Group items tagged

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

Blogs are also the epitome of the type of technology that can lead to rapid innovation. They can be free to set up, are easy to use and because they are at the user's control, they represent a liberated form for expression. There is no word limit or publication schedule for a blog

Are they central or peripheral to practice?

How do we recognise quality?

Should bloggers use institutional systems or separate out their blogging and formal identities?

the advent of social networks that is having an influence on scholarly practice.

Prior to the Internet, but particularly prior to social networks, this kind of network was limited to those with whom you interacted regularly.

Dunbar's (1992) research on friends and group size suggests that it has a capacity of around 150. It necessitates keeping in touch with a lot of people, often reinforcing that contact with physical interaction.

for those who have taken the step to establishing an online identity, these networks are undoubtedly of significant value in their everyday practice.

openness

Tim O'Reilly (2004) calls ‘an architecture of participation’, an infrastructure and set of tools that allow anyone to contribute.

It is this democratisation and removal of previous filters that has characterised the tools which have formed the second wave of web popularity, such as YouTube, Wikipedia, Flickr, blogs, Facebook and Twitter.

Openness then refers not only to the technology but also to the practice of sharing content as a default.

Fast – technology that is easy to learn and quick to set up. The academic does not need to attend a training course to use it or submit a request to their central IT services to set it up. This means they can experiment quickly.

Cheap – tools that are usually free or at least have a freemium model so the individual can fund any extension themselves. This means that it is not necessary to gain authorisation to use them from a budget holder. It also means the user doesn't need to be concerned about the size of audience or return on investment, which is liberating.

Out of control – these technologies are outside of formal institutional control structures, so they have a more personal element and are more flexible. They are also democratised tools, so the control of them is as much in the hands of students as it is that of the educator.

Overall, this tends to encourage experimentation and innovation in terms of both what people produce for content services and the uses they put technology to in education.

‘the good enough revolution’

This reflects a move away from expensive, sophisticated software and hardware to using tools which are easy to use, lightweight and which tie in with the digital, networked, open culture.

there seems to be such an anxiety about being labelled a ‘technological determinist’ that many people in education seek to deny the significance of technology in any discussion. ‘Technology isn't important’, ‘pedagogy comes first’, ‘we should be talking about learning, not the technology’ are all common refrains in conferences and workshops.

While there is undoubtedly some truth in these, the suggestion that technology isn't playing a significant role in how people are communicating, working, constructing knowledge and socialising is to ignore a major influencing factor in a complex equation.

entirely unpredicted, what is often termed ‘emergent use’, which arises from a community taking a system and using it for purposes the creators never envisaged.

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.

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).

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

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

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

Brown (2009) reports, Recently, the Nielsen Norman Group study of teenagers using the web noted: ‘We measured a success rate of only 55 percent for the teenage users in this study, which is substantially lower than the 66 percent success rate we found for adult users’. The report added: ‘Teens’ poor performance is caused by three factors: insufficient reading skills, less sophisticated research strategies, and a dramatically lower patience level’.

A new generation is behaving fundamentally differently – there seems little real evidence beyond the rhetoric that the net generation is in some way different from its predecessors as a result of having been exposed to digital technologies. There is some moderate evidence that they may have different attitudes. There is a general change in society which has relevance for learning – certainly the overall context is an ICT-rich one, and people are using the Internet for a variety of learning-related activities. People are learning in different ways – although firm evidence of informal learning is difficult to gather, there is much by the way of proxy activity that indicates this is the case. There is growing dissatisfaction with current practice in higher education – there seems little strong evidence for this. Probably more significant to the culture of education has been the shift to perceiving the student as a customer. There is certainly little evidence that the dissatisfaction is greater than it used to be, but what may be significant is that there are now viable alternatives for learners. Universities have lost their monopoly on learning, which reinforces the next point.

Higher education will undergo similar change to that in other sectors – there are some similarities between higher education and other sectors, such as the newspaper and music industries, but the differences are probably more significant. However, the blurring of boundaries between sectors and the viability of self-directed, community-based learning means that the competition is now more complex.

The first is that there is lag between society's acceptance of a technology and then its adoption in higher education. Brown (2009) suggests that in society the stages of technology diffusion can be defined as critical mass (ownership by 20–30 per cent of the population), ubiquity (30–70 per cent) and finally invisibility (more than 70 per cent). If higher education were to wait for the invisibility stage to be reached before it engaged with a technology, then given the time it takes to implement policies and technology, it really will look outdated. For example, in 2007, those using social networks might have been in the minority; now they will be in the majority. This is the problem with waiting for data to determine decisions – if you made a decision based on 2007 data that social networks were largely unused, it would look out of date in 2010. What is significant is the direction of travel, not the absolute percentages at any given time.

However as education in the developed world moves in new directions, we must be cognizant of how this move impacts the learners in the developing world. How will learners in these areas access, explore and contribute to the creation of knowledge? Will social networking technologies, and the emergence of next generation technologies further exclude the greatest number of undergraduate learners. Will this new reality result in too few voices connecting and validating knowledge? How can non-Eurocentric voices be heard? One must ensure that there is some democracy in this new model.

