However, that is not much use if you, as an e-learning professional, have an audience for knowledge- and content delivery via mobile devices including the iPhone. In this series of articles, I will discuss approaches to delivering training via PDAs like the iPhone based on applications and functionality already available on that device. Let’s see what happens after that.

Let’s set the context. There are a growing number of extensions to the well-established core term ‘E-Learning’ including this list of candidates (compiled in 2008 by CramerSweeney):

• C-Learning - learning via collaboration with co-workers and associates
• M-Learning – learning via a portable digital media device
• V-Learning – learning inside a virtual world (such as Second Life)
• G-Learning – learning via computer games

The latest term I have discovered is Ubiquitous Learning, or U-Learning, first discussed by Gary Woodill in his excellent Workplace Learning Today column, who encountered the term in the title of a Masters dissertation called The dawn of uLearning: near-future directions for 21st century educators). Can we now take this trend to its logical conclusion and define

• T-Learning – learning via reading educational books, academic and other Textual material
• S-Learning – learning via lecturers & teachers Speaking
• W-Learning – learning via the medium of pen, paper, and taking Written notes

[Image courtesy Flickr user tryingtimes]

‘X’ is one of the cooler letters – let’s have X-Learning. I’ll send a Mars Bar to the person who contributes the best definition of X-Learning. 

Or should we stop. Now. Please.

In my view, each of these descriptors implies that the alphabetti-spaghetti of C- G- M- V-Learning activities are discrete verticals or silos, standing alone and untouched by their siblings.

Of course this is not the case: if anything, we can say that each of these terms describes a learning channel that relies on or emphasizes one modality of learning, but doesn’t – I would assert can’t – occlude other learning modalities.

So whither M-Learning among this cacophony of uppercase modifiers? Does what we commonly call ‘M-Learning’ deserve to be treated as a domain in it’s own right? Can we discover if there are any unique characteristics that differentiate learning via mobile technologies? 

I think we need to take a step back. First of all, what is learning? I would assert that learning is

The acquisition of new knowledge, behaviors, skills, values, preferences or understanding, and may involve synthesizing different types of information. The progress of this acquisition over time tends to follow learning curves. Learning is a Constructivist activity.

Harold Stolovitch puts it succinctly in his 2002 text Telling Ain’t Training when he says that “learning is change, adaptation” (p.18). According to the author, we use training, instruction and education as strategies to enable people to learn.

–

†Probably.

Apple has a habit of denying that they are developing a range of devices and technologies. Right up to the moment that Apple announce their newest gizmo /  solution / partnership. Until 2009 this typically occurred at an Apple Expo event with Steve Jobs uttering the famous  phrase “One more thing…” as the latest object of geeky desire walks up the aisle of the technological Chapel O’ Love.

Next time: What is E-Learning (Slight Return)

Coming up: What is M-Learning?

_________________________

References:

Nalder, J. (2008). The dawn of uLearning: near-future directions for 21st century educators. Unpublished Master’s thesis, Queensland University of Technology, Brisbane. [Internet] Available from: http://www.scribd.com/doc/12398804/The-dawn-of-uLearning-Jonathan-Nalder-Masters-thesis Accessed 18th February 2009

Stolovitch, H. (2002) Telling Ain’t Training. ASTD Press.

Sweeney, J. (2008) Let’s Talk Terminology CramerSweeney Training Blog [Internet] Available from: http://www.cramersweeney.com/cs_id/trainingblog/ Accessed 17th February 2009

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We can say with a degree of certainty that it’s the case that these systems are in place for a range of tactical, operational, and strategic reasons. According to Klein and Eseryl (2005) this agglomeration of systems can be viewed within

a framework based on the premise that different methods are needed for different levels of knowledge and expertise.

Klein and Eseryl extend Dillon and Hallett’s 2001 learning curve model; by making it the basis for a framework (see Figure 1) that applies the notion of the conventional learning curve to the context of the corporate learning environment, they assert that to supply an “apt” structure for understanding when and how different learning approaches and strategies are used.

