Connectivism

I continue to grapple with definitions of information, knowledge, learning, and meaning. The more I read and seek clarification, the more murky my views. I’m now at the stage where I’m starting to define knowing simple as “being aware of an object/idea in a current context”. Tomorrow, the object/idea may be in a different context, and that will influence knowledge. It follows that a large part of knowledge is derived from the context or view of an idea/concept/object. Does a concept then have no intrinsic meaning? For example, I come from a pacifist faith and methodology (a concept constantly challenged in today’s world). Is non-violence always “true”? Or, if one holds to another view – is conflict or violence always the “final answer”? What does this say about concepts or ideas that we use to shape and form society? Is it relative? If so, does it then mean that our own morality is shaped by context? Can we reach “shared understandings” when knowledge is seen primarily as a function of subjective interpretation or perspective?

In the past, I’ve defined the debate of information, knowledge, meaning, and learning as being one of progressively greater intelligence applied in moving up the scale. Information (defined as data with some organizing scheme applied) is the starting point. Knowledge is an understanding or comprehension of information’s explicit and tacit domains (i.e. information in context and internalized). Meaning is the highest element in the pyramid. Meaning is an understanding or recognition of the impact of knowledge. The Dow Jones daily performance is information. Understanding why Dow Jones rose/fell is knowledge. Comprehending the impact of Dow Jones’ daily performance is a function of meaning. What does it mean? Who will be impacted? How does it affect my retirement goals? How does it reflect on national competitiveness? How does this “meaning” link to other forms of knowledge I possess (globalization, government taxation, principles of governing party)?

To see it another way, learning (which is comprised of many domains), at its highest level, is the moment at which knowledge translates to meaning. Unfortunately, we use “learning” as a vague and confusing term. Sometimes we define learning as acquiring a new skill (loading a software program). Other times we define it as an ongoing, informal experience (self-reflection). Or we define learning as a by-product of personal experiences…etc. We use the term “learning” to refer to filling knowledge gaps, increasing personal and organizational competence, increasing self-awareness, and on and on. Few words are more eviscerated of concise meaning than learning. However, if we tentatively view learning as the act of transforming knowledge into meaning (which then suggests that we can do something with (or actuate) knowledge), we can begin to tackle the challenge of perspective or subjectivity.

It seems to me that certain things are innate or certain entities possess intrinsic attributes. Perspective and subjectivity have value only to the degree that they align with these intrinsic values. A simple example: the concept of “forgiveness” is gaining much favor and attention in the field of psychology. It is generally understood that forgiving others who have wronged us is an excellent way of maintaining our own mental health. Forgiveness can be seen as an objective concept (I know I’m walking into very murky waters that require much more contextual information than I’m providing in this short example). A person can have knowledge of the value of forgiveness. Subjectivity comes in how we assign meaning to what we know (or to what might be an existing objective concept). How we personally approach forgiveness is the starting point of personal subjectivity. Context, cognition, and emotion all contribute to how we assign meaning to knowledge. The process is one of degrees, not a “yes” or “no” experience.

This is a simple thought experiment, but it does provide a basis for thinking objectively about the notion of learning and knowledge. I’m comfortable stating that everything we see/do is personally interpreted. In many cases, however, an objective concept exists as a tempering point for assigning value to my subjectivity.

This isn’t to say that all aspects of life are clearly objective or that subjectivity is always a function of assigning meaning to objective entities. Far from it. Many aspects of life, behaviour, knowledge, and learning are subjective. However, I don’t want the presence of subjectivity to exclude the possibility of objective dimensions to our learning and meaning-making.

How we assign meaning to knowledge, or how we design learning for our learners, is derived from our own conceptions of subjectivity and objectivity. The rapid development of information, the continual march of change, and global developments and conflict, are powerful illustrations of the substantial challenge facing educators. I fear that we pull the foundation out from our learners when we don’t provide at least the acknowledgement (possibility) of objective reality. Rapid change does not speak against objectivity. The higher pursuit, in today’s learning spaces, should be the creation of holistic, integrated modes of thinking, knowledge, learning and meaning. We all shape our realities. We all explore and see different parts of the aspects of life that are objective. We all contribute (connected individualism) to the aggregated whole of subjective view points leading to a more complete view of what is and what can be.

