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Putting too much structure on advanced-level learners may make learning boring, frustrating, inefficient, and off-target for them.

A curator is an expert learner. Instead of dispensing knowledge, he creates spaces in which knowledge can be created, explored, and connected.

Learning is an eliminative process. By determining what doesn’t belong, a learner develops and focuses his understanding of a topic. The teacher assists in the process by providing one stream of filtered information. The student is then faced with making nuanced selections based on the multiple information streams he encounters. The singular filter of the teacher has morphed into numerous information streams, each filtered according to different perspectives and world views.

Course content is similarly fragmented. The textbook is now augmented with YouTube videos, online articles, simulations, Second Life builds, virtual museums, Diigo content trails, StumpleUpon reflections, and so on.

Fragmentation of content and conversation is about to disrupt this well-ordered view of learning. Educators and universities are beginning to realize that they no longer have the control they once (thought they) did.

However, in order for education to work within the larger structure of integrated societal systems, clear outcomes are still needed.

How can we achieve clear outcomes through distributed means? How can we achieve learning targets when the educator is no longer able to control the actions of learners?

Thoughts, ideas, or messages that the teacher amplifies will generally have a greater probability of being seen by course participants.

Each RT amplifies the message much like an electronic amplifier increases the amplitude of audio or video transmitters.

A curatorial teacher acknowledges the autonomy of learners, yet understands the frustration of exploring unknown territories without a map. A curator is an expert learner. Instead of dispensing knowledge, he creates spaces in which knowledge can be created, explored, and connected.

In CCK08/09, Stephen and I produced a daily newsletter where we highlighted discussions, concepts, and resources that we felt were important. As the course progressed, many students stated they found this to be a valuable resource -a centering point of sorts.

Today’s social web is no different – we find our way through active exploration. Designers can aid the wayfinding process through consistency of design and functionality across various tools, but ultimately, it is the responsibility of the individual to click/fail/recoup and continue.

Fortunately, the experience of wayfinding is now augmented by social systems. Social structures are filters. As a learner grows (and prunes) her personal networks, she also develops an effective means to filter abundance. The network becomes a cognitive agent in this instance – helping the learner to make sense of complex subject areas by relying not only on her own reading and resource exploration, but by permitting her social network to filter resources and draw attention to important topics. In order for these networks to work effectively, learners must be conscious of the need for diversity and should include nodes that offer critical or antagonistic perspectives on all topic areas. Sensemaking in complex environments is a social process.

After all, why should we do the heavy cognitive work when technology is uniquely suited to analyzing and generating patterns?

I’d like a learning system that functions along the lines of RescueTime – actively monitoring what I’m doing – but then offers suggestions of what I should (or could) be doing additionally. Or a system that is aware of my email exchanges over the last several years and can provide relevant information based on the development of my thinking and work. With the rise of social media, and with it the attention organizations pay to how their brand is being represented, monitoring services such as Viral Heat are promising. Imagine a course where the fragmented conversations and content are analyzed (monitored) through a similar service. Instead of creating a structure of the course in advance of the students starting (the current model), course structure emerges through numerous fragmented interactions. “Intelligence” is applied after the content and interactions start, not before. This is basically what Google did for the web – instead of fully defined and meta-described resources in a database, organized according to subject areas (i.e. Yahoo at the time), intelligence was applied at the point of search. Aggregation should do the same – reveal the content and conversation structure of the course as it unfolds, rather than defining it in advance.

Filtering resources is an important educator role, but as noted already, effective filtering can be done through a combination of wayfinding, social sensemaking, and aggregation. But expertise still matters. Educators often have years or decades of experience in a field. As such, they are familiar with many of the concepts, pitfalls, confusions, and distractions that learners are likely to encounter. As should be evident by now, the educator is an important agent in networked learning. Instead of being the sole or dominant filter of information, he now shares this task with other methods and individuals.

By determining what doesn’t belong, a learner develops and focuses his understanding of a topic. The teacher assists in the process by providing one stream of filtered information. The student is then faced with making nuanced selections based on the multiple information streams he encounters. The singular filter of the teacher has morphed into numerous information streams, each filtered according to different perspectives and world views.

Given that coherence and lucidity are key to understanding our world, how do educators teach in networks? For educators, control is being replaced with influence. Instead of controlling a classroom, a teacher now influences or shapes a network.

Any pedagogy of abundance would then, I suggest, be based on the following assumptions:

Jonassen (1991) describes it thus: Constructivism … claims that reality is constructed by the knower based upon mental activity. Humans are perceivers and interpreters who construct their own reality through engaging in those mental activities … What the mind produces are mental models that explain to the knower what he or she has perceived … We all conceive of the external reality somewhat differently, based on our unique set of experiences with the world.

