Truth in Education

To help us prepare for the Rebel Wisdom Summit on May 12th , in London, participants have been sent links to a number of videos which feature the keynote speakers, Iain McGilchrist, Bret Weinstein, Heather Heying and Jordan Greenhall (see my last blog post for links to the videos). I have been particularly interested in the videos in which Heather Heying appears. Heying is an evolutionary biologist who, having been forced, in 2017, to leave her tenured position at Evergreen State College in Olympia, Washington, together with her husband Bret Weinstein, now describes herself as a Professor in Exile.

Although I was not aware of Heather Heying’s story before watching the Rebel Wisdom videos, the idea that free speech is being curtailed in the name of political correctness and social justice, is not new to me. Mariana Funes and I discussed this in relation to the work of Jonathan Haidt in our 2018 paper When Inclusion Excludes: a counter narrative of open online education.  I have some personal experience of the negative consequences of ‘going against the grain’, so I was interested in what Heather Heying had to say in the video in which she and Bret Weinstein discuss ‘Having a Real Conversation” with David Fuller, a founder of Rebel Wisdom. According to some news reports, Bret Weinstein asked students for a ‘dialectic‘, a ‘real conversation’, rather than a ‘debate’ about the issues that led to his leaving Evergreen State College with his wife Heather Heying, but this did not transpire.

A lot of what Heying and Weinstein say in the ‘real conversation’ video is not new to me. My experience is that good teachers know that they have to ‘set the stage’ when starting a new course or a new term with school children, and that it is worth spending some time at the beginning of the course or term mutually agreeing how the class will work. Good teachers also respect their students and know that they must ensure that everyone has a voice and that alternative perspectives are respected. I am not an evolutionary biologist, so I cannot say whether the potential for conflict in evolutionary biology classes and similar subjects is greater than in, say, something like physics or mathematics, but I suspect that it may be, especially in America where there are schools teaching creationism.

At about six minutes into the video, Heather talks about freeing students from the yoke of authority and learning to think for themselves. At this point she also says, If we’re trying to figure out what is true, science is the best tool we have,  and If we find that we can’t do science on what you’ve said, what can we do to what you’ve said to make it falsifiable. The longer we can’t falsify it, the more likely it is that it is true. So she takes a scientific approach to truth.

I specifically noticed this because I have just finished reading Julian Baggini’s book, A short History of Truth. Consolations for a Post-Truth World. On the back cover of this book is written:

How did we find ourselves in a  “post-truth” world of “alternative facts”? And can we get out of it? A Short History of Truth sets out to answer these questions by looking at the complex history of truth. Renowned and respected philosopher Julian Baggini has identified ten types of supposed truth, and explains how easily each can become the midwife of falsehood’.

Baggini discusses empirical, authoritative and reasoned truths, the idea that truth should be grounded in evidence, that truths can be known and that reason can lead to truth. All these seem to be the kinds of truths that Heather Heying focuses on as the basis for real conversations with her students.

But there are also, according the Baggini, eternal truths, esoteric truths, creative truths, relative truths, powerful truths,  moral truths and holistic truth. These seem to emphasise different aspects to how we recognise truth than the empirical truth focussed on by Heying. This made me wonder whether the idea that there can be many types of truth was discussed by her students and how this idea might influence the outcome of a ‘real conversation’.

According to Iain McGilchrist we cannot go to science for truth. As I wrote in a previous blog post he believes that

Science cannot fulfil the role of purveyor of truth. Good science is always aware of its limitations, but science cannot discover the purpose of life nor tell us about God’s nature or existence and science promotes the use of models. There is always a model whether we are aware of it or not, but the model we choose determines what we find.

Science places a high value on precision, but what about things we cannot be precise about, where apparent opposites come together? Science passes over entities that cannot be measured; it takes things out of context and decontextualizes the problem. We put our faith in science because it is seen to be objective, but science is not value free. A lot of scientific research is not adequately designed; we know that the Hawthorne effect can influence scientific results and positive findings are more likely to be published than negative ones. We can’t ask science to do what it can’t do. A hypothesis cannot be proved nor disproved. Each comes with many assumptions. Proof used to mean a trial run (as in a printed proof).

Science cannot provide us with dependable ultimate truths. It’s not pointless, but it does not provide us with reliable truth. Philosophy equally has problems with notions of intuition, uncertainty, rationality, reason and the complexity of truth.

Given that both Heather Heying and Iain McGilchrist will be speaking at the Rebel Wisdom Summit, I will be very interested to see whether the question of truth comes up, and if it does the extent to which they agree or differ on the meaning of truth.

And I wonder what they would both think of Baggini’s simple rubric to help us nurture truth. This is how Baggini ends his book in a discussion of future truths. (p.107)

  • Spiritual ‘truths’ should not compete with secular ones but should be seen as belonging to a different species.
  • We should think for ourselves, not by ourselves.
  • We should be sceptical not cynical.
  • Reason demands modesty not certainty.
  • To become smarter, we must understand the ways we are dumb.
  • Truths need to be created as well as found.
  • Alternative perspectives should be sought not as alternative truths but as enrichers of truth.
  • Power doesn’t speak the truth; truth must speak to power.
  • For a better morality we need better knowledge.
  • Truth needs to be understood holistically.

