Over the past four decades, Cognitive Load Theory (CLT) has achieved a large body of research by scholars around the world. As a result, CLT has grown and expanded significantly, becoming a leading theory in the field of learning and instruction.
As researchers increasingly combine it with other theories of learning and instruction, CLT has evolved into an interdisciplinary theory. The conference will reflect this growing diversity of topics and research directions.
Abstracts for empirical or theoretical paper submissions should have a maximum of 4,500 characters (not including the title, author information, and reference list).
Abstract for posters (research at an earlier stage) should have a maximum of 3,500 characters (not including title, author information, and the reference list).
Note that it is possible to submit an abstract even if the data are not available at the time of submission (but at the time of the conference).
Please use this template for either a paper or poster submission.
Abstract submission deadline: Monday 3 June 2024
Notification of acceptance: Monday 15 July 2024
3-day conference attendance | Cost |
---|---|
General rate - Early bird until Friday 18 October | $550 |
General rate - From Saturday 19 October | $650 |
Student rate | $330 |
To be eligible for the student rate you must be currently completing a PhD, Master of Philosophy, or master’s coursework.
*Fees are per person and are GST inclusive.
'Intelligence' is a commonly used term that is never properly defined. We assume humans are the most intelligent animals with other animals having lesser amounts. The advent of artificial intelligence has given rise to suggestions that we may have developed something that will eventually exceed us in intelligence. The cognitive architecture used by cognitive load theory can provide us with a base for considering intelligence. That base suggests firstly, that rather than being fixed, intelligence is heavily dependent on the variable contents of long-term memory and secondly, in evolution by natural selection, we already are faced with an intelligent system that vastly exceeds our own intelligence.
John Sweller, Emeritus Professor of Educational Psychology, University of New South Wales
John's research is associated with cognitive load theory. The theory is a contributor to both research and debate on issues associated with human cognition, its links to evolution by natural selection, and the instructional design consequences that follow. Based on hundreds of randomised, controlled studies carried out by many investigators from around the globe, the theory has generated a large range of novel instructional designs from our knowledge of human cognitive architecture. Based on any commonly used citation index, the work has been cited on more than 25,000 occasions.
How do educators harness the best of explicit instruction and discovery learning? Load reduction instruction (LRI) is an instructional strategy aimed at addressing this question. Drawing on key concepts and evidence from cognitive load theory and information processing models, LRI seeks to ease the cognitive burden on students so they can learn effectively. Initially, LRI involves explicit instruction. Then, as students develop fluency and automaticity in knowledge and skill, LRI moves onto less structured approaches such as guided discovery-, problem-, and inquiry-based learning. In this presentation, Andrew will explain LRI, share recent research findings, and identify some of the methodological approaches and opportunities LRI affords researchers in education and psychology.
Andrew J. Martin, Scientia Professor, Professor of Educational Psychology, and Chair of the Educational Psychology Research Group, School of Education, University of New South Wales
In addition to his substantive position with The University of New South Wales, Andrew is an Honorary Research Fellow in the Department of Education at the University of Oxford and a registered psychologist with the Psychology Board of Australia. He specialises in student motivation, engagement, learning, and quantitative research methods. He is a consulting editor for Psychological Review, Journal of Educational Psychology, and Educational Psychology and serves on numerous international editorial boards including Educational Psychologist, Learning and Instruction, Contemporary Educational Psychology, British Journal of Educational Psychology, Learning and Individual Differences, and Journal of Experimental Education. He is a Fellow of the American Psychological Association and the American Educational Research Association.
I approach this topic from the perspective of the role of models in instructional design. ChatGPT, for instance, is a Large Language Model (LLM), so questions such as these arise: What is an LLM a model of? How is the modelling done? How does the model create value for instructional designers, learners, and researchers? I discuss in what sense LLMs model the knowledge to be learned - the 'content'- because that seems to be their primary function in supporting instructional design. I also discuss how LLMs might serve as models of learners and learning, called the "student model" in intelligent tutoring systems. I critically compare the similarities and differences to more established content and student modelling methods, namely Semantic Models, Bayesian Network Models, and Cognitive Models. By the end of the presentation, it should become clearer to what extent LLMs contribute to insights and create value beyond other approaches to educational modelling.
