Ubiquitous commons

This has made me think of xAPI  and Learning Record Stores. And then, right at the end of the page, I bump into the concept of community yet again (!). I read:

Ubiquitous Commons allows for attributing citizens control over the data which they produce, and also to generate shared, meaningful patterns of perceived sensibility and responsibility, by enabling novel reflections in terms of identity, relation and belonging.

These can be used to foster new practices in which a new concept of digital public space emerges, which is accessible and inclusive, and also respectful of people’s right to self-determination and self-representation and, thus, to be able to more freely express our subjectivities, as individuals and as participants to multiple relational networksculturesbelief systemsFrom consensus to co-existence.

Ubiquitous Commons is the commons in the age of Ubiquitous Technologies.

Ubiquitous Commons is a legal-technological protocol: it positions itself among the other technological protocols which operate at the level of networks and technologies and among their legal implications and the set of laws, regulations, standards and norms which regulate them. Ubiquitous Commons is an open protocol.


In the Ubiquitous Commons environment, users can define a series of identities, which they hold and manage in what we have defined as their identity pool; each identity corresponds to a digital certificate, composed by a private and public key; identities can be of different types: individual/ collective/ anonymous/ temporary/ nomadic/ or a combination of the above.

Whenever a certain user generates data, this data is encrypted; the encrypted data is coupled with an attribution, stating which Ubiquitous Commons identity generated it (from), and which Ubiquitous Commons identities can access the data (to); this attribution is generated by the “from” identity; the encrypted data goes on to the service or application for which it was generated for; the attribution goes on to a peer-to-peer network or infrastructure –currently the BlockChain – in which the identifiers of the content (data) and of the from-to identities are published; in this way, the user can grant the availability and access to this data to the specified identities, determined autonomously.

A user who desires access to the data, executes a query onto the peer-to-peer infrastructure, asking whether data identifier X has been granted access to the user’s Ubiquitous Commons identity (the “to” identity in the attribution, picked from one of the identities in the accessing user’s identity pool) by the generating user (the “from” identity); if the user turns out to be attributed with the possibility to access (the query returns a positive result), the user obtains the decryption mechanism (recomposing the private key necessary to decrypting the data); the user uses the decryption mechanism to decrypt and access the data; the transaction is logged onto the peer-to-peer network.


Excerpts and Image from Ubiquitous Commons Website

Resources for Community (E-Learning 3.0)

The term community here refers to the social context of students and their environs. A community is a group of people with a common purpose, shared values, and agreement on goals. It has powerful qualities that shape learning. A community has the power to motivate its members to exceptional performance. M. Scott Peck defined community as “a group whose members have made a commitment to communicating with one another on an ever more deep and authentic level.” It can set standards of expectation for the individual and provide the climate in which great things happen (…) A real community, however, exists only when its members interact in a meaningful way that deepens their understanding of each other and leads to learning. Many equate learning with the acquisition of facts and skills by students; in a community, the learners—including faculty—are enriched by collective meaning-making, mentorship, encouragement, and an understanding of the perspectives and unique qualities of an increasingly diverse membership

Bickford D.J., & Wright, D. (2006). Community: The Hidden Context for Learning. In D.G. Oblinger (Ed.), Learning Spaces, EDUCAUSE e-book. Full chapter available here 

In this paper I explore different ways to understand the idea of community. Using the work of Alphonso Lingis, I make a distinction between the rational community and the community of those who have nothing in common. The latter community is the community in which we are all strangers for each other. I argue that the language of the latter community is the language of responsibility. It is this language that enables us to speak with our own, unique and individual voice. I argue that education and educators should be concerned with the latter form of speaking.
Therefore, the community without community, which exists as the interruption of the rational community, is the most important, and ultimately the only relevant educational community.

Biesta, G. (2004). The community of those who have nothing in common: Education and the language of responsibility. In Interchange, 35(3), 307-324. https://doi.org/10.1007/BF02698880 / Full paper available here 

Distributed Consensus


A distributed system is a group of computers working together to achieve a unified goal (with laymen terminology, it is a group of computers working together as to appear as a single computer to the end-user or end-client). Every distributed system has a specific set of characteristics. These include:

  • Concurrency: meaning multiple events occur simultaneously
  • Lack of a global clock: in a set of computers operating concurrently, it is sometimes impossible to say that one of two events occurred first, as computers are spatially separated.By determining which event happens before another, we can get a partial ordering of events in the system (…) time and order of events are fundamental obstacles in a system of distributed computers that are spatially separated.
  • Independent failure of components: acknowledging that components in a distributed system are faulty. These include crash fail, omission, byzantin
  • Message passing: synchronous or asynchronous

The Consensus Problem, Defined

An algorithm achieves consensus if it satisfies the following conditions:

  • Agreement: All non-faulty nodes decide on the same output value.
  • Termination: All non-faulty nodes eventually decide on some output value.