Educators and leaders in academia are confronted with important questions. How should institutions of learning be designed to serve the needs participative, social, and global information cycle? What assumptions about the system of higher education need to be abandoned due to technological advancements? How can the vital roles of research and teaching and learning be addressed through distributed means? How can accreditation be broadened to include the full spectrum of formal and informal learning activities?

Failure to recognize the pivotal role of digital technologies will result in institutions at odds with the world in which they operate.

The American Faculty: The Restructuring of Academic Work and Careers (Schuster & Finkelstein, 2006) propose that universities are experiencing a revolution—with tremendous consequences. They write, Everything is in play, as nearly every aspect of academic life is being driven by a host of inter-related developments: dazzling technological advances, globalization that permeates academic boundaries, rapid increase of tertiary students worldwide, expansion of proprietary higher education, a blurring of (the) public/private distinction, and entrepreneurial initiatives on and off campus. (p. xvii)

Technological innovations in bandwidth, storage, processing speed, and software directly impact education (Downes, 2009), creating new opportunities for learner-learner/educator and learner-information interactions.

ome researchers have turned to complexity theory to advance education, suggesting that emphasis be placed on the whole system rather than reductionist views often found in "mainstream science" (Mason, 2008). Increased collaboration in a model of "interlocking partnerships among researchers, among universities, and across international borders” (McFadden Allen, 2007, p. 3) promises a new model of not only what it means to be an academic, but also what it means to be an academic institution.

David Wiley and Rory McGreal urge universities to open their content; Wiley further envisions the future of education consisting of learner-generated materials;

Building rhizome-like learning networks can foster an environment more conducive to continuous knowledge acquisition and constructio

This does not match the way our brain process information, as we are better at learning incremental chunks of knowledge in a meaningful and authentic context.

Dave Cormie

Clark Quinn’s idea of slow learning

Here Jon Dron reminds us that tools themselves are not technology

Dron’s definitions of hard vs soft technologies relevant to both formal and informal learning, further help us to undertand that soft technologies are perhaps more useful in building learning and support communities and equipping learners with the ability to  navigate information in networks, thereby promoting lifelong learning.

The learners will end up leading the way, as they should.

So what does engagement with technology mean? First, I would suggest, it acknowledges that the changes afforded by new technology are not peripheral but fundamental to all aspects of scholarship.

In his 1973 paper on the stability of ecological systems, Holling defined resilience as ‘a measure of the persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables’.

Hall and Winn (2010) have applied the concept of resilience to education and open education in particular.

It is undoubtedly the case that the vast amount of online content means that people now conduct a lot of their learning informally, using free resources. Combined with financial pressures, this creates a pressure or an alternative competition for learning that is new to higher education.

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.

Tossing a hate bomb is the easiest thing in the world when you have a Twitter account with a fake name. It's a technology tailor made for the knee jerk reaction.

I had tracked down the sixth largest corporation in America and found that they were engaged in the same level of slacktivism as somebody sitting on a couch with a smart phone and a couple of bottles of beer.

We see that we learn by being engaged in meaningful activity, and working with others.

I like Collins, Brown, & Holum’s Cognitive Apprenticeship as a model for thinking more richly about learning.  Other learning models are not static

I’m really arguing for the need to come up with a broader perspective on learning

I think this framework will need to start with considering the experience design, what is the flow of information and activity that will help develop the learner

meaningfulness, activation and reactivation, not separate but wrapped around our lives

I’m looking to start matching our technology more closely to our brains

I don’t need or want an LMS and I often don’t need a ‘teacher’ in the traditional sense, though I welcome the wisdom of coaches and mentors.

In self regulated learning, evaluation is a metacognitive event

. Much of what my students discussed is similar to my ideal. Briefly, here are some elements, organized under the four categories of appreciative inquiry: 1) Discovery-the best of what we have previously experienced: sense of accomplishment, respect, sharing ideas, supportive atmosphere to enable taking risks. 2) Dream-best of what might be: have real life application, synergy and energy, flexible and fun, open discussions, clear direction, ideas flying around, taking on complex ideas, confidentiality in that “what happens in Vegas, stays in Vegas”. After these discussions, we went on to 3) Design-what it might truly look like and 4) Delivery-what will we commit to, an individual ranking of items central to creating a best learning experience.

The majority of us cannot live on the farm or in the bush; but can we design learning experiences along a similar model where learners contribute something of value to the community?

 We have pieces of the puzzle ( smaller chunks, content models) and we have the tools (individualized delivery, semantics), but putting them together really hasn’t been done yet.

 We still have to figure out what our design process would look like,

 It’s really trying to build a system that is more mentor like than instructor like.

The point is to take the fact that technology is no longer the limit, our imaginations are.

You have more small chunks of content, and more distributed performance model.

: "Are you trying to teach information or skills? Okay – a skill. Well, that will require practice.

that calling it “slow learning” gives the opposite impression of what it actually is

and one could argue that what you are describing could actually be FAST learning

“spaced learning”

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.