Peter Reimann, Professor of Education, Co-Director, Centre for Research on Learning and Innovation, Sydney School of Education and Social Work, The University of Sydney
Peter has been a Professor of Education at The University of Sydney since 2003. He has a PhD in psychology from the University of Freiburg, Germany. Having initially trained as a cognitive psychologist with a general interest in technology-augmented and technology-mediated learning, Peter has frequently applied computational modelling methods to advance learning research. His main research area is Computer-supported Collaborative Learning. One of his contributions to CSCL was introducing process-mining methods for analysing trace data. Also relevant to CSCL research is his work (with Jacobson and Kapur) on the role of complexity theory for theories of human learning. Currently, he and his PhD students are conducting research employing semantic technologies such as Knowledge Graphs, to support peer tutoring and learning from argumentation. During his career, Peter has directed two research centres: The Centre for Research on Computer-supported Learning & Cognition (CoCo) with Peter Goodyear, and the Centre for Research on Learning and Innovation (CRLI) with Lina Markauskaite.
Adam Szulewski, Queen’s University, Canada | André Tricot, Université Paul-Valéry Montpellier, France |
Babette Park, University of Education Freiburg, Germany | David Feldon, Utah State University, USA |
Ferdinand Stebner, University of Osnabrück, Germany | Fred Paas, Erasmus University Rotterdam, The Netherlands |
Joachim Wirth, Ruhr-University Bochum, Germany | Julie Lemarié, Université of Toulouse, France |
Maria Opfermann, University of Wuppertal, Germany | Nadine Marcus, University of New South Wales, Australia |
Paul Ayres, University of New South Wales, Australia | Roland Brünken, Saarland University, Germany |
Shirley Agostinho, University of Wollongong, Australia | Tzu-Chien Liu, National Taiwan Normal University, Taiwan |
Vicki Likourezos, University of Sydney, Australia | Alexander Renkl, University of Freiburg, Germany |
Anique de Bruin, University of Maastricht, The Netherlands | Slava Kalyuga, University of New South Wales, Australia |
Detlev Leutner, Duisburg-Essen University, Germany | Florence Lespiau, University of Nîmes, France |
Juan C. Castro-Alonso, University of Birmingham, UK | Kim Ouwehand, Erasmus University Rotterdam, Netherlands |
Franck Amadieu, University of Toulouse, France | Ouhao Chen, University of Leeds, UK |
Paul Ginns, University of Sydney, Australia | Sahar Bokosmaty, University of Wollongong, Australia |
Tina Seufert, University of Ulm, Germany | Joy Y. Lee, Leiden University, The Netherlands |
Stoo Sepp, University of New England, Australia |
While there are no “official conference hotels” we can certainly suggest the following hotels as starting points:
Located at: 9 Missenden Road, Camperdown
Recently refurbished rooms include ensuite bathroom, LCD TV, FREE wireless Internet access, tea- and coffee-making facilities. Walk to Newtown’s lively King Street for restaurants and night life. A 20-minute walk to The University of Sydney conference location.
Located at: Goulburn Street, Surry Hills
One-, two- and three-bedroom apartments. Apartments include a balcony or courtyard, a fully equipped kitchen, laundry, airconditioning, wifi and modern appliances throughout. Stylish cultural and café scene at your doorstep. A 20-minute bus ride to The University of Sydney conference location.
Recently refurbished rooms with a contemporary interior design and fitout. Features include a kitchen and laundry with plenty of room to make you feel comfortable in the space. Situated among Chippendale’s cool cafes, eateries and galleries, and close to local retail outlets. A 20-minute walk to The University of Sydney conference location.
More generally, The University of Sydney is centrally located within Greater Sydney, and there will be plenty of hotel, Airbnb and Stayz options in nearby suburbs.
If you arrange to stay in a location further away, the closest train station is Redfern station (15–minutes’ walk to the conference location, or serviced every 15–20 minutes by the University bus service from the Eastern side of the station in Gibbons St). The conference location is also close to many government bus routes. For travel planning, we recommend the official Transport NSW website and/or App.