||PRIMM: encouraging talk in programming lessons||Sue Sentance (Raspberry Pi Foundation)|
|6 Oct 2020
||Formative assessment and feedback to support student learning in CS classrooms||Shuchi Grover (Stanford University)|
|8 Sept 2020
||METRECC Instrument: sharing and contributing to international K-12 computing curricula and experiences||Monica McGill (CSEdResearch.org), Keith Quille (Technological University Dublin), Rebecca Vivian (University of Adelaide) and Elizabeth Cole (University of Glasgow)|
|28 July 2020||Gender balance in computing: what the research says||Katharine Childs (Raspberry Pi Foundation)|
|14 July 2020||Computational thinking test for beginners||María Zapata Cáceres (Universidad Rey Juan Carlos)|
|30 June 2020||Subgoal labels: reducing cognitive load in intro CS||Briana Morrison (University of Nebraska-Omaha)|
|16 June 2020||Unplugged computing and semantic waves||Jane Waite (Queen Mary University of London)|
|2 June 2020||Programming and mathematics: insights from research in England||Dame Celia Hoyles (University College London)|
|19 May 2020||Learning AI at school with Scratch and LearningML||Juan David Rodríguez (Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado – INTEF)|
|5 May 2020||Online and hybrid instruction for computer science classrooms||Lauren Margulieux (Georgia State University)|
The role of block-based programming in computer science education (1 December 2020)
David Weintrop (University of Maryland)
Block-based programming is increasingly becoming the way that young learners are being introduced to the practice of programming and the field of computer science more broadly. In this talk, David presented results from his research into the strengths and drawbacks of block-based programming. This included sharing learner-reported perceptions on block-based programming, results from studies comparing block-based and text-based programming, and findings looking at if and how block-based instruction prepares learners for future text-based programming. He also presented results looking at the role of block-based tools in creating accessible and equitable computer science learning experiences. The goal for this talk was to help educators make informed decisions about if, how, and in what ways to incorporate block-based programming into their instruction.
Dr. David Weintrop is an Assistant Professor in the Department of Teaching & Learning, Policy & Leadership in the College of Education with a joint appointment in the College of Information Studies at the University of Maryland. His research focuses on the design, implementation, and evaluation of accessible, engaging, and equitable computational learning experiences. He is also interested in the use of technological tools in supporting exploration and expression across diverse contexts including STEM classrooms and informal spaces.
His work lies at the intersection of design, computational thinking education, and the learning sciences. David has a Ph.D. in the Learning Sciences from Northwestern University and a B.S. in Computer Science from the University of Michigan. He spent one year as a postdoctoral researcher at the University of Chicago studying computer science learning in elementary classrooms prior to joining the faculty at the University of Maryland. Before starting his academic career, he spent five years working as a software developer at a pair of start-ups in Chicago.
Watch David’s seminar:
PRIMM: encouraging talk in programming lessons (3 November 2020)
Sue Sentance (Raspberry Pi Foundation)
PRIMM is an approach to structuring programming lessons that counters the known problem of novices writing programs before they are yet able to read them, and focuses on students talking about how and why programs work before they tackle editing and writing their own programs. PRIMM stands for Predict, Run, Investigate, Modify and Make.
In this talk Sue described how language and talk are emphasised in this socioculturally inspired approach to structuring programming lessons. She described a mixed-methods study which evaluated the effectiveness of PRIMM with around 500 students over a period of about ten weeks, and showed a positive impact on learning. She also considered how questioning in programming can be developed via a combination of the Block Model and the PRIMM approach. This talk will be of interest to you if you are interested in how beginners learn computer programming or have struggled with programming yourself.
Dr. Sue Sentance is Chief Learning Officer at the Raspberry Pi Foundation and Visiting Fellow at King’s College London, UK. She researches the teaching of programming in school, teacher professional development, and physical computing. Her academic background is in computer science, artificial intelligence and education, and she is a qualified teacher and teacher educator. She currently has a leading role in a nationwide government-funded programme to bring high-quality computing education to all schools in England.
Watch Sue’s seminar:
Formative assessment and feedback to support student learning in CS classrooms (6 October 2020)
Shuchi Grover (Stanford University)
One can only improve what one measures. Formative assessments & feedback serve the important purpose of assessment for learning (as opposed to summative assessments, which are assessments OF learning). They provide feedback to both teachers and students on students’ learning and understanding (or the lack thereof).