Administrators, learning designers, and teachers are facing a new kind of learner – someone who has control over the learning tools and processes. When educators fail to provide for the needs of learners (i.e. design learning in an LMS only), learners are able to “go underground” to have their learning needs met.

This happened in a program I was recently involved in as a learner. An LMS was the main learning tool (which was a good choice for the program – many of the learners valued the centralized nature of communication and content presentation). After a short period of time, however, groups of learners “broke off” from the program and started holding discussions through Skype, IM, wikis, and other tools. Learners selected tools that were more tightly linked to the types of learning tasks occurring. When the learning was content consumption or simple discussion threads, the LMS was fine. As the learning became more social, learners started using tools with additional functionality. The learning required by the instructors – assignments, discussions – still happened in the LMS. But much more meaningful, personal, and relevant learning happened underground – outside of the course.

This was a great example of the foraging dimension of learning – we keep looking until we find tools, content, and processes which assist us in solving problems. Our natural capacity for learning is tremendous. We overcome many obstacles and restrictions to achieve our goals. It’s also an example of the short-sighted nature of some learning programs. The problem rests largely in the view that learning is a managed process, not a fostered process. When learning is seen as managed, an LMS is the logical tool. When learning is seen as a function of an ecology, diverse options and opportunities are required.

What is the cost of learning “going underground” (i.e. off the radar of the institution)? The biggest impact is that the group of learners no longer has access to the thoughts of the entire group. Small communities form – but are not linked back solidly to the main group. Groups form due to ineffective learning design (tools, content, and process). Second, the organization loses its central role (this isn’t all bad – learners should have the capactity to play and mess around with new tools – an area for experimentation is very valuable…but the core learning requirements should be provided by the school). Learners who are less likely to experiment receive a different level of value from the learning process. Perhaps this is fine…as much of the underground learning is an “add on” to the main intent of the program. Still, for some, as was evidenced in the program discussed earlier, the move to underground resulted in frustrated learners who felt that they had missed part of the conversation.

This happens consistently in K-12, college, university, or corporate learning. When teachers don’t provide tools, learners take their learning process to new (user-controlled) spaces. Those who are most passionate and informed are the learners who are most likely to create new spaces of learning – and as a result, leave with much of their best insight.

Some types of learning (particularly brainstorming) are often best handled through small private groups. This isn’t the concern here. The concern is that the failure of the organization to provide tools results in a less effective learning experience for all learners (i.e. we aren’t privy to the numerous, valuable, “other space” conversations). Learners should have expectations about the type and quality of the experience. Many people will find solutions to inefficiencies, but others may simply continue trying to use the wrong tools for the wrong task. It is the responsibility of the school/college/university to provide the ecology in which learning can occur.

I was involved in a recent conversation where an individual asked for clarification on about whether connectivism was an actual learning theory…or if it was more a radical re-conceptualization of how learning happens in today’s digital environment. I chose the safe answer and stated that I intended it to be both.

To elaborate, I’ll use context to refer to the new environment in which learning is happening (and in turn impacts any theory of learning) and method to refer to a new way, or metaphor, of learning.

Our changing learning context is axiomatic. We see it in any form of information – from newspapers to radio to TV to the internet. Everything is going digital. The end user is gaining control, elements are decentralizing, connections are being formed between formerly disparate resources and fields of information, knowledge is developing rapidly, and everything seems to be “speeding up”. It’s critical that learning theories adequately meet the challenges of this environment. Regardless of how we perceive knowledge and learning (i.e. is it objective? interpreted? subjective?), our theories have to account for the environment in which learning will happen. The learning must link to real life. All three dominant learning theories (behaviorism, cognitivism, constructivism) provide some value at this level, as long as they are able to adjust to the new information context. Connectivism is, in this sense, at least partly an attempt to conceptualize learning as a function of a new context.

Connectivism’s relevance increases when we consider a new method (or metaphor) of learning. The achilles heel of existing theories rests in the pace of knowledge growth. All existing theories place processing (or interpretation) of information squarely on the individual doing the learning. This model works well if the knowledge flow is moderate. A constructivist, for example, can process, interpret, and derive personal meaning from different information formats…as long as the flow doesn’t overwhelm the learner. What happens, however, when information is more of a deluge than a trickle? What happens when information flows too fast for processing or interpreting?

