Tag Archives: MOOC
These are my notes taken during the presentation and then tidied up a bit in terms of spelling and sense – so they may well be limit, partial and mistaken!
Welcome from Sian Bayne with the drama of the day “fire! Toilets!” and confirmed that the event is being livestreamed and the video is available here.
Lesley Gourlay as chair for the day also welcomed participants from across the UK and Copenhagen. Seeking to provide a forum for a more theorised and critical perspective technology in higher education in the SRHE (Society for Research in Higher Education). Prof Richard Edwards at the School of Education gained funding for international speakers for today’s events. Unfortunately Richard is ill and can’t be here.
The theme of the event is developing the theoretical, ethical, political and social analysis of digital technologies and shift away from an instrumentalist perspective. The event Twitter hashtag is #shre
The first presentation is by Alex Juhasz on distributed online FemTechNet. FemTechNet as a network does not often speak to be field of education so this is a welcome opportunity (she has also blogged on the event here)
FemTechNet is an active network of scholars, technologist and artists interested in technology and feminism. The network is focused on both the history and future of women in technology sectors and practices. FemTechNet is structured through committees and has a deep process-focused approach to its work that is in important in terms of feminist practices. Projects involve the production of a white paper, teaching and teaching practices, workshops, open office hours, co-teaching, etc. models the interaction of theory and practice. But it has been difficult to engage students in collaborative projects while staff/ professors are much more engaged. Town halls are events for collaborative discussion events with an upcoming event on Gamergate to include a teach-in. FemTechNet have also produced a ‘rocking’ manifesto as “feminist academic hacktivism” and “cyberfeminist praxis”.
FemTechNet values are made manifest in Distributed Open Collaborative Courses (DOCCs) themes on Dialogues on Feminism and Technology (2013) and Collaborations in Feminism and Technology (2014). DOCCs against the xMOOC model to promote a participative approach to course design and distributed approaches to collaboration. DOCC was labelled as the Feminist anti-MOOC based on deep feminist principles including wikistorming, and has much press and other interest, some positive and some ‘silly’ (Fox News). FemTechNet has lots of notes on using tools and teaching approaches that can be used across lots of different critical topics beyond feminism alone.
DOCCs are designed to be distributed and with a flatter hierarchy with less of a focus on massiveness. Using technology in an open way to co-create knowledge beyond transmission. More details on the DOCC as a learning commons vs course can be found here.
The FemTechNet commons is now housed and redesigned at the University of Michigan although this may be a way of universities avoiding Title 9 violations. But as a result, the newer commons has become less open and collaborative as an online space.
Much of FemTechNet work involved overcoming technological hurdles and was based on the unpaid work of members. FemTechNet engage with critique of lobour practices and contexts in higher education.
The DOCC networks involve a wide scope of different types of universities from Ivey League and community colleges and community organisations collaborately working.
Student numbers are fairly small with approx 200 students but very high completion rates and very positive feedback and evaluations. Between 2013-4 there was not really growth of scale partly due to limitations of infrastructure. Now with the support of University of Michigan, there is an increased aspiration to develop international collaborative work.
DOCCs involve networking courses from many different fields of study involving both on-campus to fully online courses. Basic components of courses are keynote dialogue videos, smaller keywords dialogues and five shared learning activities. See also the situated knowledge map [link]. There is a big emphasis on share resources, cross-displinarity and inter-institutional working and learning.
So while DOCCs emerged from a feminist network, the tools, models and approaches can be used in many subject areas.
Ben Williamson is prsenting on Calculating Academics: theorising the algorithmic organisationan of the digital university. The open slide isof a conceptualisation of a digital university university that can react to data and information that it receives. Ben will be prsenting on a shift t understanding of the university as mediated by the digital and focus on the role of algorithms.
One of the major terms being used is in terms of the smart university based on big data to enhance teaching, engagement, research, enterprise to optimise and utilise the data universities generate. This turn is situation in the wider concept of ‘smart cities’.
Smart cities are ‘fabricated spaces’ that are imaginary and unrealised and perhaps unrealisable. Fabricated spaces serve as models to aspire to realise.