School classrooms teaching introductory CS need to work better to integrate of assessment and instruction—on combining teaching with an ongoing measurement of student progress toward instructional goals. Formative assessments serve as probes into students’ understanding, and this in turn, helps teachers’ identify student misconceptions as they are teaching.
Formative assessments could take various forms—quick exercises such as multiple choice questions, small directed/coding projects (with rubrics), Parson’s problems, fixing buggy code, or reflection questions. Many teachers use formative assessment at the beginning or end of class as brief “entry tickets” or “exit tickets”. This seminar featured several examples of various forms of formative assessment that teachers at various grade levels can use.
Dr. Shuchi Grover is a senior research scientist at Looking Glass Ventures and visiting scholar at Stanford University. A computer scientist and learning scientist by training, her work in computer science (CS) and STEM education since 2000 has spanned both formal and informal settings in the US, Europe, and Asia. Her current research centers on computational thinking (CT), CS education, and STEM+CT integration mainly in formal K-12 settings.
Dr. Grover is a recipient of several grants from the National Science Foundation to conduct research on curriculum and assessments in STEM learning and CT in varied PK-12 contexts. She also works at the intersection of learning, assessment, and big data analytics to shape future environments for deeper learning.
She has authored over 100 well-cited scholarly and mainstream articles. She has advised the K-12 CS Framework as well as several K-12 school districts on CS implementation/integration. She serves as a member of the ACM Education Advisory Committee and on the editorial board of ACM Transactions on Computing Education.
She earned a Ph.D. in Learning Sciences & Technology Design from Stanford University (with a focus on Computer Science Education), Masters degrees in Education (Harvard University) and Computer Science (Case Western Reserve University), and Bachelors degrees in Computer Science and Physics from BITS Pilani (India).
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METRECC Instrument: sharing and contributing to international K-12 computing curricula and experiences (8 September 2020)
Monica McGill (CSEdResearch.org), Keith Quille (Technological University Dublin), Rebecca Vivian (University of Adelaide) and Elizabeth Cole (University of Glasgow)
This seminar told the story of the development of METRECC, international collaboration, ongoing outcomes and how it relates and can help you, the teacher. As the discipline of K-12 computer science (CS) education evolves, international comparisons of curriculum and teaching provide valuable information for policymakers and educators.
The MEasuring TeacheR Enacted Computing Curriculum (METRECC) instrument surveys teachers in K-12 schools about their implementation of CS curriculum to understand pedagogy, practice, curricula, resources and experiences in classrooms around the world. The open-source published data represents 244 teachers across seven countries (Australia, England, Ireland, Italy, Malta, Scotland and the United States) and the instrument has evidence of validity and reliability. The resulting METRECC protocol combines a country report template and a teacher survey that will provide K-12 teachers with a means to communicate their experiences.
Further, to extend the METRECC work, METRECC South Asia has been piloted in Nepal and Pakistan. This instrument underwent a thorough review and could also be used within other countries in South Asia to offer a snapshot of enacted curriculum in middle and low income countries.
Monica McGill, Ed.D. is currently the CEO & President of the non-profit CSEdResearch.org. Monica has been conducting computing education research for over a decade, with her research work now focusing on supporting K-12 computing education researchers and evaluators. She is also a CS Professor at Knox College in Galesburg, Illinois, USA.
Keith Quille, Ph.D. is a Lecturer at the Technological University Dublin, Ireland. Keith is a project lead a CSinc.ie where the research group specialises in CS education research (at primary, second and third-level), K-12 outreach and K-12 teacher professional development. Keith was also a second-level teacher for several years.
Dr. Rebecca Vivian is a Research Fellow in the Computer Science Education Research Group (CSER) at The University of Adelaide. She is Lead designer for CSER’s national K-12 Digital Technologies Education teacher training program and conducts research into STEM engagement, K-12 and tertiary CS education and teacher professional learning.
Elizabeth Cole is a PhD student at the University of Glasgow and an active member of the Centre for Computing Science Education. She brings a wealth of experience working in schools to her research. Elizabeth is currently working on computing science pedagogy in the early years of formal education.
