E-Learning 3.0 Course Synopsis

This is a one-page synopsis of the E-Learning 3.0 course. View the course here.

-1. Getting Ready

Connectivism is based on the idea that knowledge is essentially the set of connections in a network, and that learning therefore is the process of creating and shaping those networks. A connectivist course is focused on developing two types of knowledge: personal knowledge, your own network of ideas and beliefs, which is shaped by activities and experiences; and social knowledge, which is the public network of people and institutions, which is shaped by communication and interaction.

The MOOC environment is designed to support both types of learning. There isn’t a single classroom or learning management system; instead the course itself is design to create connections between individual websites or blogs and to create a flow of conversation and cooperation across that network. Ideally, course participants will have tools to manage their individual knowledge networks as well as means to interact through social networks.

Course content consists of digital media created by and shared among the participants. The role of the instructors is to seed the course with web-based resources (such as this page), references and background reading, presentations bout some of the core course contents, tasks and activities, and audio or video discussions with guests or course participants. To this, participants are expected to add their own contributions, and to share them openly with each other, and through this practice build their own knowledge and learning communities.

The objective of the course isn’t to present some body of content to be learned or remembered by participants. Each person enters the course with their own learning objective. In a massive open online course there will be more content than any individual can read or view, let alone remember, and as a result, the experience of each person in the course is unique, and the interactions are driven by each person’s individual perspective on the material.

The learning in a connectivist course is emergent; it is not defined and transferred or transmitted; rather it is created through the process of individual experiences and interactions. It is something new, different for each person in the course, and in a broader, more social sense, an outcome of the course as a whole. 

0. E-Learning 1 and 2 - Week of October 15

The premise of this course is that we are entering the third major phase of the world wide web, and that it will redefine online learning as it has previously. The first phase of the internet as it was originally developed in 1994, based on the client-server model, and focused on pages and files. The second phase, popularly called Web 2.0, created a web based on data and interoperability between platforms. In what is now being called web3, the central role played by platforms is diminished in favour of direct interactions between peers, that is, a distributed web.

The first generation of the web was dominated by content management services (CMS) ranging from simple web page servers to database-driven CMS. The Learning Management Service (LMS) is a CMS designed with an educational intent. The second generation of the web saw the rise of services that allowed people to exchange content and media with each other. This course introduces the third generation of the web, sometimes called web3, and the impact on e-learning that follows. In this third generation we see greater use of cloud and distributed web technologies as well as open linked data and personal cryptography.

Web3 content looks a lot like it used to look; there will still be files and pages and video. But these media will be accessed through distributed content networks, secured through hash algorithms and digital keys, and available dynamically with contents changing in real time. We won’t be accessing publications per se, we will be accessing distributed applications that in turn make data streams available to us. These data streams may be digital currency, e-sports broadcasts, government statistics, or learning applications.

Learning in web3 will be a process of navigating through these distributed resources, blending and recombining them, and applying them to authentic challenges and real-world problems. It will be based on one hand on personal skills development informed by one’s own interests, and on the other hand the development of competencies within an employment and performance support network, working and learning cooperatively with colleagues and peers.

The Massive Open Online Course (MOOC) lies somewhere between web 2.0 and web3. It anticipates the desire for a decentralized network of resources and individual in course development and management, but it relied on web 2.0 tools such as social networks and e-learning platforms.

So while E-Learning 3.0 is in many ways anticipated by connectivist forms of learning, the tools, processes and outcomes are all new. The course becomes a set of linked data sources where the links are defined not only by educational institutions but by participants and learners and the sources are drawn from, and delivered into, multiple environments.

1. Data - Week of October 22

This week the course addresses two conceptual challenges: first, the shift in our understanding of content from documents to data; and second, the shift in our understanding of data from centralized to decentralized. The first shift allows us to think of content - and hence, our knowledge - as dynamic, as being updated and adapted in the light of changes and events. The second allows us to think of data - and hence, of our record of that knowledge - as distributed, as being copied and shared and circulated as and when needed around the world.

There is a history of decentralized data applications on the web, from web servers to email to the peer2peer applications of the 1990s and 2000s such as Napster and Gnutella. Yet their primary application was limited to file sharing and broadcasting, and genuine peer-to-peer interaction online was limited to platforms such as social networks and learning management systems. This week we look at new technologies enabling safe and secure interaction and data sharing based on authenticated personal content storage and linked data networks.

