Dealing with the email horror at the end of a holiday

Before going on holiday, I’ve conducted a few sessions coaching people on self-management/ productivity and so I thought I’d write a few posts on some key aspects of how I deal with some of the major pinch-points people seem to face. As I’m back from holiday today, I thought I’d start with dealing with the email backlog.

Despite all the prescriptive advice on productivity, what works is what works for you: you need to think about what might work for you and test it over a few weeks at least. This is the process that works well for me.

So, deep breath, softly swear to yourself and start …

Firstly, check you calendar for the next two weeks or so. This is to remind yourself of key deadlines, meets, etc coming up. I know ideally you will have all your priorities sorted and scheduled before any holiday but realistically its generally a struggle to get everything wrapped up before you go, let alone plan for your return. So I check my calendar for at least the next two weeks and keep a written summary of what’s coming up next to me for the rest of the process. Give yourself a time frame of 90 minutes – you’re aiming to be quick!

  1. check any high priority emails (ignore those from people who mark all their emails as high priority). These should be processed now by dealing with those that can be responded to in 2 minutes or less. The others should be either put in to a folder to be actioned today or left in your in-box and treated like any other email – in other words, they are not really high priority for you.
  2. sort your in-box by either ‘from’ or ‘subject’ (you’ll know which makes better sense for you) to conduct a quick scan of any emails relevant to you upcoming appointments and priorities. For those that look relevant, either deal with in under two minutes, or place in the folder to be actioned today or in the backlog folder. I also delete anything obviously spammy that’s made it through my filters. It’s important to do this at speed and don’t be distracted by ‘interesting’ but irrelevant content. At the end of this stage, you should be comfortable that you’re not missing emails of urgent importance to you.
  3. I now tend to give my, much depleted, email in-box another scan incase I’ve missed anything.
  4. All remaining emails in the in-box, except those sent in the last 48 hours, now get moved to the backlog folder.
  5. I process the most recent emails in the normal way – action now, onto may task list for later, delegate, delete of file – to in-box zero.
  6. I schedule some time each day over the next ten days to process the backlog of less urgent emails.
  7. I can now work on those urgent and important emails that need to be actioned today *and* finish the day with nothing in my email in-box!

Again, this works for me but they key aspect is to prioritise to my needs and to get to a point where I’ve no sense of ’email overwhelm’ quickly.

Following this process, it took me one hour to go from 484 emails to Zero.

Creating Living Knowledge: the Connected Communities programme and what it tells us about university-community partnership

These are my notes from a Digital Education Seminar at the University of Edinburgh by Professor Keri Facer on the Connected Communities research programme.

As ever with these posts, my record is partial and bias and possibly includes some inaccuracies (but not on purpose). 

