Group items tagged

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.

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.

Anyone can open their own diploma mill, err I mean badge-selling operation? Of course not. Badges would have to be accredited by someone.

If you want to get a badge though, that's going to cost.

I think the presence of a badge could actually be a detriment to an otherwise genuine learning experience.

]The whole point of education organisations adopting elearning is to cut costs. They are not doing it to improve education standards. They say it's to educate more. But we know this is a smoke screen. Bean-counters run universities and colleges just like they run commercial companies.For example, The Briish Council is planning to move into distance learning big time. 10,000s of new students. Their reasons (am I #cynic) won't be to improve educational outcomes (mostly English language teaching) but to get more qualifed teachers for for their bucks.

citation levels of articles that are published online versus those that are in closed access journals. Hajjem, Harnad and Gingras (2005) compared 1,307,038 articles across a range of disciplines and found that open access articles have a higher citation impact of between 36 and 172 per cent. So publishing in an online, open manner aids in the traditional measures of citation.

This section will look at the most concrete realisation of the open education movement, namely that of open education resources. In particular I want to revisit the notion of granularity and how changes in this, afforded by new technologies, are changing scholarly behaviour.

Zittrain (2008) terms ‘generativity’, which he defines as ‘a system's capacity to produce unanticipated change through unfiltered contributions from broad and varied audiences’. Little OERs are high in generativity because they can easily be used in different contexts, whereas the context is embedded within big OERs, which in turn means they are better at meeting a specific learning aim.

Big OER projects have a variety of models of funding, and Wiley highlights three of these demonstrating a range of centralisation: a centralised team funded by donors and grants (such as MIT), linking it into teaching responsibilities (as practised at Utah State University) and a decentralised collaborative authoring approach (e.g. Rice Connexions, http://cnx.org).

The reasons for this are varied, including technical complexity and motivation. One other reason which the OpenLearn team suggest is that the ‘content provided on the site was of high quality and so discouraged alteration’.

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.

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