References

Baggini, J. (2017). A Short History of Truth. Consolations for a Post-Truth World. Quercus.

Funes, M. & Mackness, J. (2018): When inclusion excludes: a counter narrative of open online education, Learning, Media and Technology, DOI: 10.1080/17439884.2018.1444638 When Inclusion Excludes MF:JM 280218

McGilchrist, I. (2009). The Master and His Emissary. The Divided Brain and the Making of the Western World. Yale University Press.

Bret Weinstein and Heather Heying, “Having a Real Conversation”: https://youtu.be/ZBkF-xJh6tU

A Conversation about Community in the Distributed web

This image created by Kevin Hodgson, a participant in the E-Learning 3.0 MOOC, as Stephen Downes said on Twitter, ‘basically completes the Task for week 8’.

For an interactive version of this image see: https://www.thinglink.com/fullscreen/1129211585894547458

The final discussion about the topic of community in the E-Learning 3.0 course centred on a Google Hangout discussion between Stephen Downes and Roland Legrand. The Hangout was open to anyone and there were a few people, including myself, in the chat, but only Roland Legrand in the Hangout with Stephen. This worked really well, allowing the conversation between them to develop and dig deeper into some interesting ideas. I can recommend watching the video recording, as their discussion helps to clarify some of the issues we have been struggling with in relation to this topic on community in the distributed web.

The discussion started with a review of how the week’s task had been experienced. Stephen had asked participants to create a community through consensus, without giving us any indication of how to do this, or what else to do, and ideally without using a centralised space. Laura Ritchie, Kevin Hodgson  and Roland put forward proposals on how to do this and ultimately we went with Roland’s initial suggestion, whilst also taking account of Laura and Kevin’s thoughts. Stephen pointed out in the Hangout that had the course attracted a larger number of participants the task would have been more difficult, because there would have been more proposals and people would have organised into groups. How then would we have chosen which community to join (the task stipulated only one community)? How do you solve consensus generally?

Roland thought that his proposal only required minimal commitment from participants, but Stephen thought that it could have been even more minimal. Whilst we all (those who participated) reflected on our course experience in our individual blogs, Stephen suggested that all we had needed to do was to provide evidence that we were there, maybe by posting the #el30 hashtag and stating that anyone who posted this was a member of the community. By making the task performative (writing a blog post) did it become exclusive? Roland questioned how posting a hashtag would work. Wouldn’t people be too dispersed?  He asked, ‘Why even talk about community?’

For Stephen (and see his summary for the week for further thoughts on this) the concept of community is important in the context of truth and facts. How do we know we belong to a community? This relates to how do we know a fact is a fact? And how do we know which facts to believe? How do we meet each other to discuss this?

Roland suggested that we need empathy and openness beyond the facts, because when faced with alternative facts our identities are threatened. The first thing people need is to feel recognised and safe. His question was, if we want people to meet each other to discuss alternative facts and perspectives, won’t the distributed web make things more difficult? Stephen agreed that lot of things are harder on the distributed web. It’s easier to build and work on a centralised platform, but as Stephen pointed out, we are already living in a world where information is distributed. For him centralised to decentralised is six of one and half a dozen of the other. He also pointed out that the decentralised web flourishes in the financial community and that there is no empathy in this community.

Roland questioned whether there is a planetary community and thought that the idea of a planet-wide lack of empathy was a bleak vision. He wondered whether we are too negative about it all, saying that humanity is more peaceful today than ever before, and most people can be trusted. But, as Stephen said, whilst most people the world over are ‘good’ there remain bad actors. We have to build resistance to bad actors and that’s why making things harder, through blockchain, encryption and managing our own data, might be a good thing. But Roland suggested that encryption and managing our own data might also be bad for security. Stephen agreed that there is tension between openness and privacy, and that a balance is needed.

They then went on to discuss whether we could set up some sort of community/forum to continue to discuss these complex ideas and whether this space should be open or closed, on a centralized platform or on the distributed web. Roland is keen to continue the discussion.

From my perspective the community topic has been very challenging, causing me to question my understanding of what we mean by community on the distributed web, and the role that trust, truth and consensus play in the formation of community on the distributed web. I have not come to any firm conclusions yet about how all the ideas fit together and why they are significant. But as I have mentioned in a previous post, I think it may be necessary to rethink the language we use when discussing how community is formed in the distributed web. A verse from the King James Bible comes to mind.

Neither do men put new wine into old bottles: else the bottles break, and the wine runneth out, and the bottles perish: but they put new wine into new bottles, and both are preserved.

Many thanks to Stephen and Roland for a fascinating discussion.

