Tag Archives: power

IT Futures at Edinburgh

I’m attending the IT Futures conference at Edinburgh today. These notes are not intended to be a comprehensive record of the conference but to highlight points of interest to me and so will be subjective and partial.

A full recoding of the conference will be available at the IT Futures website

The conference opens with an address from the Principal, Sir Timothy O’Shea with an opening perspective:

Points to the strengths of the University in computing research, super-computing and so on, and ‘ludicrously’ strong in e-learning with 60 plus online postgraduate programmes. In these areas, our main competitors are in the US rather than the UK.

Beginning with a history of computing from the 19402 onwards. Points to Smallwood and using computers to self-improving teaching and Papert on computing/ e-learning for self-expression. 1980s/90s digital education was dominated by the OU. 1990s the rise of online collaborative learning was an unexpected development that addressed the criticisms that e-learning (computer assisted learning) lacked interactive/ personalisation elements.

2000 saw the rise of OERs and MOOCs as a form of providing learning structure around OERs. Also noted the success of OLPC in Uruguay as one of the few countries to effectively implement OLPC.

Argues that the expansion of digital education has been pushed by technological change rather than pedagogical innovation. We still refer to the constructivism of Vygotsky while technology innovation has been massive.

How big is a MOOC?
– 100 MOOCs is about the equivalent in study hours of a BA Hons. A MOOC is made up of a 1000 minnows (I think this means small units of learning. MOOCs are good for access as tasters and to test e-learning propositions. They also contribute to the development of other learning initiatives, enhance the institutional reputations including relevance through ‘real-time MOOCs’ such as on the Scottish referendum. MOOCs provide a resource for learning analytics.

So e-learning is mature, not new, and blended learning is ‘the new normal’ and dominated by the leading university brands of MIT, Stanford, etc. A huge contribution of e-learning is access.

A research agenda: to include modelling individual learning, including predictive learning support; speed of feedback; effective visualisation; supporting collaboration; understanding Natural Language; location of the hybrid boundary (eg, in practical tests); personal programming (coding) and how realistic is it for meaningful coding skills for the non-geeks to  be developed.

Open questions are around data integrity and ownership; issues of digital curation; integration of data sources; who owns the analysis; should all researchers be programmers?; and how to implement the concept of the learner as researcher?

Questions:

Question about artificial intelligence: Answer – Tim O’Shea’s initial research interest was in developing programmes that would teach intelligently – self-improving teachers – but using AI was too difficult and switched towards MIT’s focus on self-expression and for programmers to understand what their codes were doing. Still thinks the AI route is too difficult to apply to educational systems.

Q: surprised by an absence of gaming for learning?

A: clearly they can and cites Stanford on influence of games on learning motivation

Q: on academic credit and MOOCs

A: Thinks this is inevitable and points to Arizona State University which is attempting to develop a full degree through MOOCs. Can see inclusion of MOOCs in particular postgraduate programmes – heuristic of about a third of a Masters delivered via (external) MOOCs but more likely to be taken forward by more vocational universities in the UK – but using MIT or Stanford MOOCs replacing staff!.

Now moving on to Susan Halford on ‘Knowing Social Worlds in the Digital Revolution’:

Researches organisational change and work and digital innovation. Has not directly researched changes in academic work but has experienced them through digital innovation. Digital innovation has kick-started a revolution in  research through data volume, tracking, analyse and visualise all sorts of data. So data becomes no longer used to research something but is the object of social research.

Digital traces may tell us lots about how people live, live together, politics, attitudes, etc. Data capturing social activities in real time and over time rather than replying on reporting of activities in interviews, surveys and so. At least, that is the promise and there are a set of challenges to be addressed to realise the potential of these data (also see this paper from Prof Halford).

Three key challenges: definition; methods and interdisciplinarity

Definition–  what are these digital data: these are not naturally occurring and do not provide a telescope to social reality. Digital data is generated through mediation by technology and so is not naturally occurring. In the case of Twitter, a huge amount of data, but is mediated by technological infrastructure that packages the data. The world is, therefore, presented according to the categories of the software – interesting but not naturally-occurring data. Also, social media generate particular behaviours and are not simply mirrors of independent social behaviour – gives the example of the ReTweet.