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Gender balance in computing: what the research says (28 July 2020)
Katharine Childs (Raspberry Pi Foundation)
Gender Balance in Computing is a 4-year programme of research to explore ways to increase girls’ participation in computing. The programme will investigate approaches to overcoming barriers to gender balance in computing through a number of different interventions carried out in primary and secondary (K-12) schools in England. This seminar presented a summary and synthesis of the current knowledge about gender equity in computing and examined key barriers which can prevent girls’ participation in the subject.
Katharine Childs works in the Research team at the Raspberry Pi Foundation and coordinates the Gender Balance in Computing project. Her background spans both computer science and learning theory, via her first-class honours degree in IT & Computing and Masters degree in computing education. Following 15 years of professional experience working in the IT sector, she went on to teach computing in primary (K-5) schools and deliver professional development activities for other primary teachers. Katharine writes, blogs and speaks about Computer Science Education research, with a particular focus on gender equity, inclusivity and physical computing.
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Computational thinking test for beginners (14 July 2020)
María Zapata Cáceres (Universidad Rey Juan Carlos, Madrid)
Assessing computational thinking is an indispensable element to consider in order to introduce it into school curricula. This talk described how a Beginners’ Computational Thinking Test (BCTt), aimed at early ages, was designed, submitted to a content validation process through expert judgement procedure and then administered to Primary School students. The results obtained from the BCTt were discussed.
María Zapata Cáceres qualified as an Architect at the Universidad Politécnica de Madrid and graduated in Computer Science Engineering at the Universidad Nacional de Educación a Distancia in Spain. She also holds masters degrees in Virtual Environments (CSA) and Videogames Design and Production (UEM). She is currently pursuing her doctorate in Information Technology and Communications at the Universidad Rey Juan Carlos in Madrid, where she is a researcher and visiting professor in the Games Design and Development bachelor’s degree. Her main area of research includes videogames as learning instruments for computer science both in individual and collaborative environments. She has more than 15 years of professional experience as an entrepreneur and independent professional with activities related to 3D design, videogames, technology, and teaching.
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Subgoal labels: reducing cognitive load in intro CS (30 June 2020)
Briana Morrison (University of Nebraska-Omaha)
Cognitive load is the amount of resources utilized in an individual’s working memory during learning. This talk presented the use of one cognitive load reducing mechanism: implementing subgoal labels within worked examples. The results of a quasi-experimental study using a subgoal learning framework throughout a semester-long programming course were discussed. Results included improved performance on formative quizzes, lower variance in exam scores and fewer students dropping or failing the course when learning with subgoals. Information on the next implementation steps, including how you can use subgoal labels in your classroom were covered.
Dr. Briana Morrison is an Assistant Professor at the University of Nebraska- Omaha. Briana worked at IBM for eight years as a software developer before she transitioned to academia. She was an Asst. Professor at Southern Polytechnic State University (now Kennesaw State University) for 20 years in the CS department, was the Undergraduate Coordinator for the CS and SWE programs and helped found the Computer Game Design and Development degree program. Dr. Morrison earned her BS in Computer Engineering from Tulane, her MS from Southern Polytechnic and her PhD from the Georgia Institute of Technology. Briana currently has two NSF grants: IUSE: Collaborative Research: Developing and Assessing Subgoal Labels for Imperative Programming to Improve Student Learning Outcomes; and CNS: RET Site: Wearable Research for In-Service STEM Teachers (WRIST).
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Unplugged computing and semantic waves (16 June 2020)
Jane Waite (Queen Mary University of London)
This talk explored how Legitimation Code Theory, and, in particular, semantic waves, provides a useful way to understand what makes unplugged computing activities effective (or not) in the classroom. Jane gave an overview of the theory, discussed how it applies to unplugged activities, and described a case study where it was applied to a specific, widely used, unplugged activity. In particular, Jane showed that the published lesson plan follows a semantic wave, and suggested that semantic waves are useful both in developing and reviewing lesson plans around unplugged (and other) computing activities. They also have great potential in teacher training and continuous professional development of computing teachers.