Trust, Truth, Consensus and Community on the distributed web

The seventh topic in the E-Learning 3.0 MOOC  has been Community. I have invested quite a bit of my time over the years learning about community – or more specifically communities of practice. I have been a founder member of a community (ELESIG which is still going strong) and a facilitator/moderator in a community (CPsquare – which no longer functions as a community, but relationships still remain – see image below). I have attended courses on CoPs to dig deeper into the theory behind them (BEtreat workshops ), published three research papers about communities of practice and have written numerous blog posts. (I should add a page to my blog about this). I thought I had a reasonable understanding of what it means to be a member of a community, but this week has made me doubt this understanding.  Why?I have been asking myself this question for quite a few days now, and today it occurred to me is that it is a language problem. The way in which language related to community is being used in this course about the distributed web, i.e. what we mean by community, consensus, trust and truth in the distributed web, is not how I have previously understood it.

Let’s start with trust.

Trust is thought to be an essential component of communities of practice. In their book (p.8) Digital Habitats, Wenger, White and Smith write:

‘Learning together depends on the quality of relationships of trust and mutual engagement that members develop with each other, a productive management of community boundaries, and the ability of some to take leadership and to play various roles in moving the inquiry forward’

And in Wenger, McDermott and Snyder’s book, Cultivating Communities of Practice  (p.85) they have written:

The trust community members need is not simply the result of a decision to trust each other personally. It emerges from understanding each other. As one oil reservoir engineer observed, “Sometimes you can share an insight that is so useful it saves a well from going down, but you don’t save a well at the first meeting.”

In other words, communities take time to develop, which is also depicted by the diagram above.

But in the conversation that Stephen had with Pete Forsyth they both agreed that the internet is a trust-less environment. In his post on ‘The Problem of Trust’, Vitalik Buterin has written:

If you were to ask the average cryptocurrency or blockchain enthusiast what the key single fundamental advantage of the technology is, there is a high chance that they will give you one particular predictable answer: it does not require trust.

This suggests that trust either functions differently or doesn’t exist at all on the distributed web. Stephen and Pete both believe that trust is an aspect of community. So both trust and community on the distributed web, in their terms, seem to mean something different to Wenger et al.’s understanding of it.

Pete Forsyth suggested that in Wikipedia (which I have written about in a previous post) we put our trust in facts and not in people. I can accept that on the distributed web it probably makes more sense to understand trust in these terms. That’s not to say that there won’t be trust between people, but perhaps we don’t need this on the distributed web. So the meaning of trust might be more limited term on the distributed web?

But what about community?

In a draft document he has shared with us (I have typed draft in bold, so that we can acknowledge that it might change), Stephen distinguishes between what he calls ‘natural’ communities as opposed to ‘organised’ communities of the type discussed by Wenger and his colleagues. He describes natural communities, e.g. ‘your average city’, as lacking in trust, where there are enforcement mechanisms, because we don’t trust people to obey the law or rules. ‘Cities are polyglot, factional, disjointed. Yet, still – they are communities’, he writes. I have yet to be convinced by the idea that a city is a community.

I believe that there can be and are communities within cities, but that cities are not communities. I agree with the author of this post about ‘What does community mean?’ where s/he has written: ‘just living near each other, as in a suburban neighborhood, doesn’t mean you’re in community.’ (The rest of the post is also interesting). ‘Neighbourhood’ may be a more appropriate term for a city, as Mike Caulfield suggested for FedWiki – which could be described as a decentralized distributed wiki (see my previous post for further discussion) and ‘network’ may be more appropriate for the distributed web.

But there is a reason for Stephen’s focus on community this week, which seems to be that working on the distributed web requires consensus; consensus to agree on what information can be trusted to be true. How do we achieve this consensus on the distributed web where there is no ‘leader’ and no ‘common ground’? Stephen believes that we do this through community and that community is consensus.

My question is, do we have to have community for consensus on the distributed web?  Unless I have completely misunderstood this, the evidence from Preethi Kasireddy’s post How does distributed consensus work? would seem to suggest that the answer is ‘No’, unless we are attributing the word ‘community’ to non-human actors. I have a horrid feeling that I have completely misunderstood all this, but from where I am standing, the word ‘community’ being used in this context just does not fit with any of my prior understanding.

In relation to achieving consensus on the distributed web about what information we can trust, we are told by Waggoner et al. that there are many consensus methodologies, to the point where they have written a paper questioning whether there is a consensus on consensus methodology.  From this article we can see that many researchers are working on how to achieve consensus in relation to the trust we can put in facts on the distributed web.

But what about in society? What are the consequences of a consensus driven society which relies on agreement. As John Kay wrote way back in 2007 in his article ‘Science is the pursuit of truth, not consensus’, ‘Consensus finds a way through conflicting opinions and interests’. (The Financial Times has blocked me from posting a link to this article. You will need to ‘trust’ me that this is what he wrote!) Kay seems to suggest that consensus is often arrived at, at the expense of truth. If this is so, should we ‘trust’ in the ‘truths’ arrived at by consensus?

In his article: Fake News, Wikipedia and Blockchain (Truth and Consensus), Arthur Charpentier seems to suggest that the words we use matter. He writes:

This plurality of words, and the absence of a reference word, is not unlike the philosophy conveyed by crypto-currencies: instead of a centralised mode of governance (validation, certification), it is a global validation by a network, a consensus, which will prevail. Have we changed our definition of what truth is?