Also, there is the issue of prominence and ownership of data. Survey data often is transparent in the methods used to generate data and therefore, the limits of the claims that can be made from the data. But social media data is not transparent in how it is generated – the data is privately owned where data categories and data stream construction is not transparent. We know that there is a difference between official and unofficial data. We do not know what Twitter is doing with its data but that it is part of an emerging data economy. So this data is not neutral and is the product of a series of technological and social decision-making that shapes the data. We need to understand the socio-technical infrastructure that created them.

Method – the idea that in big data, the numbers speak for themselves is wrong: numbers are interpreted. The methods we have are not good for analysis of large data. Research tends towards small scale content analysis or large scale social network analysis but neither are particularly effective at understanding the emergence of the social over time – to harness the dynamic nature of the data. A lot of big data research on Twitter is limited to mathematical structures and data mining (and is a-theoretical)  but is weak on the social aspects of social media data.

Built a tool and Southampton to dynamically map data flows through ReTweeting.

Interdisciplinariety: but is a challenge to operationalise inter-disciplinarity.

Disciplines imagine their object of study in (very) different ways and with different forms of cultural capital (what is the knowledge that counts – ontological and epistemological differences). So the development of interdisciplinarity involves changes on both sides – researchers need to understand programming and computer scientists need to understand social theory. But also need to recognise that some areas cannot be reconciled.

Interdisciplinarity leads to questions of power-relations in academia that need to be addressed and challenged for inter-disciplinarity to work.

But this work is exciting and promising as a field in formation. But also rises for responsibilities: ethical responsibilities involved in representing social groups and societies and data analytics; recognising digital data excludes those who are not digitally connected; data alone is inadequate as social change involves politics and power.

Now Sian Bayne is responding to Prof Halford’s talk: welcomes the socio technical perspective taken and points to a recent paper: “The moral character of cryptographic work” as  generating interest across technical and social scientists.

Welcomes the emphasis of interdisciplinarity while recognising the dangers of disciplinary imperialism.

Questions:

What actions can be taken to support interdisciplinarity?

A: share resources and shared commitments are important. Also academic structures are important and refers to the REF structures against people submitting against multiple subjects. (but is is pointed out that joint submissions are possible).

Time for a break ….

 

We’re back with Bernard Schafer of the School of Law talking on the legal issues of automated databases. Partly this is drawn from a PG course on the legal issues of robotics.

The main reference on the regulation of robots is Terminator but this is less worrying than Short Circuit, eg, when the robot reads a book, does it create a copy of it, does the licence allow the mining of the data of the book, etc. See the Qentis hoax. UK is the only country to recognise copyright ownership of automatically generated works/ outputs but this can be problematic for research, can we use this data for research?

If information wants freedom, does current copyright and legal frameworks support and enable research, teaching, innovation, etc? Similar issues arose form the industrial revolution.

Robotics replacing labour – initially labour but now examples of the use of robots in teaching at all levels.

But can we automate the dull part of academic jobs. But this creates some interesting legal questions, ie, in Germany giving a mark is an administrative act similar to a police caution and is subject to judicial review, can a robot undertake an administrative act in this way?

Lots of interesting examples of automatic education and teaching digital services:Screen Shot 2015-12-17 at 12.10.02

 

 

 

 

Good question for copyright law is what does ‘creativity’ mean in a world share with automatons? For example, when does a computer shift from thinking to expressing an idea which is fundamental to copyright law?

Final key question is: “Is our legal system ready for automated generation and re-use of research?”

Now its Peter Murray-Rust on academic publishing and demonstrating text or content mining of chemistry texts.

…And that’s me for the day as I’m being dragged off to other commitments.

Twitter and micro-blogging notes on day 2

These are notes from the Twitter and Micro-blogging conference at Lancaster University for day 2.  The full programme can be found on Lanyard.The Twitter hastag is #LUTwit

Conceptualising Twitter as a discourse system by @mdanganh

Looked at the Function of the # – lead to theory of contextualisation based on John J Gumperz conversational inference and contextualisation cues as surface feature that are verbal and non-verbal. So can be used to understand and analyse #

Cues reconfigure conversational contexts that presuppose and create context as social ordering (Bruns & Burgess 2011).