Figure 1 Framework for corporate learning environments (After Klein & Eseryl 2006)

[click to enlarge]

Within this framework, the conceptualization of the learning environment consists of systems to manage and support:

1. cohesive team management
2. knowledge generation and sharing
3. performance support
4. document storage and retrieval
5. on-demand learning
6. traditional training

___________________

References:

Dillon, P. & Hallett, C. (2001). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

Klein, J. Eseryel, D. (2005) The Corporate Learning Environment. [Internet] Available from: http://www.igi-pub.com/downloads/excerpts/159140505XCh1.pdf
Accessed June 12 2008
–

We can say with a degree of certainty that it’s the case that these systems are in place for a range of tactical, operational, and strategic reasons. According to Klein and Eseryl (2005) this agglomeration of systems can be viewed within

a framework based on the premise that different methods are needed for different levels of knowledge and expertise.

Klein and Eseryl extend Dillon and Hallett’s 2001 learning curve model; by making it the basis for a framework (see Figure 1) that applies the notion of the conventional learning curve to the context of the corporate learning environment, they assert that to supply an “apt” structure for understanding when and how different learning approaches and strategies are used.

Figure 1 Framework for corporate learning environments (After Klein & Eseryl 2006)

[click to enlarge]

Within this framework, the conceptualization of the learning environment consists of systems to manage and support:

1. cohesive team management
2. knowledge generation and sharing
3. performance support
4. document storage and retrieval
5. on-demand learning
6. traditional training

___________________

References:

Dillon, P. & Hallett, C. (2001). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

Klein, J. Eseryel, D. (2005) The Corporate Learning Environment. [Internet] Available from: http://www.igi-pub.com/downloads/excerpts/159140505XCh1.pdf
Accessed June 12 2008
–

By adapting the well-known learning curve, I developed a conceptual model that maps Bloom’s Taxonomy of Learning Objectives to learner requirements as they progress along the curve (see Figure 1).

Figure 1. Learning Curve model aligning Bloom’s Taxonomy and components of the E-learning Ecosystem

In today’s blog post, I’ll look at preconditions for introducing learning modalities to this model.

Now read on.

There are two components involved in investigating this aspect of e-learning as a means to enhance knowledge worker performance:

1. E-learning tools and technologies
2. Applying effective learning modalities to learning requirements

E-learning tools and technologies

In their influential white paper Powering the leap to maturity: The eLearning ecosystem, Dillon & Hallet define the concept of the “E-learning EcoSystem.” The authors assert that a “blended approach” where instructor-led resources are deployed at the earliest stages of a learner’s development, and increasingly, e-learning solutions are implemented as the learner develops.

With Web-based training, as with its manual counterpart in the classroom, the zone of applicability is actually quite limited. The only time it makes sense to pull workers off their jobs for training is limited precisely to those times when no other alternative will suffice. Off the- job forms of training make good business sense only when workers are at the bottom of the learning curve and are not yet equipped to perform at any acceptable level of competence.

Through “pervasive connectivity” (p.19), characterized by the growth of deployment of corporate portals and intranets, as well as learning support technologies such as content management systems and knowledgebases, and is the foundation for their e-learning ecosystem. In my view, the choice of terminology that the authors use is interesting; by employing the term ecosystem -

a system whose members benefit from each other’s participation via symbiotic relationships… It is a term that originated from biology, and refers to self-sustaining systems,

they imply that the nature (no pun intended) of organizations parallels complex natural systems. Similarly, a functioning learning ecosphere holistically supports a diverse range of learning modalities which enable the learner to thrive in the corporate environment.

As workers move up the e-learning curve, they quickly leave the relative isolation of pure asynchronous courseware. Initially, they enter the more richly supported environment of the online university, backed by an enterprise-level learning management system.

Progressing along the curve, the authors note the introduction of just-in-time forms of learning content delivery.

As we move even further up the e-learning curve we encounter yet another interesting revelation. Most of the learning technologies at this end of the curve are not generally recognized as “learning” technologies at all. Rather, such items as collaboration tools and intelligent search are more typically thought of as knowledge management technologies. Deploying and utilizing these types of tools are what differentiates an employee from a “performer.”

Having characterised the e-learning ecosystem, Dillon & Hallet define the components of it:

• Web-base training
• Online university
• Learning Objects
• Electronic Performance Support Systems
• Collaboration
• Intelligent Search

As they suggest, these components “put the ’system’ in ecosystem” – a statement that I would suggest is doubly true: by describing the technologies (and to a lesser extent on technology and learning solutions vendors) in their white paper, they neglect to lend appropriate weight to how these systems are implemented.