This resonates with me because this week I have been asking myself similar questions. What does community mean in relation to the distributed web? Can community function on the distributed web? Do trust, community and consensus take on different meanings on the distributed web? Perhaps we need to go back to what these words mean and whether they have taken on different meanings for use in discussion about the distributed web.

Stephen Downes’ summary of this topic – Community – Summary of the topic

https://el30.mooc.ca/cgi-bin/page.cgi?post=68638

Source of images

Stages of development of a community of practice  – https://www.slideshare.net/richard.claassens/communities-of-practice-stages-of-development-388654

Fake News, Wikipedia and Blockchain (Truth and Consensus) – https://freakonometrics.hypotheses.org/52608

Consensus and community in the distributed web

The topic for this week in the E-Learning 3.0 MOOC is Community. I struggled last week to understand how the concept of ‘Recognition’ was being interpreted in relation to the distributed web, and I suspect I am going to struggle this week to understand how the concept of community will be interpreted.

In his Synopsis for the week Stephen Downes writes that recent times have seen us shift from an idea of community based on sameness, to a time when society has difficulty agreeing on basic facts and truths. A whole blog post could be written about just this, but I will move on.

Stephen sees community formation, in this day and age of the distributed web, as dependent on decision making and consensus. Consensus is no mean feat, but is essential if we are to counteract the influence of ‘bad actors’ who distribute false information and fake news. A critical mass of society must check and agree on what information we can trust or not trust. In an interesting article by Preethi Kasireddy- How Does Distributed Consensus Work? – decision making and consensus at the level of algorithms is discussed and it is clear that artificial intelligence will have an increasing role to play in determining what we trust and how we perceive truth. But for now we will stick to a more familiar environment in which we can observe how decision-making to achieve consensus is achieved, by real people rather than robots.

This week Stephen’s conversation was with Pete Forsyth, Editor in Chief of the Signpost, a community newspaper covering Wikipedia and the Wikimedia movement.  Their discussion covered what we mean by community and consensus in relation to how ‘Wikipedia approaches questions like managing fake news, reaching consensus, and managing content‘.

I’m not sure that a discussion of how Wikipedia reaches consensus is comparable to reaching consensus on the distributed web, since Wikipedia is built on a centralised platform, but it is a platform used by tens of thousands of people across the world, and therefore provides a good basis for exploring how consensus works across large numbers. According to Wikipedia’s own site an average of 561 new articles are written every day and Wikipedia develops at a rate of over 1.8 edits per second, with editing being carried out by about 10% of users. As of August 2018, about 1000 pages are deleted from Wikipedia each day.

How is this consensus achieved?  What can we learn from Wikipedia about how to trust that the information we are reading is ‘the truth’? These are some of the thoughts shared by Pete Forsyth.

  • Wikipedia does not trust in people. There is no mechanism for establishing the authority of the writer in Wikipedia. It trusts in facts.
  • Facts must be checked and backed up by sources. (Although this wasn’t mentioned, I think Mike Caulfield’s Web Literacy for Student Fact Checkers – is worth remembering here).
  • Trust should always be rooted in understanding. It’s important to check the history and discussion forums in Wikipedia.
  • Wikipedia defines a reliable source as being independent of the topic.
  • Trustworthiness of sources is on a gradient. Exceptional claims require exceptional sources.
  • Wikipedia prefers consensus to democracy, i.e. decisions are not reached by voting but by consent, which does not necessarily mean agreement.
  • Wikipedia promotes individuals as decision makers.
  • Wikipedia is edited according to Be Bold, Revert and Discuss principles.
  • A record of every edit in kept in the page history.
  • Open process, open access and transparency are strongly held core values in Wikipedia.
  • Wikipedia software is designed to focus on creating a space for interaction and keep the software out of the way.
  • Wikipedia provides guidelines for interaction and editing.

Here is a video recording of the whole discussion.

For me the questions that remain are, is Wikipedia a community and what is a community?

Wikipedia is a community for some people – probably for the 10% using it who actually contribute to it, rather than simply use it, although on the Wikipedia page about the community, the community in the larger sense is defined as including: all casual and/or anonymous editors, ideological supporters, current readers and even potential readers of all the language versions of Wikipedia-the-encyclopedia.

My prior understanding of a community is more in line with their narrower definition: the community –  is that group of contributors who create an identity (either a user account, or a frequently-used anonymous IP), and who communicate with other contributors.

This is a better fit with my knowledge of Etienne Wenger’s work on communities of practice.  I mentioned this briefly in a comment that I made on Laura Ritchie’s blog post, where I wrote that in Wenger’s terms a community of practice exhibits the dimensions of mutual engagement, shared repertoire and joint enterprise. Laura identifies her orchestra as a community, which seems to fit with how Etienne Wenger sees a community.

In his blog post Kevin Hodgson wonders whether a community is the same thing as a network or affinity space. I have heard Etienne Wenger say that all communities are networks, but not all networks are communities (see p.19 in this publication).