Smart universities are fabricated through
technical devicecs, softre, code,
social actors including software producers, government and
discourses of text and materials.
Algorithm is seen in compsci as a set of processes to produce a desired output. But algorithms are black boxed hidden in IP and impenetrable code. It is also hidden in wider heterogeneous systems involving languages, regulation and law, standards etc.
Also algorithms emerge and change overtime and are, to an extent, out of comtrol, and are complex and emergent.
Socio-algorithmic relationality as algorithms co-constitute social practice (Bucher 2012); generate patterns, order and coordination (mackenzie 2006) and are social products of specific political, social and cultureal contexts and go on to produce by temselves.
Involve translation of human action through mathematical logics (Neyland 2014). Gillespie (2014) argues for a sociological analysis of algorithms as social, poitical as well as technical accomplishments.
Algorithms offer (Gillespie 2014): technical solutions; as synedoche – an abbreviation for a much wider socio-technical system; as stand-in for something else around corporate ownership for example; commitment to procedure as they privilige qualitification and proceduralisation.
Big data and the smart university is a problem area in this idea of the smart university. Is there a different epistemology for big data. Big data cannot exist without algorithms and has generated a number of discourses. Wired mag has suggested that big data is leading to the end of theory as there is no need to create a hypothesis as big data will locate patterns and results and this is a challenge to traditional academic practice. Also there is the rise of commercial social science such as the Facebook social science team often linked to nudging behaviours and “engineering the public” (Tufecki 2014). This is replicated in policy development such as the centre for analysis of social media at Demos using new big data sets. We’re also seeing new academic initiatives such as social physics at MIT and building a predictive model of human behaviour. Also see MIT laboratory for social machines in partnership with Twitter.
This raises the question of what expertise is being harnessed for smarter universities. Points ot the rise of alternative centres of expertise that can conduct big data analysis that are labelled as algorithmist Mayer0Schonberger and Cukier. Such skills and interdisciplinarity does not fit well in university. Sees the rise of non-sociologist sociologists doing better social research?
Mayer0Schonberger and Cukier Learning with Big data – predictive learning analytics, new learning platforms, et.\c. that is reflected in the discourses on the smarter university. Bid data generates the university in immediate and real time- doesn’t have to wait for assessment returns. See for example, IBM education for a smarter planet focused on smarter and prescriptive analytics based on big data.
Knewton talks of inferred student data that suggests the algorithm is objective and consistent. But as Seaver (2014) points out, these algorithms are created and changed through ‘human hands’.
So we’re seeing a big data epistemology that uses statistics that explain and predict human behaviour (Kitchin 2014): algorithms can find patterns where science cannot that you don’t need subject knowledge to understand the data. But he goes on that this is based on fallacies of big data- big data is partial, based on samples, what analysis is selected, what data is or can be captured. Jurgenson (2014) also argues for the understanding of the wider socio-economic networks that create the algorithms – the capture of data points is governed by political choices.
How assumptions of bid=g data are influenceing academic research practices. Increasingly algor entwinned in knowledge production when working with data – sch as Nvivo, SPSS, google scholar – Beer 2012 – algorthimic creation of social knowledge.Also seeing the emergence of digital social research around big data and social media. eg social software studies initiative – soc sci increasingy dep on digital infrrastructure not of our making.
Noortje Marres rethink social research as distributed and share accomplishment involving human and non-human.
In turn influences academic self-assessment and identity through snowball metrics on citation scores, researchfish etc. translating academic work in to metrics. See Eysenback (2011) study linking Tweets and rates of citation. So academics are subject to increasing quantified control mediated through software and algorithms. Seeing the emergence of the quantified academi self. Yet academics are socialised and by these social media networks that exacerbtes this e-surviellance (Lupton 2014). While share research develops its own lib=vely social life outside of the originator’s control.
Hall (2013) points to new epistemic environment that academics are being more social (media) entrepreneurial. Lyotard (1979) points to the importance and constraints of computerisation of research.
Finish with Q
– how do cog based classrooms learn?
– what data is collected to teach?
– should academics learn to code?
A lot of discssion on the last question. It was also pointed out that its not asked should coders learn to be sociologists?
