At the EFS we are trialing novel approaches and methods to support collective learning about education futures, emerging technologies, and policymaking. This Learning Library documents our shared repertoire of work intended for diverse audiences: researchers, educators, policymakers, plus other diverse ‘publics’ wishing to learn together about the interplay between education, technology, and democracy. Our library archive includes a range of resources: key concepts (glossary), and brief summaries of methods we have trialled (snapshots); non-traditional research outputs (online games and interactive tools); white papers, reports, and submissions; traditional research outputs (journal articles); and, media articles.
:Education Futures Studio]
Learning library
Glossary
A shared vocabulary is important for diverse stakeholders to learn about, and understand, emerging technologies in education. Informed by various projects, our Technical Democracy Collective is building a glossary which highlights keywords (concepts, organisations, and processes) that inform our research, policy and practice.
ACARA: Australian Curriculum and Reporting Authority.
Artificial intelligence (AI): An autonomous, or semi-autonomous computer system that employs algorithms to learn from patterns in large data sets in order to improve predictive abilities (see also machine learning).
Assessment: A process of gathering information and using observation to judge the progress of students. Tests are a measuring tool as one part of assessment.
Automated essay scoring (AES): A psychometric-based form of digitalised education tests that integrates algorithm models with essay datasets in order to score student writing according to specific features or criteria.
Black boxes: In automated systems that use artificial intelligence, a black box system allows someone to see the input or output but does not allow a view of what happens in between. If an AES system uses deep learning it is considered a black box as there is no way to know exactly how the system makes a decision and provides a score.
Deep learning (also known as unsupervised learning): A form of machine learning where a computer is enabled to predict and classify information without human input.
Education technology (EdTech): Technology used in a range of education areas, including administration and teaching and learning. EdTech is most commonly associated with commercial products.
High-stakes test: A test that carries “serious consequences for students or for educators”. This may encompass the decision to pass or certify a particular individual or the ranking of institutions based on cohort results. In such a test, high scores “may bring public praise or financial rewards; low scores may bring public embarrassment or heavy sanctions”https://www.aera.net/About-AERA/AERA-Rules-Policies/Association-Policies/Position-Statement-on-High-Stakes-Testing
Hybrid forum: A form of consultation involving stakeholders with diverse expertise that focuses upon collective learning and experimentation in response to a particular socio-technical controversy.
Machine learning: A form of artificial intelligence that uses algorithms to make predictions from data.
NAPLAN: National Assessment Program – Literacy and Numeracy.
Natural language processing: The capacity of a computer trained to understand spoken and written human language.
Socio-technical controversy: Controversies that involve both social and technical dimensions. Examples include nuclear power, urban planning and the use of automated technologies like artificial intelligence.
Supervised learning: A form of machine learning where an algorithm is trained by a human such that data (input) and has a predefined output.
Snapshots
As part of this EFS Learning Library, our team is building a shared toolkit which highlights how novel methods and examples have informed our work. Each snapshot slide-deck presents a series of examples, followed by a particular artefact (e.g. a game, report, or white paper) generated by our Technical Democracy Collective.
Training and courses
We are in the process of planning training and courses. More details coming soon.
White papers, reports and submissions
Automated Essay Scoring Project
White Paper: Key Issues and Recommendations
Gulson, K., Thompson, G., Swist, T., Kitto, K., Rutkowski, L., Rutkowski, D., Hogan, A., Zhang, V., Knight, S. (2022). Automated Essay Scoring in Australian Schools: Key Issues and Recommendations (White Paper). Education Innovations White Paper Series ISSN 2653-6749. Sydney Social Sciences and Humanities Advanced Research Centre (SSSHARC), University of Sydney, Australia.
Policy Brief: Collaborative Policymaking
Gulson, K., Thompson, G., Swist, T., Kitto, K., Rutkowski, L., Rutkowski, D., Hogan, A., Zhang, V., Knight, S. (2022). Automated Essay Scoring in Australian Schools: Collaborative Policymaking (Policy Brief). Education Innovations Policy Brief Series ISSN 2653-6757. Sydney Social Sciences and Humanities Advanced Research Centre (SSSHARC), University of Sydney, Australia.
Media articles
Gulson, K., Benn, C., Kitto, K., Knight, S., & Swist, T. (2021) Algorithms can decide your marks, your work prospects and your financial security. How do you know they’re fair? The Conversation.
Academic publications
Below is a selection of our collective’s latest academic publications.
Gulson, K., & Sellar, S. (2024). Anticipating Disruption: Artificial Intelligence and Minor Experiments in Education Policy. Journal of Education Policy, 1-16. https://doi.org/10.1080/02680939.2024.2302474
Gulson, K. N., Murphie, A., & Witzenberger, K. (2021). Amazon Go for Education?: Artificial Intelligence, Disruption, and Intensification. In C. Wyatt-Smith, B. Lingard, E. Heck (Eds.), Digital Disruption in Teaching and Testing (pp. 90-106). New York: Routledge.
Gulson, K. N., Sellar, S., & Webb, P. T. (2022). Algorithms of Education: How Datafication and Artificial Intelligence Shape Policy. Minneapolis: University of Minnesota Press.