I also noted when watching the video that Pete Forsyth described community as ‘an amorphous concept of affiliation’.

And Stephen in a comment on Laura’s post writes about ‘natural as opposed to organised communities’. I will copy his whole comment here as I think in it we have the essence of how we are to understand community during this week of the course, and for considering how community might be thought of on the distributed web:

When we look at (what I’ll call) natural communities (as opposed to organized communities) they have two major features: lack of trust, and lack of mutual engagement, shared repertoire and joint enterprise.

Think of your average city. There may be a lot of what we call ‘trust’ (eg. people stopping at stop signs) but in nearly all cases there’s also an enforcement mechanism, because we don’t actually trust people (eg. to actually stop).

Similarly, while in a city we can talk about engagement, repertoire and enterprise (and we should) in most cases there is no engagement, repertoire and enterprise that is _common_ to everybody in the city. Cities are polyglot, factional, disjointed. Yet, still – they are communities.

The challenge (indeed, maybe even the challenge of our times) is how to understand and improve communities where people are *not* engaged in the same enterprise as everyone else.

From all this I am beginning to think that the word ‘community’ has too much associated history to be useful when considering how to communicate, interact, make decisions and reach consensus on the distributed web. It leads to a set of expectations that may not be useful in this context. On the Wikipedia page about community is written: The essence of community is encoded in the word itself: come-ye-into-unity. That’s a lovely way to describe community as I have always understood it. But my understanding of this week’s topic is that we no longer want or need unity. Instead, we need consensus on what is true.

I don’t believe that the traditional idea of community or a community of practice will be lost. We will all interact in communities of one sort or another; Laura in her orchestra, Kevin in his classroom, me in the village where I live, and so on. But we will probably need to think differently about community when considering what information we can trust, and what is true, on the distributed web. A new way of thinking about it may become more obvious the more we interact on the distributed web.

The idea of a distributed Wikipedia was briefly discussed by Stephen and Pete, with reference to Ward Cunningham’s Federated Wiki. In 2014, I explored the potential of FedWiki with a few others. It is a wiki with no centralised space i.e. each person has their own site, from which they can link to other people’s sites and select or reject edits of their own pages. Looking back at my blog posts, I see that I found it intriguing but not easy – a bit like this course, which seems to challenge a lot of my prior understanding about learning on the web.

Mike Caulfield described Fed Wiki as a ‘neighbourhood’, not a community, nor a network. Would this be a better word than ‘community’ and if not what would? I think a different word would help with the change of mindset needed to understand all this.

Resources

How Does Distributed Consensus Work?
Preethi Kasireddy, Medium, 2018/12/05
The brief basics of distributed systems and consensus. Nakamoto Consensus is truly an innovation that has allowed a whole new wave of researchers, scientists, developers, and engineers to continue breaking new ground in consensus protocol research.

What is Blockchain?
Lucas Mostazo, YouTube, 2018/12/03
Blockchain explained in plain English Understanding how blockchain works and identifying myths about its powers are the first steps to developing blockchain technologies.

Education Blockchain Market Map
Stephen’s Web ~ OLDaily, 2018/12/05
HolonIQ, Nov 30, 2018 Though dated last June this market map appeared in my inbox from Holon only today. It reports five sectors of the education blockchain market: credentials and certifications (the largest by far), peer-to-peer ecosystems, payments, knowledge and marketplace. The website describes each briefly and links to some representative startups. The site reports, “Blockchain’s significant potential in education – from powering efficiency to collapsing costs or disrupting the current system – is becoming clearer to technologists, educationalists and governments alike.”

Consensus decision-making
Wikipedia, 2018/12/04
Consensus decision-making is an alternative to commonly practiced group decision-making processes. Robert’s Rules of Order, for instance, is a guide book used by many organizations. This book allows the structuring of debate and passage of proposals that can be approved through majority vote. It does not emphasize the goal of full agreement. Critics of such a process believe that it can involve adversarial debate and the formation of competing factions. These dynamics may harm group member relationships and undermine the ability of a group to cooperatively implement a contentious decision. Consensus decision-making attempts to address the beliefs of such problems.

Wikipedia:Consensus
Wikipedia, 2018/12/04
Decisions on Wikipedia are primarily made by consensus, which is accepted as the best method to achieve Wikipedia’s goals, i.e., the five pillars. Consensus on Wikipedia does not mean unanimity (which is ideal but not always achievable), neither is it the result of a vote. Decision making and reaching consensus involve an effort to incorporate all editors’ legitimate concerns, while respecting Wikipedia’s policies and guidelines.

How Wikipedia dodged public outcry plaguing social media platforms
Pete Forsyth, LinkedIn, 2018/12/05
Wikipedia has problematic users and its share of controversies, but as web platforms have taken center stage in recent months, Wikipedia hasn’t been drawn into the fray. Why aren’t we hearing more about the site’s governance model, or its approach to harassment, bullying? Why isn’t there a clamor for Wikipedia to ease up on data collection? At the core, Wikipedia’s design and governance are rooted in carefully articulated values and policies, which underlie all decisions. Two specific aspects of Wikipedia innoculate it from some of the sharpest critiques endured by other platforms.

