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This week, there will be a number of posts about sessions at the 2022 AARE conference, being held at the University of South Australia. They will be emailed to you irregularly across the days of the conference.
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Siemens: the biggest challenges facing education now and ways to meet them
The AARE 2022 conference opens this year with a keynote from George Siemens. Here are some of his thoughts.
We have been hearing about fundamental change in education, often driven by technology, for several decades. Previous theorists, like Illich and Freire, similarly advocated for systemic education change, but their concerns were driven by economics, inclusion, and impact. When “education must change” is now advanced as a narrative, it’s often driven by a motivation to drive use of technology or the outsourcing of some core service of universities or schools. In response to the steady drum beat of calls for change, educators have become somewhat immune and even sceptical. Where is this new reality? Why has covid produced a longing for in-person learning, rather than a great drive for online learning? In our professional lives, the appeal of space and place interactions, while increasingly augmented with online engagement, remains strong.
In this talk, I present three dynamics to consider regarding our future education systems. First, I address the education landscape and the many additional stakeholders now prominently providing some core function. Secondly, I’ll address the conflicting space between data-centric research and complexity-science orientations. Thirdly, I’ll discuss the system of education itself. I believe we are facing a systemic challenge and when looking a decade into the future, it’s apparent that a fundamental change in role and responsibility will unfold for education.
Before I begin, I want to set context for perhaps the most substantive challenge facing education. Trends can be seen as primary or secondary in terms of impact. Secondary impacts include state government mandates and even national level testing and assessment. A secondary trend may change parts of how teaching happens, the content taught, or how students are assessed. Often, the trend has a short timeline and is connected to the interests and motivations of the party in power. Primary trends, in contrast, are those that fundamentally and structurally change the systems of learning and education. In order to keep the system as it currently is, external pressure must be exerted to keep a primary trend from taking over. Unlike the rollout of national testing, which requires mandates to make things happen, a primary trend requires policy and intervention for it to NOT take over.
We’ve seen numerous primary trends over the last decade, including the rise of social media and mobile technology. The primary trend confronting education, however, has a long history, dating back to the 1950’s, and is now beginning a rapid and alarming ascent to prominence in all areas of our lives: artificial intelligence. AI presents humanity with a unique challenge that we have not faced before: an agent with intelligence that rivals our own in a growing range of domains.
Educationally, this presents a significant problem. In 2022, Generative AI has grown in influence and prominence. AI can now generate and create in domains that we have previously seen as exclusively our own: art, literature, and scientific discovery. DALL-E 2 and Stable Diffusion have created art that has won state art competitions. Moonbeam can create writing that has surprising coherence. LaMBDA can carry on conversations that are human-like. After being promised for decades that we would give up routine and mundane tasks to AI while retaining creative activities for ourselves, AI is emerging as an active competitor for our most human skills. Research and scientific discovery is now a pairing of human and artificial cognition. The entanglement that happens at the intersection of the two is spilling over into non-technical domains and sociologists, educators, and psychologists are evaluating how this interplay occurs and how it should be managed and supported.
Education, and all of society, moves forward with the looming AI trend in the background as the overarching development of the current era. The education landscape itself is undergoing significant commercialization and reliance on external stakeholders. Schools and universities are no longer primarily self-contained ecosystems. Instead, the fragmentation of function that defines globalisation has arrived. Online program managers support the development, marketing, and recruiting of students. International programs rely on a global recruitment network. Behind the scenes, consulting firms who had previously mainly addressed the needs of big business and large government now provide services to university and school leaders. Policy papers and guidance documents are produced by every major consulting firm in Australia and the prospect of big economic gains through innovation is a salivating prospect. Big technology is increasingly managing core university computing and security and privacy are now off loaded to these firms. Underpinning all of these transitions is the digital revolution and the data it produces as each student movement and interaction and engagement is logged and recorded.
Digitization produces data and data produces analytics. For researchers, a conflict is unfolding reminiscent of the science wars of the 1990s. Data has won. All research – quantitative, qualitative, mixed – is digital in capture or analysis or publication. To this end, the quantitative side has resolutely and decisively ascended to the throne. The real space of debate now is on how to move data-centric research from focusing on isolated studies to instead begin assessing and evaluating holistic systems. The “science war” emerging is one where the expression of data is the primary concern. Systemic modelling and holistic assessment sits in conflict with NAPLAN and standardised testing. Research conducted is now increasingly focusing on digital spaces or at least spaces that have a digital component: AI predicting how protein folds, sensors capturing remote environmental data, psychologists evaluating the mental health of students in digital settings. A complexity science approach to research moves from granular and limited scope research that occurs in sanitised or limited context settings to including multi-faceted and nuanced contextual data.
When systems change, inefficiencies are created. Organisations and individuals who evaluate and exploit those efficiencies reflect Gould’s punctuated equilibrium (or Kuhn’s paradigm shift): a sudden and significant phase change. This has been experienced in many sectors already, including the move from physical state music and movies to digital, the shift to on demand rather than broadcast media, and the move to networked media rather than centrally controlled. The accrual of inefficiencies – doing the things afforded by previous philosophies and technologies – is confronting education. How should we teach when AI is better at many cognitive tasks than we are? What should we teach when we can find and access the world’s information from our phone?
Looking a decade into the future, international organisations such as OECD see a world where technology is central to learning, where systems of education are dramatically different from what we see today, where AI is a co-learner, where focus on wellness and wellbeing are increasingly important. Educators have long been the end recipients of government initiatives, quasi-scientific pedagogical approaches, and somewhat short-sighted policy changes. The real work of education leadership is the work of systems change. Systems makers – those who create the structures that others work within – needs to be claimed by organisations such as AARE. The future of education is one that will only emerge to serve the broadest range of stakeholders when all participants have the ability to have a voice and to shape the conversation. Finding points of leverage in shaping learning systems through policy, research, funding, and planning landscape is the critical work of today for educational leaders.
Professor George Siemens is the professor and director of the Centre for Change and Complexity in Learning UniSA Education Futures. He researches networks, analytics, and human and artificial cognition in education. He has delivered keynote addresses in more than 40 countries on the influence of technology and media on education, organisations, and society. He has served as PI or Co-PI on grants with funding from NSF, SSHRC (Canada), Intel, Bill & Melinda Gates Foundation, Boeing, and the Soros Foundation. Professor Siemens is a founding President of the Society for Learning Analytics Research. In 2008, he pioneered massive open online courses (sometimes referred to as MOOCs).