Key part of Twitter as a discourse system. Identifies four functional operators in Twitter: the RT; the @; the # and the link That have technical and communicative function as well as positioning Twitter as intertextual and interdiscursive

For data drawn from Federal State elections 2010 – 2013 over a four week period each year from parties, media, politicians, public interactions, #. Analysis uses

– profile analysis (quant)

– speech act analysis (qual and quant) (Searle), eg, inform, state, assert, announce, request etc……. Found predominately speech acts concerned with exchanging information, especially from the institutional accounts

– discourse analysis (quant informed qualitative analysis

Use case of Conservative candidate #Rottgen. But lost NRW State election and subsequntly also dismissed as Federal minister by Angela Merkel (as a ‘mother’ figure). Discourse developed as mother metaphor

# frames Tweets in to a story narrative frame that is emergent and the co-construction of meaning.

 

Now on to the plenary session with @GregMyers on Working and Playing on Science Twitter

First Tweet on an April Fools as example of different types of Twitter streams – such as different communities  or genres. @GregMyers on writing on blogging realised that there is not one ‘thing’ of a blog – share a media but are very different. Are we talking about one genre or not? Looking at the different papers at the conference it is clear that there is not a single genre or function.

How do different Twitter communities use Twitter? Are there genre differences. Focus here on science Twitter of research scientists.

Networking is a part of any science project from the 16 century onwards. But as a community, depend for reward on the production of a very different text object, the published paper which is very unlike Twitter. So science community is a network of texts but also involving equipment, people, methods, money (ANT).

Identified two themes of sociology of science:

1. heterogeneirty of scientific networks: ANT. You become powerful in science by maintaining a network

2. rhetorical tension between empiricist repertoire as timeless claims in the formal literature and a contingent repertoire and time bound and contingent activities.

Cites letter from C19 that is very Twitter like albeit as provate letter rather than a public Tweet.

More information on Greg’s blog: http://thelanguageofblogs.typepad.com

Corpus analysis based on keywords eg, paper, scientist, research, etc… but more interesting keywords such as: over use of “i” (compared to other Tweets) as a sign of formality; use of “of” as signifier of more complex; “but” as academic signifier and a negative keyword of “love” as evaluation.

Gives ground to identify scientists as a distinct community on Twitter.

Gives an example of phatic communication – communication for the sake of contact (“who is still working” at 3 am). Problematises the use of the term “here” as “a lab” rather than a geographic co-location. Solidarity building?

Particular interest in references to time: current time – what I’m doing now; temporal cycles of, eg, work , publications, terms; future time (what will be happening); and chunking time eg, pleistocene.

Gives example of scientific criticism and never-ending use of citations and references but also criticism of socio-thermodynamics using LOLcats

Scientific criticism involves personal stance; impersonal references to shared norms and hierarchies of authority for presentational purposes. Found many Tweets involve boundary work, sealing off science and non-science while at the same time concerned with outreach and public engagement with science.

Good set of question of a Twitter community:

  • present self as a community?
  • make a distinct genre – eg, use of RT, links etc…?
  • use the same genre / register?
  • how Twitter practices relate to other practices?
  • what specific kinds of performance are valued?
  • how permeable are the boundaries of the community? How many Tweets get RT from outside the community?

Permeability of the science community enhanced as scientist may be member of other communities that may cross-overs of their specific Tweets (hip hop, feminism/ women in science). But not seeing non-scientists coming back and commenting on scientific discussions.

 

The afternoon session is about to kick-off with Noreen Dunnett on The Tweeting Zone with Twitter providing a mechanism for renegotiating boundaries between Activity Systems. Looking also at how Twitter allows renegotiation of identities and roles of learners and teachers in formal learning spaces.

Referes to liminal spaces as a rite of passage in which a person moves from one state of being to another. Could Twitter affordances at act bridge between Activity Systemas a a boundary zone between different systems and spaces? Does Twitter provide scaffolding between learning and working definitions.

Affordances (actionable properties …. user perceives some action is possible. Gibson 1977, 1979 and Norman 2004). The paper uses Connectivism and Activity Theory examining a teacher training course and the student use of Twitter ordered around a given #

Frames Twitter in terms of a Personal Learning Environment (PLE) as allowing learners to coordinate arrangements between people, materials and technology so the PLE is not a platform but is rather a process that requires agency from the learner [as actor].

Uses ethnographic action research including participant observation, interview and survey. Observation of a Twitter chat over a seven month period with researcher moderating initial discussion. Spaces of learning in, eg, Twitter, enacted in to being – emerge rather that design/ predetermined (Al Mahmood 2008).