Linked data brings into play a wide range of content sources not previously considered, or available, for collecting and sharing. We are beginning to see how we generate geographic data as we travel, economic data as we shop, and political data as we browse videos on YouTube and Tumbler. A piece of media isn’t just a piece of media any more: it’s what we did with it, who we shared it with, and what we created by accessing it.

Learning in dynamic distributed data networks becomes a process of creating and curating our own data. We will think of our learning resources as something we create, own and share, and not just as rentals from the college textbook or online publishers. It also becomes a process of being able to comprehend data, to be able to look at representations of data though dashboards and visualizations, and to be able to recognize patterns and draw conclusions.

Learning with data isn’t the same as learning with books. It’s interactive, immersive and engaging, a process of learning how to perceive and comprehend rather than to decode and store. We need to think of knowledge as recognition rather than remembering and learning as a process of adjusting and adapting to changing circumstances as they present themselves anew each day.

2. Cloud - Week of October 29

The joke is that “the cloud” is just shorthand for “someone else’s computer.” The conceptual challenge is that it doesn’t matter whose computer it is, that it could change any time, and that we should begin to think of “computing” and “storage” as commodities, more like “water” or “electricity”, rather than as features of a type of device that sits on your desktop.

The technology that enables cloud-based computing begins with the concept of server virtualization, and applications such as VMWare or Parallels, and progresses through a range of increasingly sophisticated computing containers created using programs like Docker and run using services like Amazon Web Services or Digital Ocean. It was decentralized and distributed computing that allowed MOOC companies like Coursera and EdX to serve thousands of students at a time. Today, though, this capacity is available to everyone.

These services are created and provisioned through a maze of new types of file with names like ‘Dockerfile’ or ‘Vagrantfile’ or ‘YAML’. As a result, we are now able to see entire computing environments as pieces of context we can exchange back and forth with each other. One example of this is the Jupyter Notebook, which is essentially a text document - a notebook - with live functioning computer code inside it that you can modify and run again as often as desired. Alternatively, containers with entire websites are as easy to download and deploy on your as clicking a link or opening a PowerPoint.

These new resources allow us to redefine what we mean by concepts such as ‘textbooks’ and even ‘learning objects’. By putting powerful applications into the hands of students we create new possibilities for manipulation, visualization and creativity. Students are now able to not only create text, music and art, but also to edit and create new tools to create text, music and art. They will be able to directly experience the relation between algorithm and outcome, or between mathematics and music, as the case may be.

More significantly, networks of containers, each performing its own specialized function, will allow teachers and students to work cooperatively, creating distributed data networks and services. Learning a concept as simple as “load balancing” - a mechanism where requests to a single web page are send to different computers to spread out the load - is a doorway to being able to imagine and understand deep and complex service networks.

3. Graph - Week of November 5

The graph is the conceptual basis for web3 networks. This concept will be familiar to those who have studied connectivism, as the idea of connectivism is that knowledge consists of the relations between nodes in a network - in other words, that knowledge is a graph (and not, say, a sequence of facts and instructions). Graphs, and especially dynamic graphs, have special properties, the results of which can be found in social network theory, in modern artificial intelligence, and in economic and political theory.

Previous work in graphs on the internet have had to do with the semantics of graphs; hence, for example, we say the development of things like the semantic web and the web of trust. These have been limited successes. In web3 the connections between nodes (the “edges”) are created using cryptography, thus creating chains or trees with incorruptible connections. One example of this is the Merkle Tree, where branches contain hashes of the leaves, and trunks contain hashes of the branches. Graphs - such as the directional acyclic graph (DAG) - can be created in this manner.

The data structures we can build using these technologies have created a new type of content. One well-known example is BitCoin, which is based on the recording of transactions in a blockchain, which is essentially a has chain. Another example is the collection of updated versions of software stored in GitHub, which manages version control and software replication using DAGs. Attribution networks, conceptual networks, websites - all these can be represented using graphs.