Broadly speaking, consensus algorithms typically assume three types of actors in a system:

  1. Proposers, often called leaders or coordinators.
  2. Acceptors, processes that listen to requests from proposers and respond with values.
  3. Learners, other processes in the system which learn the final values that are decided upon

Notes from: How Does Distributed Consensus Work? by Preethi Kasireddy in Medium Blockchain, full article available here

Image available here

Community (E-Learning 3.0)

So, I am doing a bit of catching up as I haven’t been able to follow everything on time. I did try to install the IPFS but never succeeded, so here are some thoughts about community that I’ve been thinking about ever since Prof. Downes involved us in that google doc writing.

First of all, (and after listening to Amy Burval conversation this became more clear) I really don’t think that community is necessarily consensus. For me, community is about reaching out to people that somehow inspire us by challenging our understanding of reality or what we do. We don’t necessarily have to agree with them or to reproduce their views or methods as much as to use their argumentation to get to know ourselves better. For example, Jenny (Mackness) talked about betweenness; that made me think of what that word meant to me, or how I related to her suggestions in defining this term, or Prof. Downe’s interpretation of community which is why I am now writing this post.

Bottom line is for me that community is primarily an expression of wanting to become better, knowing more or relating to otherness in a way that lights up one’s own path of being. Otherness in this sense, creates the challenge of having an opinion for yourself while community is gradually formed by the people one chooses to connect with in order to find his/her identity.

This is why it is so hard to make community members work together. It’s not so much about trust as it is about making all members share the exact same purpose when everyone is in pursuit of a different thing. People can be in the same community and still have different objectives. Consensus is probably the extremely rare case when community members actually agree on what they want, the way to get it and their own personal responsibility and accountability in getting it.

meraki/ μεράκι (E-Learning 3.0)

Oh, I really like this word. Thank you Amy for bringing it up. Meraki is a word that bears different meanings, it needs at least whole phrase to communicate its essence: it could express desire/longing, having fun or on the contrary suffering/feeling pain. The phrase however, is mostly used to describe one’s attentive curation of something, taking up an endeavor with zeal, with a joyful disposition to make it work.

I can totally relate to this term and what it stands for. I think all of you people in this course have it for learning and e-learning 3.0 in particular which is why being in this course felt so nice. So, thank you.


Knowledge as Recognition (E-Learning 3.0)

Recognition in Education_could come in the form of evaluations, badges etc. The answer to ‘how do we know hat this person is a qualified doctor?’ is the same as the answer to the question ‘how do we know that this is a tiger?’ In short, assessment is about recognition, about trying to figure out how we know.

knowledge is a type of perception, which we call ‘recognition’, and knowledge serves as the justification for other things, including opinions and beliefs

1st QUESTION_What are we recognizing? Learning success bit also environment/ resources/ facilitators. Assessment and recognition is a total package.

2nd QUESTION_What counts as success? We use test scores, standardized tests etc. but this is unreliable in the long run. Another thing is competencies, but what is a competency? So, this question backfires. And then there is task completion. Was the person able to do what was asked successfully? In informal learning we are not trying to amass a body of knowledge about some content especially in this world where things may change in one week, rather to get something done.

In the assessments of online environments we often fall back on the Kickpatrick Model: Level 1: Reaction, Level 2: Learning, Level 3: Behavior, Level 4: Results (this categorization ranges from satisfaction from the amount of knowledge retained to whether you applied it in the workplace/ whether it actually improved performance in the workplace) and some people add Level 5: Return on investment. A lot of MOOC evaluation is process oriented. Did the person attend all classes? Did the person complete the course?

3rd QUESTION_Who decides? This is where is the original MOOCs challenged the status quo. David Cormier suggests that what counts as success is what you define it to be.

4th QUESTION_How do we know? How do we make the actual evaluation? Is this a private or a public process? How do we communicate that knowledge? That’s the role of the degree.

