Group items tagged

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

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.

explore new pedagogy, technology

they are not open in the sense of being reusable and openly accessible

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.

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.

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.

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

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

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

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.