Also pointed out that people demonstrate the importanc of embodoed experiences through protests, demonstrations, that reflects what is loss in the turn to data.
After a short break, we now have Norm Friesen on Education Technology or Education as always-already Technological”. Talking about educational technology as not new but as going through a series of entwinements over time. Norm will look at older technologies of the text book and the lecture as looking back at older recognisable forms.
Looking back we can argue that educational technologies now are not presenting particularly novel problems for higher education. Rather higher education has always been constituative with educational practices then we can see how practices can adapt to newer technologies now.
Tech in education have always been about inscription, symbols as well as performance. If we understand the university as a discourse networks – see Kipler’s discourse network in analysis of publishing in 19 Century. Institutions like universities are closely linked to technology in storing and using technologies and modifying technologies for their practices.
In the example of tablets going back to ancient times or the horn book or other forms that is tigtly couple with institutions of learning and education. Such as clay tablets dating back to 2500 – 2000 BCE that show student work and teacher corrects as symbolic inscriptions of teaching and learning practices. And such tablets work at the scale of individual student work or as larger epic literatures. Can see a continued institution symbolic practices through to the iPad. Here technologies may include epistemic technologies such as knowledge of multiplication tables, procedures of a lecture – technologies as a means ot an end – so technologies are ‘cultural techniques’.
For the rest of the presentation will focus on the textbook and lecture as technologies that are particularly under attack in the revisioning of the university. Ideas of the fipped classroom still priviliges the lecture through video capture. Similarly the text book has yet to be overtaken by the e-textbook. Both provide continuities fromover 800 years of practice and performance.
The lecture goes back to the earliest university as originally to recite a text, so for transmission rather than generation of knowledge with a focus on the retention of knowledge. Developing ones own ideas in a lecture was unknown and student work involved extensive note taking from oral teaching (see Blair 2008). The lecture is about textual reproduction. Even following the printing press, this lecture practice continued although slowly, the lecturers own commentary on the text was introduced manifested as interlines between lines written from the dictated text. Educational practice tended to not change as rapidly as the technologies of printing such that education was about 100 years behind.
But in 1800 saw the first lectures only from the lecturers own notes. so the lecture was recast around the individual as the creator of knowledge. So the individual lecturer and student not the official text became the authoritative sources of knowledge. Also the notion of the performance becomes increasingly important in the procedure of the lecture.
In textbooks we see pedagogical practice embedded in the text as end of chapter questions for the student to reflect and respond to (the Pestalozzian method, 1863). This approach can be seen in Vygotsky, Mead and self-regulated learning.
Specific technological configurations supported the increased emphasis on performance such as podcasting, powerPoint, projectors, etc. (see TED talks).
In the text book, similar innovations are happening in term sof layout, multimedia, personalised questioning (using algorithms). The text book becomes an interactional experience but continue from much older forms of the textbook. What is central is what are the familiar forms – that underlying structures have persisted.
But it is also the case that lectures nolonger espouse their own theories, they do not create new knowedge in the lecture.
I am currently trying to catch up on the Coursera MOOC on social network analysis . My main aim in taking the course is to force myself to learn about using Gephi for network analysis. The course so far has been clear and well presented but its early stages. Also, using Gephi on the Mavericks version of OSX has been a pain largely due to Java as Gephi won’t run on the default install of Java. The solution can be found on the Gelphi forums here although I’m still having some problems with Java.
I don’t use Facebook much and was a bit surprised at the density of the network as a whole but having that number of sub-clusters was less surprising considering the stop-start nature of how the network developed. I’ll have to find out who the single unconnected nodes are once the Java issues have been resolved.
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
We’ve now moved to the parallel sessions and I’ll be taking short notes on these.
We’re starting with Lynne Booth, Michelle Blackburn and Simon Warwick (Sheffield Hallam) on the effective use and assessment of web-based collaborative learning. The learning intervention was in the context of a UG programme on business and human resources and the development of a first year course. Also positioned in the context of a institutional emphasis on employability and the student experience to develop tangible evidence of the HR knowledge of students. So the course was based on the production of HR websites by the student groups using Google Sites so outside the VLE but able to use Sites templates.