Given that it has a loose definition, it is hard to pin down a constructivist approach exactly. Mayer (2004) suggests that such discovery-based approaches are less effective than guided ones, arguing that the ‘debate about discovery has been replayed many times in education but each time, the evidence has favoured a guided approach to learning’.

When Kirschner, Sweller and Clark (2006) claim, with some justification, that ‘the epistemology of a discipline should not be confused with a pedagogy for teaching/learning it’ that only highlights that the epistemology of a discipline is now being constructed by all, so learning how to participate in this is as significant as learning the subject matter of the discipline itself.

However, the number of successful open source communities is relatively small compared with the number of unsuccessful ones, and thus the rather tenuous success factors for generating and sustaining an effective community may prove to be a barrier across all subject areas. Where they thrive, however, it offers a significant model which higher education can learn much from in terms of motivation and retention (Meiszner 2010).

Abundance does not apply to all aspects of learning; indeed the opposite may be true, for example, an individual's attention is not abundant and is time limited. The abundance of content puts increasing pressure on this scarce resource, and so finding effective ways of dealing with this may be the key element in any pedagogy. However, I would contend that the abundance of content and connections is as fundamental shift in education as any we are likely to encounter, and there has, to date, been little attempt to really place this at the centre of a model of teaching.

As neuroscientist Joshua Greene puts it, ‘the Internet hasn't changed the way we think anymore than the microwave oven has changed the way we digest food. The Internet has provided us with unprecedented access to information, but it hasn't changed what we do with it once it's made it into our heads’ (Gerschenfeld 2010).

Whether there are social and behavioural impacts of operating online is a serious question, however. Just as the television had serious social impacts, we must accept that computers and Internet will also have consequences. These will undoubtedly be a mixture of positive and negative, but I would argue that using pseudo-scientific explanations to back up prejudices will not help us address these issues.

For educators there are two main issues; the first is the extent to which they help students manage their online identity, and the second is how they manage their own boundary between personal and professional life.

The over-adoption of tools can lead to what has been termed ‘creepy tree house’ syndrome (Stein 2008) when authority is seen to try and invade a young person's social space.

There is strong resistance from students to universities and lecturers making formal use of social networks as this is seen as an invasion of their social space (e.g. Madge 2009).

For the teaching function of scholarship then the question is ‘How can educators utilise the potential of these tools without destroying what makes them valuable to students?’

I would suggest the following as the most challenging for digital scholarship: Moving beyond the superficial – many successful Web 2.0 services essentially allow a very simple function, for example, sharing a photograph. Can we use the same techniques for deeper, more difficult tasks?

Understanding quality – this is not just about maintaining current quality, as this may not be appropriate in many forms, but appreciating when different levels of quality can be used.

Managing online identity – there is a tension for scholars and their students in gaining the benefits of a social network, which thrives on personal interactions, while not compromising professional identity.

Ownership of scholarly functions – there is also a dilemma regarding how much of scholarly discourse and activity we give over to cloud computing services and whether the benefits in terms of widespread use and (often) superior tools outweigh the potential risks.

" Ruth Clark and Richard Mayer in the industry classic e-Learning and the Science of Instruction, explain that seductive details are "interesting but irrelevant material added to a multimedia presentation in an effort to spice it up

Seductive details are those elements in a program that draw you in, attract the eye and engage the brain. They seduce your interest, but distract from the main point.

It's interactive! It's intriguing! But it's exhausting, and let's face it"—there's no point. Fatigue sets in and you move on

This is the premise underlying the arousal theory, the idea that entertaining and interesting embedded effects cause learners to become more emotionally aroused and therefore they work harder to learn the material.

. Designers who don't understand the basics of effective instructional design are committing what Clark Quinn of Quinnovation calls "instructional design malpractice.

CCBB design shines and sparkles wildly in the sun

When we force learners to practice without context, they've memorized facts but may not be able to apply them correctly in context.

Too much clicking can lead to learner fatigue, is distracting to the learner, and doesn't promote deeper understanding

We need to provide more contextual opportunities for drill exercises that will help the learner both retain and apply the knowledge they are practicing.

"Well-written, multiple-choice questions teach and assess knowledge within the context of a game. Poor questions simply allow the gamer to play the game without learning.

that the addition of interesting yet unimportant augmentations can divert learners from learning the main points that are being made

. You're best served to spend your time designing the right type of course and spending less time looking for ways to 'jazz it up'"

. Now, take a look at the screen and see where your eye lands first. Is it the flashing Next button in the bottom right corner? Or is it the important content bit at the center of the screen? Ask an objective outsider to take a look, too

Pilot your program with some test learners

heck in with them immediately afterwards, one week, three weeks. See what they remembe

, don't take this to mean that elearning shouldn't look good.

