Tag Archives: networks

LinkedIn network map

This is a very short post on my LinkedIn network mapLinkedIn network map

The identification of three distinct clusters of contacts is interesting and (kind of) makes sense. What is particularly useful is identifying the links between clusters that ‘should’ be stronger. In terms of developing a professional personal learning network as part of a personal learning environment, LinkIn maps  look  useful as a visual “sense-making” tool and for identifying your network’s strengths and weaknesses. Next is to attempt to work out why some components of my networks look weak, if these weaker areas can and should be strengthened and, if so, how?

Social Network Analysis and Digital Data Analysis

Notes on a presentation by Pablo Paredes. The abstract for the seminar is:

This presentation will be about how to make social network analysis from social media services such as Facebook and Twitter. Although traditional SNA packages are able to analyse data from any source, the volume of data from these new services can make convenient the use of additional technologies. The case in the presentation will be about a study of the degrees of distance on Twitter, considering different steps as making use of streaming API, filtering and computing results.

The presentation is drawn from the paper: Fabrega, J. Paredes, P. (2013) Social Contagion and Cascade behaviours on Twitter. Information 4/2: 171-181.

These are my brief and partial notes on the seminar taken live (so “typos ahead!”).

Looking at gathering data from social network sites and on a research project on contagion in digital data.

Data access requires knowledge of the APIs for each platform but Apigee details the APIs of most social networks (although as an intermediary, this may lead to further issues in interfacing different software tools, e.g., Python tool kits may assist in accessing APIs directly rather than through Apigee). In their research, Twitter data was extracted using Python tools such as Tweepy (calls to Twitter) and NetworkX (a Python library for SNA) along with additional libraries including Apigee. These tools allow the investigation of different forms of SNA beyond ego-centric analysis.

Pablo presented a network diagram from Twitter using NodeXL as ego-networks but direct access to Twitter API would give more options in alternative network analysis . Diffusion of information on Twitter was not possible on NodeXL.

Used three degrees of influence theory from Christakes & Fowler 2008. Social influence diffuses to three degrees but not beyond due to noisy communication and technology/ time issues leading to information decay. For example, most RTs take place within 48 hrs so tends not to extend beyond a friends, friends friend! This relates to network instability and loss of interest from users beyond three degrees alongside increasing information competition as too intense beyond three degrees to diffusion decomposes.

The  direct research found a 3-5% RT rate in diffusion of a single Tweet. RT rates were higher with the use of a hashtag and correlate to the number of followers of the originator but negatively correlates to @_mentions in the original Tweet. This is possibly as a result of @_mentions being seen as a private conversations. Overall, less than 1% of RTs went beyond three degrees.

Conclusion is that diffusion in digital networks is similar to that found in physical networks which implies that there are human barriers to communication in online spaces. But the research is limited due to the limits on access to Twitter API as well as privacy policies on Twitter API. Replicability becomes very difficult as a result and this issue is compounded as API versions change and so software libraries and tools no longer work or no longer work in the same way. Worth noting that there is no way of knowing how Twitter samples the 1% of Tweets provided through the API. Therefore, there is a need to access 100% of the Twitter data to provide a clear baseline for understanding Twitter samples and justify the network boundaries.

Points to importance that were writing code using R/ Python preferable as easier to learn and with larger support communities.

Learning Insights ….

Kineo, the e-learning company, have issued a new report on e-learning insights based on interviews with “learning leaders” to identify key emerging trends. I’m not going to repeat the report but will look at a few of their ten key insights:

1. Learning is pervasive. Learning is continuous, collaborative and connected and most learning lives outside a learning management system. This has implications for the learning architecture and intervention models adopted by Learning and Development departments.

ZypadI see this as a key insight. Not as some new trend in learning but rather as something that L&D is (finally) waking up to. Most learning at and for work occurs through working: by solving problems; collaborating with others; being challenged and being observant. Much of this learning occurs vicariously and by serendipity and well outside much of the activities and service offers of L&D functions. The weaknesses were always that organisations were failing to understand that all this learning was going on and that staff weren’t being recognis

ed for making this learning happen. In addition, learning and knowledge was being lost because no attempt was made to capture it, staff were not always making best use of it as learning wasn’t either intentional or the main goal and also that employees had under-developed capabilities in “learning to learn“. What *has* changed is that digital technologies, especially digital working, has made such learning and knowledge more visible and these informal learning processes more transparent.

4. Design higher empathy learning. …It is not so much about meeting learning objectives as about empathy with the learner, their position, their challenges and personalising their experience.

Which I take to mean L&D should seek to get the right knowledge to the right people at the time they need to use it … No argument here and this may well prove to be a crucial focus for future developments around predictive learning analytics; knowledge management and knowledge resource development and work/ learning integration.

7. Informal learning must not become chaotic. There is a danger with the pervasive nature of learning and the wide range of informal opportunities that learning can become chaotic.

Is an interesting pronouncement but I’d argue not a key issue as most workers will be seeking to get the job done to the best of their abilities. L&D should be concerned with developing an enabling infrastructure, establish baseline (learning to learn) competence in employees but then largely get out of the way.

10. Where web technology goes, learning will follow. It is difficult to overstate the degree of change in web technology.

This seems to me to point to a shift from LMS  to Personal Learning Environments/ Networks that span the boundaries of any organisation and where significant components are owned by the employee – see Jane Hart’s post here

Its an interesting a useful report.


[Image of the ZYPAD, rugged wrist wearable computer from Arcom Control Systems licensed under the Creative Commons Attribution-Share Alike 3.0 Unported]

Learning networks

I’ve been recently reading a few papers on learning networks, either as open networks or within a single organisation. What these papers had in common was a focus on networks as mechanisms to support members, especially ‘novices’ (and boy to I hate that term), to navigate through some form of agreed curriculum. This seems to be based on Wenger’s definition of communities of practice as involving a common competence. So if there is a common (agreed) competence set then developing a curriculum whether formally or informally (even intuitively) should be fairly straightforward. But my research of open networks for learning indicate something else happening: that beyond a fairly discrete core, there is not a common competence and no clear curriculum. Rather, learning networks operate as sites of ongoing and continuous negotiation and renegotiation of a bounded set of requisite competences. Networks are rather curriculum forming mechanisms where that curriculum does not appear to settle. Now my research has been focused on learning and education professionals where external and prescribed ‘bodies of knowledge’ are not universally desired (but still seen as a necessary part of being a ‘proper’ professional). So I’m wondering what the experience of others who participate in learning networks and whether you recognise the notion of a curriculum of development guiding that network?