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]
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.
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.
Games and tools
A key aspect of the EFS is the co-creation of games and tools which can support collective experimentation and learning about socio-technical controversies.
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.
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.
Below is a selection of our collective’s latest academic publications.
Gulson, K., Sellar, S., Taylor Webb, P., Webb, P. (2022). Algorithms of education: How datafication and artificial intelligence shape policy. Minneapolis: University of Minnesota Press.
Greg Thompson, Kalervo N. Gulson, Teresa Swist & Kevin Witzenberger (2022) Responding to sociotechnical controversies in education: a modest proposal toward technical democracy, Learning, Media and Technology, DOI: 10.1080/17439884.2022.2126495
Kalervo N. Gulson & Kevin Witzenberger (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
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
Kevin Witzenberger & Kalervo N. Gulson (2021) Why EdTech is always right: students, data and machines in pre-emptive configurations, Learning, Media and Technology, 46:4, 420-434, DOI: 10.1080/17439884.2021.1913181
Howard, S., 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 & Education. Artificial Intelligence, 3, 100073 1-8.
Sam Sellar & Kalervo N. Gulson (2021) Becoming information centric: the emergence of new cognitive infrastructures in education policy, Journal of Education Policy, 36:3, 309-326, DOI: 10.1080/02680939.2019.1678766
Perrotta, C., Gulson, K., 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.
Gulson, K., 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: Assessments, Big Data, and the Transformation of Schooling, (pp. 90-106). New York: Routledge.
Thompson, Greg, Rutkowski, David, & Seller, Sam (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.Routledge, United Kingdom, pp. 55-63.