The seminar was opened by Prof Sian Bayne to introduce Keri as Professor of Educational and Social Futures at Bristol University and was previously Research Director at FutureLab. Her research takes a critical stance on digital education and on the role of educational institutions in society. Today she’ll be talking about her work onConnected Communities and the newly released Creating Living Knowledge report on lessons learnt from the Connected Communities programme.
Keri Facer:
The main questions that will be explored today include: what is Connected Communities and what is shaping university-community partnership, what they are creating and the implications for the future trajectories of universities and their interface with their communities?
CC is a research council UK programme led by AHRC and currently funds 324 different projects. Projects range from 6 months to five years and involving working with external organisations from creative economy, environment, health and well-being etc…
The bigger picture of the programme is to address question of how university and community knowledge be combined to generate better research. Underpinned by the assumption that co-produced research is a ‘good thing’. The RCs are making huge claims on the potential for the co-production mode of research in terms of research quality and impact while others are concerned that this agenda is concerned with the instrumentalisation and marketisation of research.
CC enters a massively uneven playing field between large institutions through to voluntary community activists, freelancers, community organisations, etc. The HE sector is also very diversified between research/ teaching intensive interacting with socio-cultural diversity. Also, CC works with a wide diversity of motivation for engaging with research: generalists and learners engaged by interdisciplinary research; makers wanting to make something happen; scholars with a particular topic orientation; entrepreneurs interested in funding available; accidental wanderers caught up in projects; advocates for new knowledge landscape arguing for a rethink of how knowledge is generated.
The are also different research traditions in:
– participatory, collaborative, community engaged research developing grass-roots capacity
– development traditions – changing policy
– people’s history, feminist and civil rights interested in alternative narratives of history
– innovative co-design changing services and products
– open/ crowd and open innovation creating something new
– participatory arts where unsettling and exploration is the purpose.
These different traditions mobilise different performances of community and ‘publicness’. Also involving different participants and audiences and different working practices. Again, these shape the landscape of collab
Social networks and funding. Raises questions of access to social networks and how and where conversations happen. Over 50% of partners had already worked inside universities. So other possible partners face a barrier to entry to these collaborative opportunities while intensive workshops can discriminate with caring responsibilities.
So the injunction to co-produced research can reproduce and intensify existing inequalities.
Important to acknowledge that the cultures of universities can be very diverse and not only a culture of critique, e.g., engineers want to make stuff
Different groups want different things from one another: from practical help, personal value and friendships and symbolic benefits e.g., of offering authenticity and credibility and status. Everyone has to negotiate the ‘fantasy’ of the university and the community. Beyond the quick gains between partners leads to difficult questions around, e.g., the legitimacy of knowledge production or the representativeness of community groups.
Different modes of collaboration emerge:
  1. division of labour – keep to our own silos
  2. relational expertise – can we see the issue through each others eyes
  3. remake identities – about learning each others skills and knowledge so we can take on each others’ roles.
  4. colonisation – unsettled identities but no learning. Academics attempting community work or community groups attempting research data collection.
Where works well, collaboration leads to the breakdown of division and new roles are mobilised such as catalysers; integrators; designers; broker; facilitator; project managers; data gatherers; diplomats (makes things work in and between institutions); accountants; conscience; nurturer; loudhailer.
This requires time to develop trust; understand each others’ expertise, etc. so that these projects can do a different sort of work where  “The adventure of thought meets the adventure of action” (A.N Whitehead)
While their is strong legacy from these collaborations, this legacy is precarious due to key staff being junior staff and in precarious employment. This is linked to the funding environment. Short-term project funding can disturb the work of small organisations as well as disturb personal relationships. Also, the funding requires working with HEI systems that are not fit for working with smaller and precarious partner organisations. These negative effects are exacerbated by trends in HE towards marketisation
We cannot state whether such projects will democratise knowledge production as that depends on many other variables. Similarly, the idea that co-production leads to better research – well, its another set of methods but collaboration can, if done mindfully, lead to better quality research in terms of needs of all of those involved.
Recommendations from the research (from the report):
  1. improve the infrastructure
  2. recognise the need for time for collaboration
  3. explicitly address the risk of enhancing inequalities
  4. invest in and support civic society’s public learning infrastructure.

PhD Abstract: Twitter chat events & the making of a professional domain

Here is the latest draft of a one page abstract of my PhD:

Distributed online discussion events in social media are increasingly used as sites for open, informal professional development, knowledge sharing and community formation. Synchronous chat events hosted on Twitter have become particularly prominent in a number of professional domains. Yet theoretical and critical analysis of these Twitter chat events has, to date, been limited: this thesis contributes to the development of such analysis through a socio-material, network assemblage lens employing trans-disciplinary and multi-method research approaches. This research positions the Twitter chat events as the relational effects of network-assemblages of human and non-human actants.A picture of various draft word processed documents

This thesis explores Twitter chat events with a particular focus on human resource development (HRD) as a professional domain that is widely seen as inherently changeable, fluid, contested and continually emergent. This study examines how practitioner-generated reportage of professional practice and the specific functions of Twitter intra-act to generate a particular definition of HRD as a professional field of practice.

A combination of descriptive statistics, Social Network Analysis and analysis of the content and structure of the Chat events has been employed in researching 32 separate chat events with 12,061 tweets. The research methods generated multiple readings of the research data and surfaced different and fluid potential lines of enquiry in to the Twitter chat events. A number of these potential lines of enquiry were then selected as points of entry to ‘zoom in’ to the data using a critical discourse analysis for a smaller sample of the Chat events.

A key finding of the research is that the Twitter chat events seek to generate an idealised archetype of HRD bounded by a stable set of dominant practices. This idealised archetype is positioned in contrast to a repertoire of common HRD practices presented as illegitimate in this professional grouping. A second key finding relates to the chat event assemblages as collective achievements involving human and non-human actants. The collective effects surfaced in the research problematise (a) the notion of online communities as the product of network ties and (b) the individualist orientations of much of the literature on professional learning.