Once knowledge/information flow becomes too rapid and complex, we need to conceptualize a learning model that allows individuals to learn and function in spite of the pace and flow. A network model of learning (an attribute of connectivism) offloads some of the processing and interpreting functions of knowledge flow to nodes within a learning network. Instead of the learning having to evaluate and process every piece of information, she/he creates a personal network of trusted nodes (people and content). The learner aggregates relevant nodes…and relies on each individual node to provide needed knowledge. The act of learning is offloaded onto the network itself – i.e. the network is the learning. This view of learning scales well with continued complexity and pace of knoweldge development.

In a recent article, I provided the information system that provides the foundation for learning:

• Data – a raw element or small meaning neutral element
• Information – data with intelligence applied
• Knowledge – information in context and internalized
• Meaning – comprehension of the nuances, value, and implications of knowledge

I have grappled with a suitable definition of learning for quite a while. In the past I’ve stated that learning is actuated or actionable knowledge (i.e. something we can do). I’ve also alluded that learning informs the “softer” elements – beliefs, attitudes, and perspective (which in turn, result in a change in actions). For some reason, these definitions aren’t satisfying. I believe them to be true in most instances, but they don’t appear to completely explain the attempt and focus of learning.

Recently, I’ve become fascinated with the concept of “meaning-making”. In my current taxonomy of what it means to know, I see the sequence mentioned above: data to information to knowledge to meaning. It is one thing to “know something”, but quite another to understand what it means. We may know certain things about a person/organization/country, but to understand what it means (i.e. what are the implications, the probable outcomes, the need for action) requires a higher level of comprehension. (I’m actually feeling a bit hamstrung by the language I’m using – I keep wanting to come back to the concept of “knowing”, which in itself, is a level down from understanding meaning.)

Currently I see learning as the event that happens when we move from knowledge to meaning or sense-making. Knowing something is great. Knowing what it means moves us to a level where we can act – to support, change, redirect, challenge, or whatever. That brings me back full circle to the original definition I had of learning – actuated or actionable knoweldge…but with a greater focus on “what does it mean”. For some reason that still leaves me dissatisfied.

I’ve talked previously of connected specialization – the notion that we gain greatest value by permitting connections between elements, rather than attempting to upgrade the “whole”. The entire learning network is far more adaptive when an individual element/node is able to learn, and then pass the knowledge back through the larger network. A recent NPR broadcast explores the concept of how our brain is wired in this manner: Learning, Memory, and the Brain. Numerous systems exist within the human brain. These systems are connected but not entirely dependant on each other. Individuals who have suffered some brain injuries may lose certain functions (in the case of the study cited in the broadcast – a certain type of memory), yet retain related but different function.

Many different theories exist which try to explain how we learn. Based on those theories, we have numerous approaches to learning design. The whole field is quite connected (inbred?)…and boring. These theories are strongly slanted to reflect a course-based approach to learning. Courses are effective for many types of learning (especially for learners starting out in a new field). For most of us, however, the bulk of our learning has come from informal methods.

As informal learning gains greater recognition, it’s worth exploring the different learning theories that inform this style of learning. Except for one problem – there aren’t any. Over the last six months, I’ve reviewed a significant number of theories, severely abused Google, and have yet to come across a theory that provides guidance for designing informal learning(IL). Many resources exist for designing communities of practice, but that’s only one type of informal learning. Many organizations don’t focus on IL – they assume that the learners (employees) will find the answer to their problems. Even companies who are advanced in this area often do little more than provide software to blog, ask questions, and try and access the tacit knowledge of others in the organization.

Informal learning is too important leave to chance. But why don’t we have theories that provide guidelines (I imagine “steps 1, 2, and 3″ approaches would be a bit at odds with informal learning) to designing in these environments? Or is the notion of informal learning to vague (free spirited?) and applying increased design is an effort in futility?

Perhaps the challenge with IL is the many different approaches a learner might take (i.e. how can we plan and design for it?). Perhaps even our notion of design is worth rethinking – do we design learning? Or do we design environments in which motivated learners can acquire what they need? Yet if we can’t impose some type of order on the process, is it really design? Will corporations invest in a learning theory that isn’t strongly tied to strategic goals?

I wonder which established processes and systems can inform designing for informal leanring? Complex adaptive systems? Or am I seeking a difficult solution when an easier one exists? Any thoughts?