While group work was popular but not for assessment due to social loafing (and emphasised by employers) so generated a way of differential marking. Groups were self-selected by should be mixed gender and mixed culture with an aim for authentic learning to enhance employability.
The websites were tone developed as a response to a development need identified by a fictional HR manager by email.
Students also had to create a academic reflection on the sites produced.
Students tended not to react to formative feedback but did appear to have a positive effect on placement rates but was difficult to mark. Little evidence of social loafing.
There remains an issue on scalability and support from learning technologists for staff and students. Also, all other modules use more traditional assessments.
Placing the Transfer of Learning at the Heart of HRD Practice with Vivienne Griggs, Dianne McLaren, Barbara Nixon, Joanna Smith of Leeds Met but the research involves both Synaptic Change Ltd and Connecting Housing on testing a transfer of learning model. The research on the model is framed by issues of alignment between L&D and business strategy as well as the use of big data in evaluation of L&D. The model was based on the importance of the line manager in L&D in embedding training and development outcomes in BAU. Also sought to embed evaluation as a process in L&D interventions.
A key focus was to define success criteria for the individual, the trainer, the line managers and the organisation which can be a challenge.
The training was on having difficult conversations which was followed by stakeholder focus groups and interviews on how effective the model was seen for transferring and embedding training and learning. A stakeholder approach including the trainees in defining success criteria was important in the overall success of the intervention. However, there was little preparation by line managers for the transfer of training. Line managers seemed disengaged from the use of the training but peer support was effective. A trainee comments that they would want their manager involved!
There is an issue of scaling the process to larger organisations. Further development to involve sustainability of impacts and supporting peer-support.
Return on Investment: Contrary to Popular Belief, MOOC’s Are Not Free with Marie A. Valentin, Fred Nafukho, Celestino Valentin Jr., Detra Johnson, John LeCounte (Texas A&M) started with a introduction to MOOCs and the research questions on the true costs of MOOCs and the direct and indirect ROE based on Human Capital Theory. The research seemed to be
MOOC providers making revenue from credits; certification; but also recruitment services pay a fee for data on users, text book sales by linking the course to the text book; selling data to third parties and claims for HEI recruitment to mainstream programmes. This was a work-in-progress and was very much orientated to xMOOCs and the VC-backed US MOOC providers.
Donald Clark has given ten reasons why MOOCs flip higher education. While he makes some valid points, the post itself is overly influenced by the hype surrounding MOOCs and does not really provide a justification for how MOOCs address the problems he identifies in HE (deficiencies in pedagogy, some poor teaching and high costs). I particular: flip 2 suggests almost that MOOCs have been imposed on HE from outside rather than developed by HE; flip 4 from teaching to learning has been going on for a long time and certainly is embedded in the higher quality online (and face-to-face) programmes; flip 5 on assessments is pure conjecture as there are no actual MOOCs I’m aware of that provide for recognised credits (as in part of a national qualifications framework); flip 8 on criticism, yes some criticism is ridiculous but the credibility of MOOCs as learning has yet to be established, but the main criticism of monetisation is important (at least for the platform providers and VCs) and its hard to see how ROI can be established. Its the same for HEs but HEs may be involved for reasons other than as money-making opportunities (reputation enhancement, experimentation and innovation)
Fundamentally, the assumption that MOOCs have succeeded (and succeeded in what?) is not clear to me. Having said that, there are lots of good points here on online learning being as good or better than face-to-face, on the potential to drive greater responsiveness to demand for HE; being more learner-centric (and that’s before looking at a feudal timetable of HE and the balance of teaching vs research in reward and recognition in the sector. All good and interesting stuff but lets engage with what we know about MOOCs rather than what we’d which about them.
My talk was followed by Amy Woodgate talking about the University of Edinburgh‘s experience with MOOCs. There is a detailed report on the University’s first round of MOOCs available here. What surprised me, was the extent of the treatment of MOOCs as Open Education Resources and the positive way the University was supporting other universities in using MOOC content for their own degree programme, other organisations in using MOOC resources for workplace learning and even schools using the MOOCs for classroom teaching. All in all, an inspiring talk and discussion.