Two major technical approaches to assessment: competencies and competency frameworks and the badge infrastructure. Neither of these can provide satisfactory answers.

Competencies_Knowledge skills/ abilities and behaviors that contribute to something: individual or organizational performance, successful learning living and working, highly effective performance in a job. 

USA, Advanced Distributed Learning intiative (ADL): Competency and Skills System (CaSS). This is a mechanism for tracking and recording learning activities. There is xAPI that defines how the activity records are created, how they look like and how they are stored in learning record stores for analysis and evaluation. This is kind of neutral in regard to what a competency is; it allows different learning tools to create these records; it is something that can be used in a generic way as part of learning analysis.

Badge_A specific token given to a person in recognition of the satisfaction of the proof as specified in a competency definition. The badge is the symbol of the process of assessing some competence. In a LMS or learning environment the infrastructure is created by a badge API which describes the work flow of the process.  

Badges could be a proxy of what could be a recognition of some sort.

The next generation of learning technology will have some mechanism for generating recognition entities. In grss this generation mechanism is considered as a loose association of three key elements:

  • Modules: it describes the skills intended to be captured by learners, it is a complex entity: knowledge and skills are complex, it has different parts and components (videos, summaries, posts from participants). Knowledge is not a bunch of statements; it is this messy, networky kind of thing that we try to get a handle on.
  • Tasks: it associates the performance required in order to demonstrate comprehension for understanding of the module. it produces an outcome, an artifact of that comprehension
  • Badges: it is the mechanism for bringing the association together, it represents the successful completion of a task.

In an open and distributed network it is allowed and indeed expected that all of these things would be developed by multiple participants. The idea of learning a discipline is not about one person defining all these things, it is all of the community defining these things.

In this kind of course with this understanding of assessment and recognition, knowledge is in the doing itself (meaning is use). The knowledge is created by our performing the tasks (and we are performing the tasks by manipulating or creating new parts of  this overall linked data that constitutes the disciplinary domain in question).

Ultimately we are going to use actual AI based assessment of competency models. The danger is that these automated systems would start confusing incidental characteristics with proficiency. Another danger is that any personal data can be hacked and distributed over the web. Decentralized network technology can help supersede this because it will be the technology itself that will provide trustworthiness.

In the near future, evidence of success will not be a degree, but a job offer (not a career but a short contract).

Thoughts on Web 3.0 (E-Learning 3.0)

Here are some thoughts about the previous post I’ve made. I’ve been thinking about the decentralized web and its repercussions. Having tried networked learning for some years now, I’ve noticed how despite the multiplicity of resources available for each course, learners always tend to seek the arguments that ground their own research objectives. There is a certain bias in their decisions to pursue this tool or the other: it looks more that they are looking for ways to support the views that they have already formed or to drive their research with means they are familiar with.

Up to now I’ve considered this to be a true value of networked learning. But when you zoom out of the strict limits of an academic learning process I see that this alone cannot be enough. There is a certain kind of responsibility in one’s actions: despite our desire to prove our point there must be something more. And that has to do with understanding the other, who the other is and why his/her reality is different. So, eventually choosing which way to go, doesn’t simply refer to how we’d like it to go but as offering another perspective on a much larger scaled conversation with others. Our views in this framework are not just personal preferences but become political in the sense that they relate to what is there even if that is different from us.

So to talk about a distributed of Web means that our learning should not be limited to sources or resources that we relate to somehow, but a new type of being with others where we are not afraid of conflicting perspectives and where we can establish a mutual agreement on staying connected despite our different views. I know diversity is one of the founding stones of connectivism, but here the notion of otherness becomes crucial in our understanding of the world. This is not diversity for the sake of argument; in the scale of the web diversity is key to democracy. It takes another kind of ethos than the one we have now and it would take a lot of effort to create the conditions necessary for this to work.

Why Web 3.0?


The graph refers to the formal properties network and the network to the physical properties of the graph.

The beginning of the Web: It started out as a decentralized network of interconnected servers, overtime it became dominated by platforms like google. The original web was hard to use. They needed an index, bookmarks etc. The people who were on the internet began creating indexes, library things like that. It was the domain of academic libraries. Overtime the human activity of indexing the web was replaced by the computational functionality of search engines. These began to be used not just for indexes but also articles, messages, journals. So, the platform gradually became the predominant thing. Web 2.0 was born and so was E-learning 2.0 and at some point we gave up on the idea of distributed web. This brings us to today when these platforms have become the “source of truth”.