Hacking History: Redressing Gender Inequities on Wikipedia Through an Editathon
Nina Hood, Allison Littlejohn, International Review of Research in Open and Distributed Learning, 2018/12/05
This article explores the “experiences of nine participants of an editathon at the University of Edinburgh on the topic of the Edinburgh Seven, who were the first women to attend medical school in 19th century United Kingdom.” The authors argue “it was through the act of moving from consumer to contributor and becoming part of the community of editors, that participants could not only more fully understand issues of bias and structural inequities on Wikipedia, but also actively challenge and address these issues.” It makes me think of the slogan: “no knowing without doing.”

Wiki Strategies. Making Sense of Collaborative Communities – https://wikistrategies.net/

Identity graphs as a ‘source of truth’

Week 4 on Identity in Stephen Downes’ E-Learning 3.0 MOOC has come to an end. It was another very interesting week. Stephen has summed up the week with a video and a paper, both of which I will link to in this post. They deserve to be widely distributed.

Whilst the topic title for Week 4 was ‘Identity’, and we had some discussion about identity in general as a philosophical idea that ‘runs through the history of education in a single thread’, the week mainly focussed on digital identity, which Stephen clearly said is not the same as ‘my identity’.

I was pleased to hear him say this, as throughout the week I consistently felt that our digital identity is nowhere near the whole picture of who we are. As Stephen says it is just one outcome of ‘myself’, through which I can recognise parts of myself and others can get glimpses of me, or if they know me can recognise a bit more.

I subscribe to National Geographic and this week I received a mailing from them which included this stunning photo by Yuri Andries.

But it was the text underneath that caught my attention. ‘…. the photo was taken in Ladakh, a remote alpine region of northern India where Tibetan Buddhists, Shia Muslims, Sunnis, and Christians live in villages connected by rocky roads. There’s no phone signal, Internet, or gas stations, and hardly a single person in sight.’ So no digital identity for anyone living there, but of course that doesn’t mean no identity.

I was struck by this because also this week I have been trying to understand what Stephen means by the ‘source of truth’ for the identity graphs we have created. How do we know the graph is an accurate representation of who we are? Where does the information come from? In his video Stephen says ‘we are the source of truth for our digital identity’; we are the thread that runs through the disconnected and distributed data that makes up our digital identity graph. Instead of our digital identities being about quantified demographics, they will (in E-Learning 3.0) be about quality, about the rich tapestry of data relations we have.

In case your were wondering, there is a link in my thinking between the people living in Ladakh with no digital identity, the many of us who do have a digital identity of one sort or another, and the idea of a source of truth, because it occurred to me that the truth about identity has to also include what is neglected, hidden or invisible. The emergent identity from the graph must surely be as much about what is not there as what is there.

As I was thinking about this Vahid Masrour published a post in which he writes about how he discusses online identity with this students. He urges his students to consider how their digital identity might be interpreted by future employers. This highlighted for me the idea that we have to manage our identities, deciding what to reveal and what to hide.

Stephen has said that identity in E-learning 1.0 and 2.0 was about the ‘quantified self’ where our digital identities were represented by demographics and numbers. E-Learning 3.0 will see us shift towards digital identities which reflect the qualified self and ultimately the connected self.

For the connected self, being represented by numbers (the quantified self of E-Learning 1.0) and facts (the qualified self of E-Learning 2.0) will not be sufficient. The connected self will be more about our relations and interactions. Will this lead to a more ethical Web? Will digital identity as a ‘connected self make the ‘source of truth’ of these identities more visible? Will the ‘connected self’ be more reflective? Will ‘the connective self’ more honestly reflect our hopes, aspirations and dreams?

Much of what is being discussed in this course is new to me and that includes the idea of identity graphs and the qualified and connected self. Stephen has clearly been thinking about this for some time. For further insights into his thinking I can recommend watching the video embedded above and reading this paper, which he shared as a summary of the week.

E-Learning 3.0, Part 4: Identity – https://el30.mooc.ca/cgi-bin/page.cgi?post=68516

Further resources

Digital Identity on the Threshold of a Digital Identity Revolution – http://www3.weforum.org/docs/White_Paper_Digital_Identity_Threshold_Digital_Identity_Revolution_report_2018.pdf

The Economic Impact of Digital Identity in Canada – https://diacc.ca/wp-content/uploads/2018/05/Economic-Impact-of-Digital-Identity-DIACC-v2.pdf

Canada’s Digital Economy Relies on a Foundation of Digital Identity – https://diacc.ca/2018/05/16/the-economic-impact-of-digital-identity-in-canada/

Identity as an Analytic Lens for Research in Education https://www.jstor.org/stable/1167322?seq=1#page_scan_tab_contents

See also the resources listed on the course site: https://el30.mooc.ca/cgi-bin/page.cgi?module=8

E-Learning 3.0 : Identity Graphs

We are now in the fourth week of this E-Learning 3.0 open course/MOOC. The task for this week is to create an Identity Graph, which Stephen Downes (convener of this course) has outlined as follows:

Identity – Create an Identity Graph

  • We are expanding on the marketing definition of an identity graph. It can be anything you like, but with one stipulation: your graph should not contain a self-referential node titled ‘me’ or ‘self’ or anything similar
  • Think of this graph as you defining your identity, not what some advertiser, recruiter or other third party might want you to define.
  • Don’t worry about creating the whole identity graph – focusing on a single facet will be sufficient. And don’t post anything you’re not comfortable with sharing. It doesn’t have to be a real identity graph, just an identity graph, however you conceive it.