What about your LMS? At Kineo, where I work, we love using Moodle and Totara as an LMS solution for our clients, not only because of the great features and the fact that it's open source, but always because we can make it look like almost anythin

In formal learning, we should be presenting meaningful and authentic tasks, and asking learners to solve them, ideally collaboratively.

While individual is better than none, collaborative allows opportunity for meaning negotiation.  We need to allow failure, and support learning from it. We need to be able to ask questions, and make decisions and see the consequences.

I think there are 8 elements!! I miss Collate, this is not the same as Choose, it is about organising anbd structuring what we learn, (constructing our emergent uderstanding) as opposed to the selection of a direction.

However, real learning involves research, design, problem-solving, creativity, innovation, experimentation, etc

the intersection of 1) self-organized learning and 2) online collaboration is what I consider should be a primary focus of organizational learning professionals

Common web 2.0 practice is to link back to what has been copied, a form of collaboration or perhaps cooperation.

in informal learning, we need to create ways for people to develop their understandings, work together, to put out opinions and get feedback, ask for help, and find people to use as models.  By using tools like blogs for recording and sharing personal learning and information updates, wikis to collaborate, discussion forums to converse, and blogs and microblogs to track what others think are important, we provide ways to naturally learn together.

informal learning is NOT, by definition, manageable

just trying to raise awareness that what we typically do formally is not well aligned with how people really learn, and that supporting some of these activities is the key to unlocking organizational innovation.

but instead to provide a conducive environment and encourage them

Revisit your assessment (remember that? You designed it before anything else). Will this information in any way affect successful performance? If not, can you add a link to the policy and an e-mail introduction with a disclaimer? Can you cut the verbiage (here’s a trick: what if you had to pay $5 per word? I bet you could cut it then.)? Or, if there’s really that much content, should you break it into pieces?

just because it’s online doesn’t mean you can control it. Build it with this in mind.

Zooming and spinning words, irrelevant animations, and neon “next” buttons do not heighten engagement. They confuse and distract learners

“You know, I don’t think that will solve the performance problem. Can we talk about some ways to get you a better solution?” Designing a solution that doesn’t solve the problem, or that makes it worse, doesn’t do anything to help the organization, the field, or your department’s reputation – or your own. “Instructional designer” is a job title. “Performance consultant” is a mindset. 

Instruction does not cause learning.

grounded in the object-oriented paradigm of computer science.

build small (relative to the size of an entire course) instructional components that can be reused a number of times in different learning contexts

Moreover, those who incorporate learning objects can collaborate on and benefit immediately from new versions. These are significant differences between learning objects and other instructional media that have existed previously.

Supporting the notion of small, reusable chunks of instructional media, Reigeluth and Nelson (1997) suggest that when teachers first gain access to instructional materials, they often break the materials down into their constituent parts.

if instructors received instructional resources as individual components, this initial step of decomposition could be bypassed

The Learning Technology Standards Committee chose the term “learning objects” (possibly from Wayne Hodgins’ 1994 use of the term in the title of the CedMA working group called “Learning Architectures, API’s, and Learning Objects”)

provided a working definition

Learning Objects are defined here as any entity, digital or non-digital, which can be used, re-used or referenced during technology supported learning. Examples of technology-supported learning include computer-based training systems, interactive learning environments, intelligent computer-aided instruction systems, distance learning systems, and collaborative learning environments. Examples of Learning Objects include multimedia content, instructional content, learning objectives, instructional software and software tools, and persons, organizations, or events referenced during technology supported learning (LOM, 2000).

The proliferation of definitions for the term “learning object” makes communication confusing and difficult.

It would seem that there are almost as many definitions of the term as there are people employing it.

In addition to the various definitions of the term “learning object,” other terms that imply the general intention to take an object-oriented approach to computer-assisted instruction confuse the issue further.

Depressingly, while each of these is something different, they all conform to the Learning Technology Standards Committee’s  “learning object” definition. An in depth discussion of the precise meanings of each of these terms would not add to the main point of this discussion: the field is still struggling to come to grips with the question, “What is a learning object?”

At the same time, the creation of yet another term only seems to add to the confusion. While the creation of a satisfactory definition of the term learning object will probably consume the better part of the author’s career, a working definition must be presented before the discussion can proceed.

Therefore, this chapter will define a learning object as “any digital resource that can be reused to support learning.”

This definition includes anything that can be delivered across the network on demand, be it large or small.

This definition of learning object, “any digital resource that can be reused to support learning,” is proposed for two reasons.