More.

FÓGRA: Malinka Ivanova of the Technical University in Sofia has an interesting perspective on this topic. Click here to find out more.
_____________________

References:

Definition of “Ecosystem.” Learnthat.com. [Internet] Available from: http://www.learnthat.com/define/view.asp?id=302 Accessed 30 May 2008.

Dillon, P. & Hallett, C. (2001, October). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

–

By adapting the well-known learning curve, I developed a conceptual model that maps Bloom’s Taxonomy of Learning Objectives to learner requirements as they progress along the curve (see Figure 1).

Figure 1. Learning Curve model aligning Bloom’s Taxonomy and components of the E-learning Ecosystem

In today’s blog post, I’ll look at preconditions for introducing learning modalities to this model.

Now read on.

There are two components involved in investigating this aspect of e-learning as a means to enhance knowledge worker performance:

1. E-learning tools and technologies
2. Applying effective learning modalities to learning requirements

E-learning tools and technologies

In their influential white paper Powering the leap to maturity: The eLearning ecosystem, Dillon & Hallet define the concept of the “E-learning EcoSystem.” The authors assert that a “blended approach” where instructor-led resources are deployed at the earliest stages of a learner’s development, and increasingly, e-learning solutions are implemented as the learner develops.

With Web-based training, as with its manual counterpart in the classroom, the zone of applicability is actually quite limited. The only time it makes sense to pull workers off their jobs for training is limited precisely to those times when no other alternative will suffice. Off the- job forms of training make good business sense only when workers are at the bottom of the learning curve and are not yet equipped to perform at any acceptable level of competence.

Through “pervasive connectivity” (p.19), characterized by the growth of deployment of corporate portals and intranets, as well as learning support technologies such as content management systems and knowledgebases, and is the foundation for their e-learning ecosystem. In my view, the choice of terminology that the authors use is interesting; by employing the term ecosystem -

a system whose members benefit from each other’s participation via symbiotic relationships… It is a term that originated from biology, and refers to self-sustaining systems,

they imply that the nature (no pun intended) of organizations parallels complex natural systems. Similarly, a functioning learning ecosphere holistically supports a diverse range of learning modalities which enable the learner to thrive in the corporate environment.

As workers move up the e-learning curve, they quickly leave the relative isolation of pure asynchronous courseware. Initially, they enter the more richly supported environment of the online university, backed by an enterprise-level learning management system.

Progressing along the curve, the authors note the introduction of just-in-time forms of learning content delivery.

As we move even further up the e-learning curve we encounter yet another interesting revelation. Most of the learning technologies at this end of the curve are not generally recognized as “learning” technologies at all. Rather, such items as collaboration tools and intelligent search are more typically thought of as knowledge management technologies. Deploying and utilizing these types of tools are what differentiates an employee from a “performer.”

Having characterised the e-learning ecosystem, Dillon & Hallet define the components of it:

• Web-base training
• Online university
• Learning Objects
• Electronic Performance Support Systems
• Collaboration
• Intelligent Search

As they suggest, these components “put the ’system’ in ecosystem” – a statement that I would suggest is doubly true: by describing the technologies (and to a lesser extent on technology and learning solutions vendors) in their white paper, they neglect to lend appropriate weight to how these systems are implemented.

More.

FÓGRA: Malinka Ivanova of the Technical University in Sofia has an interesting perspective on this topic. Click here to find out more.
_____________________

References:

Definition of “Ecosystem.” Learnthat.com. [Internet] Available from: http://www.learnthat.com/define/view.asp?id=302 Accessed 30 May 2008.

Dillon, P. & Hallett, C. (2001, October). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

–

• entry-level or new hire orientation and competency building
• internal transfers to a new discipline (i.e. the worker moves from Production to QA)
• retraining on a new production system (i.e. a new type of widget-making tool or a new software system)
• employee career advancement (i.e. from individual contributor to manager)

Psychology tells us that the learning curve obeys what is called a power law (Ritter & Scholler, 2002).