Jane Waite works and studies at Queen Mary University of London. She is undertaking a part time PhD studying the teaching of design in K-5 (primary) programming activities. Jane also organises and runs teacher professional development and undergraduate modules on computer science education. Working with Sue Sentance she has researched PRIMM, the micro:bit, and pedagogy in general. With Paul Curzon she is investigating the use of Semantic Waves in the teaching of computer science. Jane is the Computing At School Research and University Working Group Chair running #CsEdResearchBookClub every first Thursday of the month.
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Programming and mathematics: insights from research in England (2 June 2020)
Dame Celia Hoyles (UCL Institute of Education, University College London)
In England, computing including a component of programming is compulsory for all students from age 6 to 16 years old. In this talk, Celia described the UCL ScratchMaths research project that developed a 2-year curriculum for 9-11 year olds in England aligned to the mandatory national computing and national mathematics primary curricula. ScratchMaths set out to support the teaching of carefully selected core ideas of computer programming alongside specific fundamental mathematical concepts, thus seeking to exploit potential for learning in both subjects by forging links between them. Celia presented the design features of this project, the findings from its external evaluation and internal monitoring, and the ongoing next steps.
Professor Dame Celia Hoyles was awarded a first-class honours degree in mathematics from the University of Manchester and holds a masters and doctorate in mathematics education. She taught mathematics in London schools before moving into higher education. She became a professor at the Institute of Education, University of London in 1984.
Celia has received many awards: first recipient of the International Commission of Mathematics Instruction (ICMI) Hans Freudenthal medal in 2004, and of the Royal Society Kavli Education Medal in 2011. She has received Hon Doctorates from the Open University, Loughborough University, Sheffield Hallam University and University of Bath. In 2016, she received the Suffrage Science award for Communications in acknowledgement of her scientific achievements and ability to inspire others especially women into mathematics.
Celia has given policy advice in mathematics as Chair of the Joint Mathematical Council of the United Kingdom 1999-03, founder member of the Advisory Committee on Mathematics Education (ACME) 2002-4, and the UK Government’s Chief Adviser for mathematics 2004- 07. She served as the Director of the National Centre for Excellence in the
Teaching of Mathematics (NCETM), 2007-13. Celia was President of the Institute of Mathematics and its Applications (IMA) (2014-15). Celia was made an Officer of the Order of the British Empire in 2004 and a Dame Commander in 2014.
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Learning AI at school with Scratch and LearningML (19 May 2020)
Juan David Rodríguez (Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado – INTEF)
In this talk, Juan described LearningML, a tool he is developing together with the Kindergarten and Beyond and Lifelong Learning (KGB-L3) research group at the Universidad Rey Juan Carlos, Madrid. LearningML is intended to learn and teach the basis of machine learning, the most prevalent technique used nowadays in artificial intelligence applications. During the seminar, Juan performed a practical demo and showed how practical AI projects can help to foster computational thinking skills, adding new concepts, practices, and perspectives.
Juan David Rodríguez is a secondary education teacher and software developer. He works at Spain’s National Institute of Educational Technologies and Teacher Training (INTEF), a unit of the Spanish Ministry of Education and Vocational Training which has responsibility for the integration of ICT and teacher training in non-university educational stages. Juan is currently working on computational thinking skills development through practical artificial intelligence activities. He has started exploring how machine learning (ML), one of the most used techniques in current AI applications, can be taught at school. To do this, he is developing the educational tool LearningML which is designed to easily build ML models that can be used in Scratch programs.
LearningML (now available in English and Spanish!):
- Website: learningml.org
- Machine Learning editor: learningml.org/editor
- Programming platform: learningml.org/scratch
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Online and hybrid instruction for computer science classrooms (5 May 2020)
Lauren Margulieux (Department of Learning Sciences, Georgia State University)
Online instruction comes in many forms to serve many purposes. It can be a powerful tool to add to your teaching practice, especially when mindfully paired with face-to-face instruction for a hybrid classroom. This talk described multiple goals that can be achieved through online instruction, how to mix it with face-to-face classrooms, and tips for making it successful.
Lauren Margulieux, Ph.D. is an Assistant Professor of Learning Sciences at Georgia State University. She received her PhD from Georgia Tech in Engineering Psychology, the study of how humans interact with technology. Her research interests are in educational technology and online learning, particularly for computing education. She focuses on designing instructions in a way that supports online students who do not necessarily have immediate access to a teacher or instructor to ask questions or overcome problem-solving impasses.
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