Netscape was the standard bearer for Web 1.0, Google is most certainly the standard bearer for Web 2.0. While the former was based on old software, the second appeared as an application used to manage data. So Google developed both as a set of tools and specified data (O’ Reilly) 

What has come to be known as Web 3.0 is a response to this. It is the idea that each of us should be able to share our data directly with each other through special protocols. The graph is the conceptual basis for Web 3.0 networks. It’s actually the name of a software library written in java script to support data sharing and interaction by means of the block chain network, the ethereum block chain network. The idea here is that we have this distributed web our data recorded in this block chain and then we use this for contracts, applications and whatever we want to do with this data. You don’t have to use block chain to get to distributed networks. The block chain offers an answer to a key question: why should I believe anything that I hear over this network?

Types of networks

  • Social Networks: made up of people connected by means of friending or following and interacting by means of texts or messages etc.
  • Neuron Network: made up of neurons or in a computer by artificial neurons, interacting by means of pins or signals etc.
  • Financial Network: made up of web ids, people, it may have balances of various sizes: coins, tokens, it is connected to transactions, contracts recording a transaction in a block chain
  • Semantical Network: such as the web, where what we have resources, people, books, libraries, institutions etc, and they are connected conceptually and they interact with logical relation with each other. Block chain is the latest from a series of conversions from centralized mechanisms to decentralized ones offering new dimensions to sth that has been around for a while.

Networks/ Graphs are important because they show connections and not just relations (relations is different from connections: when a change of state in one node can result to a change of state in another node). Graphs are also important because as a collective entity they constitute a distributed representation of a state of affairs. There is no specific place where an idea is located. The state of affairs is itself knowledge.

web 3.0

What’s changed in E-Learning 3.0 is the question what makes it knowledge? Because it is a just a set of connections. What is the source of truth? What grounds ontologies and trust? These are the questions that led us to the use of platforms in the first place. The platform became the source of truth. But this is not always the case: first because there are multiple competing platforms, second because the platforms are subject to being manipulated by bad actors. Web 3.0 manifests a dissatisfaction with that solution, a distrust of platforms and centralized authority. Therefore it proposes to incorporate elements of identity, community in order to create what we might think of as a shared graph.

Remembering is not cognitive decoding but a physical process based on experience and perceiving. To learn is to experience through all facets of perception: sensation, reflection and interactivity.

The graph is the conceptual basis for Web 3.0 networks. The graph is a distributed representation of that state of affairs created by our interactions. The graph itself is at the same time the outcome of these interactions and the source of truth about these state of affairs. The graph is not a knowledge repository but a perceptual system that draws on the individual experiences and contributions of each node. This is why each node is important. This informs not only what we learn but also how we learn, creating new learning contents, new forms of learning, new literacies required.

Notes from E-Learning 3.0 Video on Graphs available here

Images available here and here

ID Graph, E-Learning 3.0/ 1st draft


So, here I am, first attempt. I thought I’d show the complexity of being a. an architect, b. in Greece, c. during crisis. Graph shows a multiplicity of roles and some of their interconnections (dotted lines): for example freelance self-employment leads to a certain amount of knowledge in construction which leads to an educational position in building technology in two universities. Same thing for my PhD research on arch education which informs and is informed by the studios I’ve been teaching in NTUA etc.

Memo: color orange indicates roles; color blue indicates the institutions I have been involved with in the past 4 years (public sector galaxy); color green shows the courses I’ve been teaching and finally yellow color shows all the activities related to my PhD research and studio(private sector galaxy). Thicker lines show first degree/ direct affiliation, thinner lines show connections, dotted lines show mediated interconnections (causal). Vectors indicate what informs what (also causal). Blogging id is both yellow as a writing activity and orange as an autonomous role.

I used cacoo to draw this graph. I’ve discovered it recently and it stole my heart.

Identity, Personality & Agency (E-Learning 3.0)


One needs it to be oneself; yet being oneself solely on the strength of one’s free choice means a life full of doubts and fears of error … Self construction of the self is, so to speak a necessity. Self confirmation of the self is an impossibility’(Bauman 1988:62).