Gulson, K. N., & Witzenberger, K. (2022). Repackaging Authority: Artificial Intelligence, Automated Governance and Education Trade Shows. Journal of Education Policy, 37(1), 145-160, DOI: 10.1080/02680939.2020.1785552
Holloway, J., Lewis, S., & Langman, S. (2023). Technical Agonism: Embracing Democratic Dissensus in the Datafication of Education. Learning, Media and Technology, 48(2), 253-265. https://doi.org/10.1080/17439884.2022.2160987
Howard, S. K., Swist, T., Gasevic, D., Bartimote, K., Knight, S., Gulson, K., Apps, T., Peloche, J., Hutchinson, N., & Selwyn, N. (2022). Educational Data Journeys: Where Are We Going, What Are We Taking and Making for AI? Computers and Education: Artificial Intelligence, 3, 100073. https://doi.org/10.1016/j.caeai.2022.100073
Knight, S., Dickson-Deane, C., Heggart, K., Kitto, K., Cetindamar, D., Maher, D., Narayan, B., & Zarrabi, F. (2023). Generative AI in the Australian Education System: an Open Data Set of Stakeholder Recommendations and Emerging Analysis from a Public Inquiry. Australasian Journal of Educational Technology, 39, 101-124. https://doi.org/10.14742/ajet.8922
Knight, S., Shibani, A., & Vincent, N. (2024). Ethical AI Governance: Mapping a Research Ecosystem. AI and Ethics. https://doi.org/10.1007/s43681-023-00416-z
Lewis, S. (2023). Platforming PISA: The OECD as a Mobile Governance Actor in Global Education. In World Yearbook of Education 2024 (pp. 175-195). https://doi.org/10.4324/9781003359722-14
Lewis, S., Holloway, J., & Lingard, B. (2022). Emergent Developments in the Datafication and Digitalization of Education. In Reimagining Globalization and Education (1 ed., Vol. 1, pp. 62-78). Routledge. https://doi.org/10.4324/9781003207528-5
McKenzie, M., & Gulson, K. (2023). The Incommensurability of Digital and Climate Change Priorities in Schooling: an Infrastructural Analysis and Implications for Education Governance. Research in Education, 117. https://doi.org/10.1177/00345237231208658
Pangrazio, L., & Sefton-Green, J. (2023). Digital Literacies as a ‘Soft Power’ of Educational Governance. In World Yearbook of Education 2024 (pp. 196–211). https://doi.org/10.4324/9781003359722-14
Perrotta, C., Gulson, K. N., Williamson, B., & Witzenberger, K. (2021). Automation, APIs and the Distributed Labour of Platform Pedagogies in Google Classroom. Critical Studies in Education, 62(1), 97-113. https://doi.org/10.1080/17508487.2020.1855597
Sellar, S., & Gulson, K. N. (2021). Becoming Information Centric: the Emergence of New Cognitive Infrastructures in Education Policy. Journal of Education Policy, 36(3), 309-326. https://doi.org/10.1080/02680939.2019.1678766
Swist, T., Buckingham Shum, S., & Gulson, K. (2024). Co-producing AIED Ethics Under Lockdown: an Empirical Study of Deliberative Democracy in Action. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00380-z
Swist, T., Gulson, K.N. (2022). School Choice Algorithms: Data Infrastructures, Automation, and Inequality. Postdigit Sci Educ. https://doi.org/10.1007/s42438-022-00334-z
Swist, T., Gulson, K., & Thompson, G. (2023). Education Prototyping: a Methodological Device for Technical Democracy. Postdigital Science and Education, 6. https://doi.org/10.1007/s42438-023-00426-4
Swist, T., & Gulson, K. N. (2023). Instituting Socio-technical Education Futures: Encounters with/through Technical Democracy, Data Justice, and Imaginaries. Learning, Media and Technology, 48(2), 181-186. https://doi.org/10.1080/17439884.2023.2205225
Swist, T., Gulson, K. N., Benn, C., Kitto, K., Knight, S., & Zhang, V. (2024). A Technical Democracy Design Experiment: Making the UK Exam Algorithm Controversy Game. Design Studies, 91-92, 101245. https://doi.org/https://doi.org/10.1016/j.destud.2024.101245
Swist, T., Humphry, J., & Gulson, K. (2023). Pedagogic Encounters with Algorithmic System Controversies: a Toolkit for Democratising Technology. Learning, Media and Technology, 48, 1-14. https://doi.org/10.1080/17439884.2023.2185255
Thompson, G., Gulson, K. N., Swist, T., & Witzenberger, K. (2023). Responding to Sociotechnical Controversies in education: a Modest Proposal Toward Technical Democracy. Learning, Media and Technology, 48(2), 240-252. https://doi.org/10.1080/17439884.2022.2126495
Thompson, G., Rutkowski, D., & Sellar, S. (2019). Flipping Large-scale Assessments: Bringing Teacher Expertise to the Table. In Andrews, J, Paterson, C, & Netolicky, D M (Eds.), Flip the System Australia: What Matters in Education (1 ed., pp. 55-63). Routledge. https://doi.org/10.4324/9780429429620-9
Williamson, B., Gulson, K., Perrotta, C., & Witzenberger, K. (2022). Amazon and the New Global Connective Architectures of Education Governance. Harvard Educational Review, 92, 231-256. https://doi.org/10.17763/1943-5045-92.2.231
Witzenberger, K., & Gulson, K. N. (2021). Why EdTech is always right: students, data and machines in pre-emptive configurations. Learning, Media and Technology, 46(4), 420-434. https://doi.org/10.1080/17439884.2021.1913181