Screen shot 2013-04-11 at 13.54.07

Cited example of student who left the course asking for permission to continue to use the hashtag.

Trainee teachers participate in a range of discourse communities simultaneously, spanning formal and informal learning environment. The course tutor conflicted about Twitter and the degree of control and policing role.

Useful Tweet here:

Screen shot 2013-04-11 at 13.45.30

Twitter bridge Activity Systems as discourse communities.

Role of tutor not clear: has emergent and non-emergent elements as the Twitter space was formally set up to support students in placement but tutor also wanted to use it for learning tasks by setting up a series of tasks Tutor was concerned that the students controlled by eg, GTC notions of professional conversation.

References from the presentation can be found here

 

Now out of power and seeking a plug point ……

 

Now at An analysis of professional exchange and community dynamics of Twitter from Nicola Osborne and Clare Llewellyn from Edinburgh

Used Martin Hawksey’s TAGs for grabbing #feeds into Google Docs.

Social media collaboration group came into being to analyse Tweets on London riots using manual analysis and wanted to use more automation in the analysis. Clare developed a prototype as the JISC Twitter Workbench initially for analysis of Olympics on Twitter  but extended in to more general academic use. Currently working on developing the Workbench to work with smaller, discreet data including elimination of direct (unchanged) RTs. Testing Workbench for use at a conference (done aferwards but could be done in real time).

Used algorithm  for LDA clustering but found it no more accurate than incremental clustering 

At the conference, was a lot of interest in, for example, PechaKucha and specific talks that gained a lot of interest.

Found different algorithms appropriate for different size of event/ Twitter hashtags. Clusters confirmed some hunches about the conference.

Noted that clustering does not analyse influence of a Tweet. Confirmed participant feedback on conference sessions. Did identify what was popular (not influential?) and ‘hot topics’ etc which could have been very useful for real time use eg, in back channel. Could imply unpopular sessions not Tweeted but this is not clear.

Very clear decline in volume of Tweets after conference – often sharing links.

Analysis was about the content of Tweets and not about connections between Tweets…

Balance to be developed between clustering duplication versus clustering granularity.

Q of why JISC funded this given the existence of NodeXL

Now time for a break….

Fell behind on the blogging – lack of power, fat fingers etc….

Now at the plenary Professional Twitter Panel which can be followed at #lutwitrc

Discussing finding the time for Twitter and intensity, @johnnyunger very variable in intensity of Tweeting. Mentions that avoiding marking leads to increase in Twitter use.

A number of comments in Tweeting in between times

Screen shot 2013-04-11 at 16.25.01

Tweet when we’re doing other things or when can’t do other things

But also comments about the rhythms of the day – energy, roles etc…

Screen shot 2013-04-11 at 16.28.00

… and in relation to activities in “real life” (sitting on the bus) as well as on other SoMe.

Discussion on whether Twitter is distracting or takes time [but avoids issues of cognitive-shifting]

Moving to ethics, eg, is it OK to be anonymous on Twitter and issue of institutional constraints. Also scales of anonymity, eg, less easy to identify the individual rather than anonymous per se.

Comment on analysis of HE SoMe policies that are very constraining requiring various disclaimers for staff. HE senior management prickliness on potential reputation harm from ‘rogue staff’.

Comment that first rule of the internet that there is no such thing as anonymity – don’t say something online you wouldn’t say elsewhere (from @pennyb).

Moved on to impact – beyond simply number of followers but also who follows.

Discussion on ethics and the nature of public domain with good understanding of the nuances around anonymising Tweets. Also refering to Twitter TOS in tension with research ethics, eg, on anonymity.

divided by – social media?

Nice post here from George Siemens – worth quoting

Does the internet – social media in particular – act as a unifier? Apparently not, according to several researchers. Instead, social media amplifies existing social structures. Or, as danah boyd states, “pervasive social stratification is being reified in a new era”. Technology doesn’t (immediately) alter human nature. It provides new views (mirrors) for seeing what we are. The desire to associate with people who share our beliefs, values, and economic conditions, migrates to new social spaces – digital or physical.

As I’ve said before [here], we shouldn’t think of social media/ web 2.0 as being any different from any other form of social practice and so subject to the type power relations and issues that can be found in any social context, I think Danah Boyd says it a bit more elegently mind.