In connectivism we have explored the idea of thinking of knowledge as a graph, and of learning as the growth and manipulation of a graph. It helps learners understand that each idea connects to another, and it’s not the individual idea that’s important, but rather how the entire graph grows and develops. It helps us see how a graph - and hence, knowledge - is not merely a representational system, but is rather a perceptual system, where the graph is not merely the repository, but a growing and dynamic entity shaped by - and shaping - the environment around itself.

Graphs and graph theory demonstrate in a concrete way how everything depends on something else, and helps us place our understanding of ourselves, or knowledge, and our work into a wider context. Hash graphs take this a step further by illustrating fundamental knowledge-creation mechanisms as cloning, forking, versioning and merging.

4. Identity - Week of November 12

Identity is one of the deepest problems of philosophy and one that runs trough the history of education like a single thread. In this course we look at identity relatively narrowly, asking how we know who someone is, how we project ourselves on the internet, and how we can be safe and secure. Even so, our relation to technology and to each other can be seen to change. In the era of websites and content management systems, we were the clients. In the era of platform-based social networks we were the product. What do we become in a world of artificial intelligence, linked data and cryptographic functions?

One of the key changes with web3 technology will be the way we identify ourselves online. Anonymity and password-based usernames will be more difficult to sustain. We are already in a world of biometrics and two-factor authentication, but there are weaknesses in the system (and an endless stream of successful hacks to underline that problem). Cryptographic keys - either digital or physical - will become the norm, but this gives us a permanent identity that not only secures our data, it is our data.

We were the client, we were the product - are we, at last, the content? We are the thread that runs through an otherwise disconnected set of data, and knowledge about ourselves, our associations, and our community will create an underlying fabric against which the value and relevance of everything else will be measured. Instead of demographics being about quantity (sales charts, votes in elections and polls, membership in community) we will now have access to a rich tapestry of data and relations.

If this becomes the case, then we will have an unparalleled opportunity to become more self-reflective, both as individuals and as a community. The “quantified self” will give way to the “qualified self” and ultimately to the “connected self” as we begin to define ourselves not merely by simple measures of ethnicity, language, religion and culture, but through thousands of shared experiences, affinities, and inclinations. Evidence for this trend already exists and can be found through the exploration of expression of communities and culture online.

Our new identities have the potential to be an enormous source of strength or a debilitating weakness. Will we be lost in the sea of possibilities, unable to navigate through the complexities of defining for ourselves who we are, or will we be able to forge new connections, creating a community of interwoven communities online and in our homes?                                                                                                                                                           

5. Resources - Week of November 19

 

From its earliest days the internet has pitted a philosophy of sharing against more consumer-driven models of content consumption. Usenet, mailing lists, websites and file transfer services facilitated the easy exchange of ideas and information. Since those early days the web has been increasingly locked down, and the once-seamless interaction between people and data has been locked more and more behind paywalls and content silos. Web3 is to a large degree a reaction against this, and developers across the internet are working on a new infrastructure that will defy the efforts to enclose the commons.

These technologies build on some of the ideas underlying the file sharing networks of the past but add elements that address their vulnerability to centralized control and regulation. One example of this is the Interplanetary File System (IPFS) and its cousin Interplanetary Linked Data (IPLD). Instead of relying on internet addresses to locate content, these new file sharing systems use the hash of the data or content as an address, enabling the data to be distributed across the cloud, accessible from the nearest convenient source.

We have already seen more transitional contents, such as books, media and music, being distributed through IPFS. Similar technologies are being deployed to support more complex content, for example, distributed applications (dApps), subscriptions and lists, contract networks, and even distributed organizations such as the DAO (Decentralized Autonomous Organization). With no central point of origin, there is no means to control these types of content, which raises questions about both their legality and their vulnerability.

In education, these concepts are used to introduce a new type of Open Educational Resource, Content Addressable Resources for Education (CARE) along with the associated concepts of CARE Packages and CARENet. These resources - which may be anything from courses and programs to event access and recordings to some of the advanced learning applications described above - will be packaged and distributed across a content-addressable network, whereupon they become permanently open, with no possibility of being enclosed by commercial services, both by virtue of their immutability, and by virtue of the fact that the process of hash addressing guarantees that the content that was created is the content that was received.

The concept of Content Addressable Resources for Education addresses the question of the sustainability of open educational resources, as it is the distributed network of teachers and learners that sustains them through their use. It also creates mechanisms for the creation of resource graphs linking data, media, software and people, redefining our idea of an open course (and open pedagogy) as something dominated not by licenses and institutions, but by people and practice.