It is further argued here that the entanglement of the particular technologies and functions of Twitter and the discursive structures and strategies mobilised in the Chat events creates tensions between discursive territorialisation and stabilisation of particular discourses of professional identity and meaning-making and the deterritorialisation, fragmentation and fluidity unscripted in to Twitter itself.

Digital Badges

Initial sketch for structure of Digital Badges

Initial sketch for structure of Digital Badges

I’m currently working on an open content course – the learner proposes the learning activities, the evidence they will gather and how they will demonstrate that they have met the agreed learning outcomes. It is pretty interesting stuff and opens up huge opportunities for experimenting on learning and education. To help in keeping students on track in the course, we are looking at developing a couple of sets of process-based digital badges and this is an early sketch of the possible structure of the badges.

Line manager role identity as facilitators of learning


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?


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.


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.

The Twitter Experience

For all the structuring effects of the Twitter functional features, the Twitter experience is generally perceived as a private one as only the individual user can see their Twitter feed, as they have structured it, on their particular screen configuration (Gillen and Merchant 2013). This aspect of the individualisation and heterogeneity of public and open textual communication adds to the complexities of interpreting, analysing and making sense of Twitter. Gillen and Merchant’s (2013) discussion of the capacity of Twitter users to organise the flow of discourses they are presented seems to ignore both the algorithmic impositions of, for example, Trending terms in that interface as well as the effects of the content of individual Tweets being perceived as a coherent informational flow or a chaotic mess of impressions (or both). The Twitter user experience is not an isolated or individualised one but is, rather, an entanglement of heterogeneous intentions, business logics, coded protocols, algorithmic outputs, collective norms and individual perceptions.

It is this entanglement between the human and material that opens, closes and patterns or orders the particular uses of Twitter. Twitter is constantly and actively made and remade in the intra-actions of user behaviours, hardware, coding, algorithms and visual design, rather than Twitter being a neutral utility or passive instrument.

Getting stuff [and writing] done

I’ve recently started using Chris Winfield‘s technique of chunking tasks to 40 pomodoros per week which he describes here. I’m essentially using this technique for “maker” time – as described in this post from Paul Graham. I’ve found this technique works really well for writing (one I know what I’m going to write) as described as writing sprints here.

It may be the case that the “quieter summer” has made this easier and once the new academic year starts, I’ll find it harder to maintain this, but so far, I’ve found it impressively productive and not too tough to keep to.

Distributed curriculum

This Tweet caught my eye today by triggering a train of thoughts on what a ‘distributed curriculum’ might involve.

Digitally Distributed Curriculum

This idea appears to position the curriculum as an outcome of interacting within networks of people, resources and technologies. I wonder if this curriculum is a restating for a formal education context, of the sort of personalised learning I previously discussed here. One of the issues here is on curricula design and whether all students have the capabilities, capacities and capital to direct the generation of their own curriculum in a coherent and sustainable manner or whether ‘fluid curricula’ models will need and be required to be fairly striated or ‘channeled’. Similarly, there is a need to develop successful practices on supporting students and staff in approaches to self-directed and self-regulated learning enabling deep engagement with ‘wicked’ subject problems.

Another aspect to the distributed curriculum may well be a social aspect of both participating in external professional and other communities as well as generating ephemeral communities of learners that ‘swarm’ around specific learning objects and artefacts as well as collectively bringing these objects/ artefacts in to engagement with the subject problem of interest.

Weeknotes 26062015

This has been a week of knuckling down at getting stuff done – but I also squeezed in one day off as the last day available before the schools break for summer (schools in Scotland start the holidays at the end of June but return mid-August which still feels so wrong to me). What I did this week:

attended a briefing session on the University’s process for academic promotion (lots of paperwork and pretty tough criteria)
had numerous dissertation supervision sessions on Skype
progress on my PhD writing an overview of research in to Twitter and the dominant approaches based on quantitative methods of statistical analysis and Social Network Analysis or studies based on conversation analysis. The comparative lack of qualitative research is a notable omission especially in considering the affective dimensions of online ‘communities’

A couple of links of interest from this week:
No, Sesame Street Was Not The First MOOC from Audrey Watters is a great post on open education, the history of MOOCs, the insurgency of venture capital in EdTech and the importance of theory and research in education (and some good pics of Bert & Ernie).

Mark Carrigan’s post on using a blog as a research journal is a useful overview of the purpose of a research journal as well as the benefits of working out loud.