Conclusion

Our manifesto offers a challenge to what we see as some of the dominant and negative trajectories in digital education practices, politics, and technologies. Much of this book has been framed around the need to debunk, rethink, challenge, and stop “going further in the same way as before” (Latour 2010, 473). We have used the manifesto as a way of pushing against perspectives and assumptions that we see as undercritiqued, underpracticed, undertheorized, or underresearched. However, the manifesto is also intended to work positively as a statement of hope for a near future for education that is responsible, intellectually ambitious, critical, and creative. It is this spirit that drives the manifesto and that we have also tried to expand in this book.

The field is fast changing. When we reworked the 2011 manifesto in 2016, we kept several of the points and preoccupations presented in the earlier version—for example, around assessment, contact, multimodality, openness, and surveillance. But there were also many new points that aimed to address what we then saw as some of the key challenges and issues facing us as teachers: instrumentalist lockdown, neglect of materiality, at-scale teaching, algorithms, and automation. Looking ahead to the planned 2021 edition, we see the need to include much that is again new. We therefore use this conclusion to outline the trajectories that are likely to shape the next manifesto, looking ahead to advocate in new ways for how we, as teachers, we might work to shape a desirable near future for our teaching.

Climate Crisis

The next manifesto needs to address the implications for teaching about the climate crisis. What would our teaching, our colleges and universities, look like if we were to prioritize climate issues across all our work? One aspect of this relates to the greater focus on materiality we have argued for throughout this book: if we use digital technologies in our teaching, we need to pay attention not only to their social effects but to how they are made and the implications of their materiality. Vague statements around, for example, “clean tech” and “cloud-based” learning technologies will need to give way to important discussion about the massive ecological impact of the energy use, extraction, and waste disposal that underpin our everyday technology use.

In their assessment of future global emissions from the information and communication technologies (ICT) industry to 2040, Belkhir and Elmeligi (2018) found that the relative contribution of ICT to total global greenhouse emissions is expected to grow from about 1 percent in 2007 to 14 percent by 2040—more than half the contribution of the entire global transportation sector. Emissions from smartphone production are particularly concerning: the energy used for the manufacture and the gold and rare-earth elements needed to make them, the phone plans that encourage early replacement and obsolescence, the waste. But it is the server farms and data centers that power our devices and the software that drives them that make up the lion’s share of ICT industry climate impact. According to Belhkir and Elmeligi, these infrastructural elements will contribute 45 percent of the overall ICT footprint by 2020 (459).

Beyond the infrastructural and industry aspects of the climate crisis, there is a need for a shared rethinking of education on the part of practitioners and leaders—a shift into a cultural mode focused on justice, planetary health, and sustainability. The ecoversities alliance, for example, is a translocal gathering of people from India, Mexico, Portugal, Canada, Zimbabwe, and elsewhere focused on asking, “What might the university look like if it were at the service of our diverse ecologies, cultures, economies, spiritualities and Life within our planetary home?” (ecoversities, n.d.)—a reimagining that takes account not only of climate issues but of social justice, radical pedagogy, cultural diversity, and decolonization. Similar ideas, differently framed but more specific to digital education, find their way into other gatherings, such as the Digital Pedagogy Lab with its focus on critical and liberatory pedagogies (Digital Pedagogy Lab, n.d.).

Datafication

The datafication of society extends to all aspects of daily life and has implications for education across all sectors: the data trails we generate in our everyday lives; the surveillance and performance measurement regimes that feed on these; data-driven decision-making by institutions, governments, and corporations; and the concentration of influence in particular algorithms and platforms. We have included some discussion of these in this book, particularly in parts III and V, but we see this issue as having a much more central focus for a future manifesto.

Datafication in higher education converges with a range of social factors running in parallel with technological change—for example, unbundling and privatization (discussed below), changing patterns of engagement and recruitment at the global scale, the normalization of surveillance, the massification of higher education and its effects on staff workload and academic precarity, populism and the public perception of the value of universities. As Williamson (2019a) has argued, political imperatives to make universities instruments of social change, accompanied by the embedding of market values within higher education, mean that universities’ data infrastructures are increasingly politicized, with practice becoming driven by performance metrics and strongly datafied evaluations of teaching and research quality.

In part V, we touched on the ethics and politics of the routine surveillance of students. With sensor- and device-based tracking of individuals technically possible and discussions around the “smart campus” becoming commonplace in universities, we teachers need to continue to develop ways of articulating a values-based position on student tracking, attendance, and engagement monitoring. This should be a position that recognizes where there are benefits as well as dangers. Predictive uses of data are already used in universities—for example, the combination of application and progression data sets to predict patterns of admission and withdrawal and analytics designed to identify students at risk of failure (see Dawson et al. 2017); however, the risks of predictive analytics and algorithmic discrimination are well documented, in education and elsewhere (O’Neil 2016). We need to keep a strong focus on the recoding of education discussed in part III, one that holds this intensification of data-driven planning and decision-making to account.

