MOOCs automation, artificial intelligence and educational agents

Geoge Veletsianos is speaking at a seminar hosted by DiCE research group at University of Edinburgh. The hastag for the event is #edindice and the subject is MOOCs, automation and artificial intelligence.

[These notes were taken live and I’ve retained the typos, poor syntax and grammer etc… some may call that ‘authentic’!]
George began by stating that this is an opportune time for the discussion as MOOCs in the media, the developments on the Turing Test and MIT media lab story telling bots used for second language skills in early years or google’s self-driving cars. Bringing together notion of AI, intelligent being ets.
Three main topics: (1) MOOCs as sociocultural phenomenon; (2) autonomation of teaching and (3) pedagogical agents and the automation of teaching.

MOOCs: first experienced these in 2011 and Change11 as a facilitator and uses them as object of study for his PG teaching and in research. Mainly participated as observor/ drop out.

MOOCs may be a understood as courses of learning but also sociocultural phenomena in response to the perceived failure of higher education. In particular, MOOCs can be seen as a response to the rising costs of higher education in North America and as a symptom of the vocationalisation of higher education. Worplace training drives much of the discussion on MOOCs as illustrated by Udacity changing from higher ed to training provider and introducing the notion of the nano-degree linked to employability. Also changes in the political landscape and cuts to state funding of HEIs in the USA and the discourse of public sector ineffieciencies and solutions based on competition and diversity of provision being prefered. MOOCs also represent the idea of technology as a solution to issues in education such as cost, student engagaement  and MOOCs as indicative of scholarly failure. Disciplines and knowledge of education such as learning sciences not available many as knowledge locked-in to costly journals, couched in obscure language. MOOCs also represent the idea that education can be packaged and automated at scale. Technologies seen as solutions ot providing education at scale, including TV, radio and recording lectures etc. so education is seen as content delivery. 
Also highlighted that xMOOCs came out of comp sci rather than education schools and driven by rubics of efficiency and autonomation. 
Pressey 1933 called for an industrial revoluation of education through the use of teaching machines that provide information, allow the learner to respond and provide feedback on that learner response. B.F. Skinner also created a teaching machine in 1935 based on stimulous/ response of lights indicating whether a response is correct or not. 
Similarly MOOCs adopt similar discourses on machine learning around liberating teachers from administration and grading to be able to spend more time teaching. So these arguments are part of a developed narrative of efficiency in education.But others have warned against the trend towards commodification of education (Noble 1988) but this commodification can be seen in the adoption of LMS and “shovelware” (information masquarading as a course).
Automation is increasing encrouching in to academia via eg, reference management software, Google scholar alerts, TOC alerts from journals, social media automation, RSS feeds, content aggregators (Feedly, Netvibes) and programming of the web through, for example, If This Then That (IFTTT). 
As a case, looks at the Mechanical MOOC that are based on assumptions that high quality open learning resources can be assembled, that learners can automatically come together to learn and can be assessed without human involvement and so the MOOC can be automated. An email schedular coordinates the learning, OpenStudy is used for peer support and interactive coding is automatically assessed through CodeAcademy. So attracts strongly and self-directed and capable learners. But research incates the place and visibility of teachers remains important (Ross & Bayne 2014). 
Moving on to educational agents as avatars that present and possibly respond to learners. These tend to be similar to virtual assistants. Such agents assist in learning, motivation, engagement, play and fun but the evidence to support these claims is ambiguous and often “strange”. In the research, gender, race, design and functions all interact and learners respond often based on the stereotypes used in human interactions. The most appealing agent tending to have a more positive effect on learning. Also context mediates perceptions and so how pedagogical agents are perceived and understood. 
The relationship between the agents and learners and their interactions is the subject of a number of studies on topics of discussion and social practices. Found that students and agents engage in small-talk and playfulness even though they are aware they are interacting with an arteficial agent. Also saw aggressive interactions from the learners, especially if the expert-agent is unable to answer a query. Students also shared personal information with the agents. Agents were positioned in to different roles as a learner companion, as a mediator between academic staff and learner, as a partner.
So social and psychological issues are as important as technology design issues. So do we need a Turing test for MOOC instruction? How we design technologies reflect as well as shape our cultures. 
//Ends with Q&A discussion

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