Here is my graph, which I created using Matthias Melcher’s Think Tool – Thought Condensr, which is very quick and easy to use.

Like Matthias,  I puzzled over why Stephen required that the graph – “should not contain a self-referential node titled ‘me’ or ‘self’ or anything similar”. How could I avoid this if the graph is to be about my identity? In the event, it became obvious that not only is it possible to create the graph without referring to me, but also that doing this clearly demonstrates that knowledge of my identity is in the network rather than any specific node. My identity begins to emerge from the graph, without me having to specify it.

You can see from the graph that there are three links which don’t connect. I did this by simply cutting them off for the screenshot of the graph, because I wanted to suggest that this graph could, in fact, go on and on. This image provides only a glimpse of my identity. I could not only expand the graph, by making more links and connections, but I could also make more connections within this section of the graph. I am also aware that if I started afresh and drew this tomorrow it would be different because my identity and how I think of it is fluid and evolving.

I was also aware in drawing the graph that pretty much all of it is traceable online. It reminded me of the introductory task that was set on Etienne Wenger’s online course  Foundations of Communities of Practice that he ran with John Smith and Bron Stuckey in 2008. The task was based on the idea of six degrees of separation. “Six degrees of separation is the idea that all living things and everything else in the world are six or fewer steps away from each other so that a chain of “a friend of a friend” statements can be made to connect any two people in a maximum of six steps” (see Wikipedia). At the start of that course we were given the name of an unknown fellow participant and had to find out enough about them to be able to link to them in six steps and then share this information. This was a very good way of learning more about fellow participants at the start of the course, but also of recognising that we can easily connect to anyone across the world in just a few steps.

Stephen set some further optional questions for us to consider:

  • What is the basis for the links in your graph: are they conceptual, physical, causal, historical, aspirational?

They seem to be physical and historical, whereas Matthias’s graph seems to emphasise the conceptual. 

  • Is your graph unique to you? What would make it unique? What would guarantee uniqueness?

I think it must be unique. The nodes are not unique, but the relations between the nodes, whilst they might not be unique individually, as a whole must be unique. I think it would be impossible to guarantee its uniqueness if it remained static. Anyone could come along and copy or mimic it. Uniqueness can only be guaranteed if the graph is continually updating, evolving and new connections are being made. I am not sure whether old connections can be broken, or do they just become inactive and move way off to the edge of the graph?

  • How (if at all) could your graph be physically instantiated? Is there a way for you to share your graph? To link and/or intermingle your graph with other graphs?

I’m not sure if I have understood the question correctly? Isn’t the graph I have created using Matthias’s Think Tool, and posted here, a physical instantiation? Does physical instantiation have a specific meaning in relation to graphs? I think I might have missed the point – but I can see that it would be relatively easy to intermingle my graph with Matthias’s graph. It might be necessary for us both to add a few nodes and links, but not many, to be able to connect the two graphs fairly seamlessly (a bit like the six degrees of separation task described above).

  • What’s the ‘source of truth’ for your graph?

This is a big question as it raises the whole question of what we mean by truth. I have been grappling with this for quite a few months now. In my most recent blog post about ‘truth’ –  I reported that both Gandhi and Nietzsche have expressed the view that “human beings can only know partial and contingent truths and perspectives; there are a multiplicity of truths and perspectives.” So in these terms, the truth of my graph can only be partial or contingent. Even if I have not knowingly lied, I have selected what to include in the graph and therefore I have also selected what to leave out.

But Stephen’s question is about the ‘source of truth’. Is he asking about ‘source of truth’ as defined in information systems?  This is not a subject I know anything about.

In information systems design and theory, single source of truth (SSOT) is the practice of structuring information models and associated data schema such that every data element is stored exactly once. Any possible linkages to this data element (possibly in other areas of the relational schema or even in distant federated databases) are by reference only. Because all other locations of the data just refer back to the primary “source of truth” location, updates to the data element in the primary location propagate to the entire system without the possibility of a duplicate value somewhere being forgotten. https://en.wikipedia.org/wiki/Single_source_of_truth

In these terms I’m not sure how to answer Stephen’s question about ‘source of truth’. If someone could enlighten me that would be great.

Thinking of knowledge as a graph

This is a response to the E-Learning 3.0 task  for course participants created by Matthias Melcher. See https://x28newblog.wordpress.com/2018/11/09/el30-graph-task/

The task requires that we select from one of the topics of this course, and create a map from the list of keywords for the topic provided by Matthias. Matthias took the keywords from the synopsis for each topic written by Stephen Downes. The task is to connect and annotate the keywords.