Unbundling

“Unbundling” refers to the disaggregation of higher education into its component parts (for example, the separation of teaching from research; the outsourcing of student support and assessment; the breaking down of academic work into para-academic service roles). Fueled by the for-profit sector and happening as the expansion of higher education drives up the cost for governments and individuals, proponents of unbundling see in it a positive disruption that will make higher education more market driven and ultimately more affordable, with a greater focus on employability, flexibility, and personalization. MOOCs and the various new credit models emerging from these as they evolve are one example of unbundling, as are the promotion of fast-track degrees and more modular, flexible, professionally oriented provision (Wicklow 2017).

Criticism of unbundling focuses on its reduction of higher education to a service industry for employers, the undermining of the ideal of higher education for the public good, and its division of teaching from research. This division, it has been argued, pushes universities into impoverished, transmission-based teaching models isolated from the research leading edge (McCowan 2017). Important work from a project on the unbundled university in South Africa (Swinnerton et al. 2018) shows how unbundling works to reinforce existing power asymmetries, at the same time as it foregrounds fundamental discussions about the purpose and future of universities (Swartz et al. 2018).

Unbundling, taken to its logical end point, would indeed mean the end of universities as we know them: coherent, valued communities of scholarship in which research and teaching are supported in the interest of a social, inclusive vision that extends beyond the imperatives of the market. Unbundling very much needs its own call to attention.

Artificial Intelligence

To a very large extent linked to datafication, advancements in artificial intelligence (AI) and automation have profound and growing implications for education. Our reference to robot colleagues in the 2016 manifesto (discussed in part III) was intended to be a playful way of indicating the need to remain open to the opportunities presented by AI in education while maintaining a critical perspective on it. However, as the AIEd industry grows—recent published reports describe an annual growth of 38 percent and a US$2 billion market by 2023 (Reuters 2019)—and the hype around personalized learning and AI tutors persists, we will need increasing nuance in the way we talk about the implications of this for teachers and teaching.

If AI has the potential to work well alongside human teachers (for example, by offering us new ways to analyze and direct student discussions in group forums or developing responsible new ways of building curriculum), it also eases us into an acceptance of automation highly problematic in a sector where precarious employment is rife. The path to automation is made easy where teacher professionalism is not valued and teaching itself has been reduced to low-grade, routinized work.

Neurotechnology

Neuroscience, and the insights it offers for the embodied and physiological dimensions of cognition, are likely to have a new impact on teaching and on the project of education itself in the coming years. Neurotechnology is another, and even more intimate, manifestation of the datafication discussed previously, defined by Williamson (2019b) as

a broad field of brain-centred research and development dedicated to opening up the brain to computational analysis, modification, simulation and control. It includes advanced neural imaging systems for real-time brain monitoring; brain-inspired “neural networks” and bio-mimetic “cognitive computing”; synthetic neurobiology; brain-computer interfaces and wearable neuroheadsets; brain simulation platforms; neurostimulator systems; personal neuroinformatics; and other forms of brain-machine integration. (65)

This is another rapidly growing industry, with profound implications for teaching and learning, yet it remains underdiscussed among teachers, often perceived as only marginally relevant. Of the many forms of neurotechnology that Williamson indicated, some are focused on enhancement. Transcranial direct current simulation, for example, is a cheap, low-tech procedure that involves sending a low electric current to the brain with apparently positive effects on memory and learning (Au et al. 2016). Others are more oriented to neurosensing and monitoring, such as the Harvard spin-out BrainCo, which has “developed a headband that reports real-time brainwave data to a teacher’s dashboard to indicate levels of attention and engagement” (Williamson 2019b, 76). This kind of real-time monitoring opens up other possibilities for mining the mind, “not only to infer mental preferences, but also to prime, imprint or trigger those preferences” (Ienca and Adorno 2017, quoted in Williamson 2019b).

Educational neurotechnologies are surrounded by transhumanist “enhancement” imaginaries that are too easy to dismiss as sci-fi. They are real, promising to render individual, personal learning data more intrusive and potentially problematic than anything currently discussed in the mainstream literature on learning analytics and educational data. This is an issue for teaching practice that we need to surface and discuss.

Finally

The manifesto has always benefited from the insights, comments, and criticisms it has received from scholars all over the world on social media or in the many workshops, talks, and seminars we have given. We hope that this will continue and open up an invitation to everyone who has a stake in digital education—in whatever form—to engage with us as we shape the next version. We see this as collective work that extends well beyond the team in Edinburgh. We thank everyone who has contributed to its development so far and invite further comments, proposals, and provocations as we work on the next one. Digital education is, as John Urry has described futures research, “a murky world, but it is one that we have to enter, interrogate and hopefully re-shape” (Urry 2016, 192). Please join us as we continue to build The Manifesto for Teaching Online in this spirit.