Lately, I’ve been thinking about sequencing instruction. Various theorists promote sequencing in terms of:

• Moving content from simple to complex
• Moving content forward based on prerequisites
• Meeting goals of learners by advancing goals as content advances
• Linear content presentation (behavioural)
• Adaptive sequencing based on learner needs

As I’ve discussed previously, so much of our learning design is rooted in approaches that may no longer provide value to learners. Most learning is content-focused, with the underlying assumption stating that our ability to navigate content is what generates learning. What if exposure to content isn’t learning? What if content isn’t even required in the learning process? Inquisition and dialogue are the core traits of learning in complex environments, largely replacing content (due to the fact that content is changing rapidly).

In this environment of chaos and shifting core elements (note the changing fields of media, music, and news), what we know (usually thought of as possession of content-based knowledge) is replaced with how we continue to stay current and informed. We often think of this learning process as isolated based on the learner learning a new concept or idea. It’s valuable, however, to see learning as a whole – when one element changes it creates a ripple effect on others. A network image is more completely explains what happens when things change quickly (and at their core). As a node in a network is updated, it creates a ripple effect that alters the network itself – but not the other nodes in the network (this is the concept “connected specialization” – one node advances (hopefully) and impacts the health of the network…but still allows all additional network nodes autonomy to function as they desire).

What does this have to do with instructional sequencing? If instruction is more than content, and learning is about increased development of health within a network as a whole, then sequencing needs to include interaction, dialogue, and network-related functions (i.e. creating a network, growing a network (adding new nodes like content resources, newcomers, masters, correlative/complimentary nodes, etc.). Sequencing in this model still allows designers to incorporate some of the approaches listed above (e.g. simple to complex, linear). While the methodology may be similar, the process of sequencing is extended to include a rich array of non-content based items. Giving learners control to explore the network, interact with nodes of interest, and then continue on in fostering their own network may replace much of the designer’s design process. Learning motivation is not exclusively a function of learner goals…instead (in a network model), motivation is also driven by contextual changes, reactionary to growth or nodes within the network as a whole.

I’ve been reflecting on various definitions of learning and knowledge. Often, knowledge acquisition and learning are used interchangeably. I think they are very different terms (at least when used in the context of what it means to learn today). Acquiring knowledge leaves room for a dormant state (i.e. we know something, but we may not actually do what we know). In contrast, learning involves knowledge acquisition, but is defined by use/doing. When I learn, I’m growing in performance capacity based on acquiring knowledge. If acquired knowledge doesnt’ lead to some type of use, I don’t believe learning has occurred (a changed state of knowledge is only half the process. Our discussions of learning usually acknowledge this half, but fail to account for the equally important “doing”).

Some clarification of terms:

• Data: raw facts, symbols
• Information: Data that has been organized, interpreted, processed and made useful (useful being defined as the criterion for which the data was originally collected).
• Knowledge: information in context (i.e. understanding the significance of information) or information with semantic meaning.
• Learning: actuated (or actionable) knowledge, doing something with knowledge

I’ve received some comments from readers challenging the notion that learning is actionable knowledge. Dwelling on organizational learning, personal knowledge management, social learning, and networked learning, I’m convinced that in today’s environment, learning isn’t learning unless there is an action component.

Epistemology is the study of knowledge. Historically, three broad orientations exist:

• Objectivism – knowledge is external and knowable through experience and sensory perception (empericism) or through rational thought (rationalism)
• Pragmatism – knowledge is intepreted through a model or internal representation.
• Interpretivism – knowledge is constructed and internal.

Each model has value. Yet none are useful in all cases. In certain fields, learning/knowledge can be very much external and our learning is successful once we align our internal representation with reality. In other cases, knowledge is an internal, personally constructed “object”.

I recently encountered the concept of memetics as an additional view of knowledge: “A meme is a cognitive or behavioral pattern that can be transmitted from one individual to another one. Since the individual who transmitted the meme will continue to carry it, the transmission can be interpreted as a replication: a copy of the meme is made in the memory of another individual, making him or her into a carrier of the meme.”

Within the field of knowledge management, the holy grail is the ability to take internal (tacit) knowledge and make it external (explicit). This notion partly expresses my frustrations with many theories – wrong application for the wrong task. Often tacit knowledge is transferred viral-like through stories and shared experiences (pragmatic and interpretivist). Procedurally, how to operate a machine is transferred in an objectivist approach. Nothing is all – each for a proper concept and proper implementation.