Matthias provided links to two types of mapping tool – cmap.ihmc.us  and  a tool he has created himself – http://condensr.de/download-page/ . I have used both tools in the past, but I am more familiar with Matthias’ tool, so I used that.

I selected the ‘Cloud’ list of keywords, to create this map.

  • storage
  • electricity
  • server virtualization
  • vmware
  • docker
  • amazon web services
  • edx
  • coursera
  • yaml
  • vagrantfile
  • jupyter
  • redefine textbooks
  • experience
  • algorithm
  • containers
  • load-balancing

Creating the map

Since I have used this tool before (see A new mapping tool: useful for research purposes) I did not find it technically difficult.

Here is a screenshot of the map I created. Click on the image to enlarge it.

And here is a link to the interactive map, which is much more interesting, because by clicking on a node you can see the annotations – http://x28hd.de/tool/samples/JM%20Cloud%20Map.htm 

(I contacted Matthias to ask him to create this link for me. WordPress does not host .htm files; at least, as far as I am aware it does not)

Despite the lack of serious technical difficulties,  I did somehow manage to inadvertently make 4 copies of my map, one under the other. I found that it took a while to delete each node and link individually. And at another stage I managed to lose the map entirely (I think I swiped it off the screen). I have done this before, but I couldn’t remember how to get it back. I had saved the xml file though, so just uploaded it again. I know that Matthias is refining this tool all the time, so a block delete function sometime in the future would be great. (Update 13-11-18 – See http://condensr.de/2018/11/12/a-user-question/ for Matthias’s video explanation of how to overcome these minor difficulties that I had)

I created the map using the text from Stephen’s synopsis. This revealed the aspects of the topic that I still haven’t understood. I made a note of these in the text annotations (in italics). I did look up definitions and explanations of some terms and added text if an explanation wasn’t evident in Stephen’s text, e.g. algorithm. If I were to continue to develop the map, I would do more of this.

Thinking of knowledge as a graph

This is the real challenge, i.e. moving from thinking and seeing knowledge in a linear way to thinking and seeing knowledge as a network/graph.  I like lists, but in recent years I have come to appreciate that when you organise and categorise terms in lists you miss the richness of connections. Some terms need to be in more that one category. A map shows us how ideas are interconnected. A list cannot do this. Matthias explains this really well at the start of his video, which is posted on his website download page – http://condensr.de/download-page/

I know from my experience of using this tool, that my tendency is to use it as a repository for resources. It is actually great for this. I have used it for research purposes, as a place to store information and thoughts about related articles, but as Stephen writes

The graph, properly constructed, is not merely a knowledge repository, but a perceptual system that draws on the individual experiences and contributions of each node. This informs not only what we learn, but how we learn.

To develop my knowledge of the Cloud, to learn and understand more about it, I need to grow my connections and the links between them. The state of my knowledge can then be represented by the map. A  key affordance of Matthias’ Think Tool is that it is easy to ‘grow’ the map, adding nodes and links, and storing information about them, as this growth occurs.

A graph is a distributed representation of a state of affairs created by our interactions with each other. The graph is at once the ­outcome of these interactions and the source of truth about those states of affairs. The graph, properly constructed, is not merely a knowledge repository, but a perceptual system that draws on the individual experiences and contributions of each node. This informs not only what we learn, but how we learn. (Stephen Downes – https://el30.mooc.ca/cgi-bin/page.cgi?post=68472)

I do not yet fully understand the link that Stephen makes between graphs and the “source of truth”. I have yet to read the article he links to – Epistemology in the Cloud, which I think might help. Stephen has written

The source of truth, if there is any, lies in how those links are created and maintained ….. and that …. it’s not the individual idea that’s important, but rather how the entire graph grows and develops. It protects us from categorization errors and helps prevent things like confirmation bias.

This links to what Matthias says, at the beginning of his video, about the dangers of pigeon-holing things.

These ideas go beyond what Matthias asked for in his task, but I do see that in order to start thinking of knowledge as a graph, we probably need to start by creating graphs, and his Think Tool helps to make the shift from thinking of knowledge as a representational system to thinking of knowledge as a perceptual system.

And finally, I now realise, more than before, that I have already been thinking about this, implicitly, in my search for understanding what Iain McGilchrist means by ‘betweenness’, which I was writing about last month on this blog. See

‘Betweenness’ : a way of being in the world – https://jennymackness.wordpress.com/2018/10/02/betweenness-a-way-of-being-in-the-world/

Understanding ‘Betweenness’ – seeing beyond the parts – https://jennymackness.wordpress.com/2018/10/10/understanding-betweenness-seeing-beyond-the-parts/

Edusemiotics, the Divided Brain and Connectivism https://jennymackness.wordpress.com/2018/09/17/4436/

Resources

Matthias Melcher Thought Condensr website – http://condensr.de/

E-Learning 3.0, Part 3: Graph – https://el30.mooc.ca/cgi-bin/page.cgi?post=68472 and associated video https://youtu.be/WiaxHxiN_IA  (Stephen’s summary of the week)