6. Recognition - Week of November 26

The question is often asked, how do we know a course has been successful? How do we know what someone has learned? These are underscored by the deeper question of whether we can trust in the education of our mechanics, doctors, engineers and pilots. The problem is intractable because there is not clear agreement on what counts as success. The different outcomes from learning events can be tracked and measured in any number of ways. And all the while, there is the danger of bad actors - of those who cheat on tests, fake certificates, or misrepresent their qualifications.

In recent years we have seen renewed focus the idea of competencies and competency definitions. The American Advanced Distributed Learning initiative has launched the Competencies and Skills Systems program, for example, part of their wider Total Learning Architecture. There are numerous competency definition standards, everything from Australia’s National Competency Standards to the NIH’s Nursing Competency standard. Activity tracking has been formalized by xAPI and records are stored in Learning Record Stores (LRS). These systems are gradually migrating toward a decentralized linked data model, as exemplified by the suggestion to develop a blockchain network for badges and certifications.

As a result, we need to think of the content of assessments more broadly. The traditional educational model is based on tests and assignments, grades, degrees and professional certifications. But with activity data we can begin tracking things like which resources a person read, who they spoke to, and what questions they asked. We can also gather data outside the school or program, looking at actual results and feedback from the workplace. In the world of centralized platforms, such data collection would be risky and intrusive, but in a distributed data network where people manage their own data, greater opportunities are afforded.

While no doubt people will continue to collect badges, degrees and certificates, these will play a much smaller role in how we comprehend how and whether a person has learned. The same data set may be analyzed in any number of different ways and can be used by learners as input to evaluation services that use zero-knowledge methods to calculate an individuals status against any number of defined (or implicit) employment or position requirements. The skills of a traditional learner - passing the test and meeting the expectations of a teacher - will be replaced with a more concentrated focus on developing a unique set of skills and capacities (and a body of work to support that).

It might be said that the certificate of the future will be a job offer. Already software is being developed to map directly from a person’s online profile to job and work opportunities (this is how one of our projects, MicroMissions, works in the Government of Canada). These profiles today are unreliable and superficial, but with trustworthy data from distributed networks we will be able to much more accurately determine the skills - and potential - of every individual. And I think we’ll be surprised by what we see.

7. Community - Week of December 3

The traditional concept of community was built on sameness, on collections of people from the same family, speaking the same language, living in the same place, believing the same things. This concept was challenged by a range of social and political reforms through the last few centuries, and while some wish to return to that simpler time, the fact is that the fundamental challenge to community is to make decisions on matters affecting everybody while leaving to individuals, companies and institutions those matters not effectively managed by consensus. In recent years, however, this concept of community has come under challenge, with a broad social inability to even agree on basic facts and events.

In fact, as theorists such as Simon Blackburn argue, each of us can determine for ourselves whether something is true or not, at least to a certain degree. Are two numbers the same? Is one thing bigger than the other? Yes, there is a possibility of error, but the deeper problem is posted by bad actors - people who deliberately misrepresent the truth for their own benefit. The capacity to withstand the influence of such bad actors is known technically as Byzantine Fault Tolerance, and there are different approaches to achieving consensus even when there is no certainty, based on the general common sense of the rest. While not defining truth as consensus, the problem of truth, at least from a community perspective, is a consensus problem.

This makes the mechanisms we use to interact and reach consensus particularly important. For example, even if we have a chain of verified and trustworthy facts, validated by previous consensus and guaranteed by encryption technology, how do we choose between competing chains? Digital currencies such as Bitcoin and Ethereum use a “proof of work”. This makes it too expensive to create a fake chain from scratch, but at the cost of inefficiency and enormous energy consumption. Other types of content create other types of consensus: “proof of stake” relies on guarantees of resources or assets; “proof of authority” depends on certification or validation, and “oracles” depend on widely observable and incorruptible sources of data.

What this teaches is that community and consensus are about more than voting and about more than having power. What is required for a community to work is not merely control, but agreement on the part of the members of the community. Underlying this is a respect for law, institutions and processes, and when these break down, and when consensus is lost, it is very difficult to restore. Fostering an understanding the importance of these processes, and the costs of not being able to establish them, is a fundamental goal of education. This can be accomplished best (and maybe only) through the process of engaging in them and developing community and consensus in the classroom.