Identity: sharing an identity suggests some active engagement on our part (…) We choose to identify with a particular identity or group (…)  identity requires some element of choice and awareness on our part (…) late modernity suggests that identity matters more now because we have more choice (…) Identity is marked by similarity, that is of the people like us, and by difference, of those who are not (…) symbols and representations are important in marking the ways in which we share identities with some people and distinguish ourselves as different from others (…)  identities are necessarily the product of the society in which we live and our relationship with others. Identity provides a link between individuals and the world in which they live. Identity combines how I see myself and how others see me. Identity involves the internal and the subjective, and the external. It is a socially recognized position, recognized by others, not just by me (…) The link between myself and others is not only indicated by the connection between how I see myself and how other people see me, but also by the connection between what I want to be and the influences, pressures and opportunities which are available (…) The concept of identity encompasses some notion of human agency; an idea that we can have some control in constructing our own identities (…) identities are not fixed and constant; they change too (…) The body is also an important component of personal identity (…) identity is forged in the social sphere is located within temporal relations; a sense of the past, present and future haunts identity-work and identity practices (…) The inter-relationship between past, present and future in the on-going work of developing an identity suggests that who we are, what we do and what we become changes over the life course and furthermore, the work of identity remains fragile and unstable to the point where settlement is unachievable (…) Something as ordinary, everyday and ubiquitous as talking to others becomes central to defining oneself and one’s place in the world (…) Volsiniv identifies two poles: the ‘I-experience’, which tends towards extermination as it does not receive feedback from the social milieu; and the ‘we-experience’ which grows with consciousness and positive social recognition (…) identity is confirmed through processes of social recognition and challenged through processes of misrecognition. Identity formation from this perspective remains structured through the identification of processes of ‘sameness and difference’ (…) it is possible to see identity as relational – formed and played out in relation to those who are similar and those who are different (…) Identity can be seen as multiple: spoken through and in dialogue with a range of social categories and positions (…) Significantly, identity is contextually specific

Personality: the sort of person I am (…) it describes qualities individuals may have, such as being outgoing or shy, internal characteristics

Agency the degree of control which we ourselves can exert over who we are



Kehily, M. J. (2009). What is identity? A sociological perspective. In: ESRC Seminar Series: The educational and social impact of new technologies on young people in Britain, 2 Mar 2009, London School of Economics, UK.

OpenLearn, Identity in question: What is identity? Retrieved from here

Image: Facial casts of Nias islanders, J.P. Kleiweg de Zwaan, 1910, Rijks Museum (personal collection)

Where do trees come from? Graphs!


Trees start from a root node and might connect to other nodes, which means that could contain subtrees within them. Trees are defined by a certain set of rules: one root node may or may not connect to others, but ultimately, it all stems from one specific place. The tree follows one direction and cannot have loops or cyclical links.

Graphs are non linear structures: their data doesn’t follow an order. Trees will always be graphs, but not all graphs will be trees. Graphs do not have a concept of a root node. They can have a direction or not or they could have some links that have direction and others that don’t. Every graph must have at least one single node. (a graph with one node is called singleton).

Edges (sometimes referred to as links) can connect nodes in any way possible. Edges are what differentiates graphs. There are two types of edges: a edge that has a direction or flow, and an edge that has no direction or flow. We refer to these as directed and undirected edges, respectfully. In a directed edge, we can only travel from the origin to the destination, and never the other way around (digraph). However, it’s an entirely different story with undirected edges. In an undirected edge, the path that we can travel goes both ways. That is to say, the path between the two nodes is bidirectional, meaning that the origin and destination nodes are not fixed.

In mathematics, graphs are a way to formally represent a network, which is basically just a collection of objects that are all interconnected. For example, in mathematical terms, we describe graphs as ordered pairs. Remember high school algebra, when we learned about (x,y) ordered pair coordinates? Similar deal here, with one difference: instead of x and y, the parts of a graph instead are: v, for vertices, and e, for its edges. If our graph has more than one node and more than one edge that ordered pair — (V, E) — is actually made up of two objects: a set of vertices, and a set of edges. The “unordered” part is really important here, because remember, unlike trees, there is no hierarchy of nodes.

Facebook, a massive social network, is a type of graph. Twitter, on the other hand, works very differently from Facebook. I can follow you, but you might not follow me back.


Vaidehi Joshi, A Gentle Introduction To Graph Theory. In Medium, Retrieved from here