The definition adopted for this chapter emphasizes the purposeful use (by either an instructional designer, an instructor, or a student) of these objects to support learning

Second, the proposed definition is based on the LTSC definition (and defines a proper subset of learning objects as defined by the LTSC), making issues of compatibility of learning object as defined within this chapter and learning object as defined by the LTSC explicit

With that compatibility made explicit, the proposed definition differs from the LTSC definition in two important ways.

First, the definition explicitly rejects non-digital

The definition also drops the phrase "technology supported" which is now implicit, because all learning objects are digital.

Second, the phrase "to support" has been substituted in place of "during" in the LTSC definition. Use of an object "during" learning doesn't connect its use to learning

First, the definition is sufficiently narrow to define a reasonably homogeneous set of things: reusable digital resources. At the same time, the definition is broad enough to include the estimated 15 terabytes of information available on the publicly accessible Internet (Internet Newsroom, 1999).

Armed with a working definition of the term learning object, the discussion of the instructional use of learning objects can proceed.

Instructional design theory and learning objects

Reigeluth

[I]nstructional design theories are design oriented, they describe methods of instruction and the situations in which those methods should be used, the methods can be broken into simpler component methods, and the methods are probabilistic. (p. 7).s11 {margin-left:0; line-height:2.400000; text-indent:36;}

Because the very definition of “theory” in some fields is “descriptive,” design theories are commonly confused with other types of theories that they are not, including learning theory and curriculum theory (Reigeluth, 1999a).

The following discussion takes a step in this direction, by recasting two of the largest issues in the learning objects area – combination and granularity – in instructional design terms

Combination

there is astonishingly little conversation around the instructional design implications of learning objects.

item (d) in the Learning Objects Metadata Working Group’s PAR (LOM, 2000) reads as follows:

To enable computer agents to automatically and dynamically compose personalized lessons for an individual learner

at this point a brief discussion of metadata, the focus of the Learning Object Metadata Working Group’s efforts, is necessary.

Metadata, literally “data about data,” is descriptive information about a resource

he Learning Objects Metadata Working Group is working to create metadata for learning objects (such as Title, Author, Version, Format, etc.) so that people and computers will be able to find objects by searching

​The problem with 7(d) arose when people began to actually consider what it meant for a computer to “automatically and dynamically compose personalized lessons.”

his meant taking individual learning objects and combining them in a way that made instructional sense, or in instructional design terminology, “sequencing” the learning objects.

The problem was that no instructional design information was included in the metadata specified by the current version of the Learning Objects Metadata Working Group standard.

​The lack of instructional design discussion at this standards-setting level of conversation about learning objects is disturbing, because it might indicate a trend.

Once technology or software that does not support an instructionally-grounded approach to learning object sequencing is completed and shipped to the average teacher, why would he or she respond any differently

Wiley (1999) called this “the new CAI – ‘Clip Art Instruction’” (p. 6).

Discussion of the problem of combining learning objects in terms of “sequencing” leads to another connection between learning objects and instructional design theory.

Granularity

The most difficult problem facing the designers of learning objects is that of “granularity” (Wiley, et al., 1999).

How big should a learning object be?

Reuse is the core of the learning object notion, as generativity, adaptivity, and other –ivities are all facilitated by the property of reuse.

designating every individual graphic and paragraph of text within a curriculum a “learning object” can be prohibitively expensive

It’s about knowing how and knowing who and knowing who knows who knows what. Whatever you call this blend of craft and community, one of the most important challenges posed by the real-time, ubiquitous, wireless, always-on, often alienating interwebs are the skills required for the use of media to be productive and to foster authentic interpersonal connection, rather than waste of time and attention on phony, banal, alienated pseudo-communication. Know-how is where the difference lies.

Novices can best be supported through a series of activities that are structured on connectivist learning principles with a goal to enhance autonomy and the building of personal learning networks.

Based on the success of Norvig and Thrun’s experiment, the university’s computer science department is planning to broadcast eight additional courses for free in the spring, most focusing on high-level concepts that require participants already to have a pretty good command of math and science.

It raises the question: Whose certification matters, for what purposes?

For one, the professors can only evaluate non-enrolled students via assessments that can be graded automatically.

it can be difficult to assess skills without being able to administer project-based assignments

With a player like Stanford doing something like this, they’re bringing attention to the possibilities of the Web for expanding open education

This course will allow us the opportunity to collaborate together and experience first hand this new way to learn. In addition to this we can evaluate whether or not this is an effective way to learn

The work on the Open Badge Infrastructure is only slightly encouraging with regard to this challenge.

However, we can’t credential (responsibly) without assessments, and in the midst of all the excitement about badges I’ve yet to hear of any novel (or even uninteresting) work on the underlying assessments themselves

Given the OAR acronym, users of OER are literally up a creek without a paddle. And will be until we start making some progress on the OAR issue.