The critical literacies in a society run deeper than reading, mathematics and science. They include pattern recognition, perspective and context, inference and reasoning, and practical application and communication. They include not just being able to communicate with each other, but to be able to build and create. Consensus, ultimately, is a question of stigmergy, and we will look not only how it is created, but also how it is undermined (think, for example, of ‘dark patterns’).

9. Experience - Week of December 10

It is a truism that we learn from experience, and yet creating a role for experience in learning has been one of the most difficult problems in education. And so much of education continues to rely on indirect methods depending on knowledge transfer - reading, lectures, videos - rather than hands-on practice and knowledge creation. The emergence of the web, YouTube, Web 2.0 and social media was a great step forward, assigning a role for creativity in the learning experience. But experience, ultimately, requires an openness that media platforms were unable to provide.

New technology is beginning to combine the ability of teachers and role models to model and demonstrate successful practice and the need for learners to practice and reflect on their learning in that environment. Content distribution networks and live streaming are transforming real-world events into hands-on learning experiences. A good example of this is the live-streaming platform Twitch and especially games like Fortnight, in which players become spectators, and back again, over and over. And using applications like xSplit or Open Broadcaster Software individuals can make their experiences part of the learning experience shared by others.

It is a model in which the creation of the content becomes a part of the content itself. We see this with the recent self-shredding art by Banksy or the inside look at how the single-scene time-lapse sequence in Kidding was filmed. Some artists have made working openly part of the act - Deadmau5, for example, showing how electronic music is produced. Being able to see and experience how something is created is a key step on the way to becoming a creator oneself, and becoming a creator, in turn, becomes a key part of the learning experience.

The difference between previous iterations of learning technology and that which we are experiencing with E-Learning 3.0 is that these creative activities become distributed and democratized. Just as multiple authors can edit Wikipedia articles or work on code in GitHub, participatory learning media enables learners to interact creatively without management or direction; the outcome is a consensus determined not by voting but by participation. Experience in learning changes the relation between teacher and student from one of persuasion (and even coercion) to one of creativity, co-work, and construction.

Workplaces, and especially distributed workplaces, are beginning to create self-organizing consensus-based co-production networks. Early awkward and exploitative platform-based efforts such as Uber and Airbnb are giving way to more sophisticated and equitable network alternatives such as Steam, Koumbit and Medium.

9. Agency - Week of December 17

Each of the major developments in the internet - from the client-server model to platform-based interoperability to web3-based consensus networks - has been accompanied by a shift in agency. The relative standing of the individual with respect to community, institutions, and governments was shifted, for better or worse. Each stage in technological development is inspired by social, political and economic aspirations, and understanding the next generation of learning and technology requires understanding the forces that shaped them. So we close our enquiry with a consideration of issues related to power and control, to peace and prosperity, to hopes and dreams.

McLuhan said that technology is a projection of ourselves into the community, so we need to consider how human capacities are advanced and amplified in a distributed and interconnected learning environment. Our senses are amplified by virtual and augmented reality, our cognitive capacities extended by machine vision and artificial intelligence, and our economic and social agency is represented by our bots and agents.

We are the content - the content is us. This includes all aspects of us. How do we ensure that what we project to the world is what we want to project, both as teachers and learners? As content and media become more sophisticated and more autonomous, how do we bind these to our personal cultural and ethical frameworks we want to preserve and protect? These are tied to four key elements of the new technological framework: security, identity, voice and opportunity.

What we learn, and what makes learning successful, depends on why we learn. These in turn are determined by these four elements, and these four elements are in turn the elements that consensus-based decentralized communities are designed to augment. Learning therefore demands more than just the transmission or creation of knowledge - it requires the development of a capacity to define and instantiate each of these four elements for ourselves. Our tools for learning will need to emphasize and promote individual agency as much as they need to develop the tools and capacities needed to support social, ;political and economic development.

It is difficult to imagine a world in which education is not solely about knowledge and skills. But as we transform our understanding of learners from social and economic units to fully realized developers and sustainers of the community as a whole, it becomes clear that education must focus on the tools and capacities for agency along with the knowledge, culture and skills that sustain them.