As such they are often said to conform to “the power law of practice”. Cognitive psychology has shown that the power law of practice is ubiquitous, and cognitive modeling has explained both the general speedup and variability in performance.

So, as a worker learns a task, skill, or process (”progresses along the learning curve”), their competency improves and productivity increases with some variations but broadly within generally accepted parameters.

As workers advance from their neophyte status, they begin to attain what Marc J. Rosenberg (2006) calls “performer” status; the are transitioning from being Novice to Competent, along a path that will enable them to become Experienced, until they achieve Expert (or Master) status.

We can also say that once a worker reaches a certain level of competence, their learning needs are met less by generic courses and curricula, and more by specific, even personalized, learning interventions such as task/skill practise and coaching, access to knowledge and performance resources, and collaboration and problem solving (see Figure 1).

As an extension of this (and effectively demonstrated by Rosenberg), organizations failing to move beyond the classroom or traditional CBT-type courseware for their ongoing learning and development needs, are probably impeding the development of their workers, as well as negatively affecting their (the organization’s) own potential (see Figure 2).

In order to provide effective learning and performance support to workers after they become competent, organizations must strive to develop their workers’ skills as employees undertake their regular workplace activities. It is my view that this level of performance support can only be provided through access to networked knowledge assets.

And that is what I’ll be discussing tomorrow.
______________________

References:

Dillon, P. & Hallett, C. (2001, October). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

Ritter, F. E., & Schooler, L. J. (2002). The learning curve. In International encyclopedia of the social and behavioral sciences. 8602-8605. Amsterdam: Pergamon.
[Internet] Available from: http://www.iesbs.com/ Accessed 27 May 2008.

Rosenberg, M. J. (2006) Beyond e-Learning. San Francisco, CA: John Wiley & Sons, Inc.

–

• entry-level or new hire orientation and competency building
• internal transfers to a new discipline (i.e. the worker moves from Production to QA)
• retraining on a new production system (i.e. a new type of widget-making tool or a new software system)
• employee career advancement (i.e. from individual contributor to manager)

Psychology tells us that the learning curve obeys what is called a power law (Ritter & Scholler, 2002).

As such they are often said to conform to “the power law of practice”. Cognitive psychology has shown that the power law of practice is ubiquitous, and cognitive modeling has explained both the general speedup and variability in performance.

So, as a worker learns a task, skill, or process (”progresses along the learning curve”), their competency improves and productivity increases with some variations but broadly within generally accepted parameters.

As workers advance from their neophyte status, they begin to attain what Marc J. Rosenberg (2006) calls “performer” status; the are transitioning from being Novice to Competent, along a path that will enable them to become Experienced, until they achieve Expert (or Master) status.

We can also say that once a worker reaches a certain level of competence, their learning needs are met less by generic courses and curricula, and more by specific, even personalized, learning interventions such as task/skill practise and coaching, access to knowledge and performance resources, and collaboration and problem solving (see Figure 1).

As an extension of this (and effectively demonstrated by Rosenberg), organizations failing to move beyond the classroom or traditional CBT-type courseware for their ongoing learning and development needs, are probably impeding the development of their workers, as well as negatively affecting their (the organization’s) own potential (see Figure 2).

In order to provide effective learning and performance support to workers after they become competent, organizations must strive to develop their workers’ skills as employees undertake their regular workplace activities. It is my view that this level of performance support can only be provided through access to networked knowledge assets.

And that is what I’ll be discussing tomorrow.
______________________

References:

Dillon, P. & Hallett, C. (2001, October). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

Ritter, F. E., & Schooler, L. J. (2002). The learning curve. In International encyclopedia of the social and behavioral sciences. 8602-8605. Amsterdam: Pergamon.
[Internet] Available from: http://www.iesbs.com/ Accessed 27 May 2008.

Rosenberg, M. J. (2006) Beyond e-Learning. San Francisco, CA: John Wiley & Sons, Inc.

–

These earliest stages can also be considered the “off-the-job training” segment meaning they are also the most expensive to the sponsoring organization.

As learners move up the curve, they move out of the classroom and into an on-the-job learning environment. As they approach higher levels of expertise, learners require more one-on-one or individualized approaches to learning. Because each worker has a unique set of knowledge and skill gaps separating him/her from the highest levels of expertise, it will take the mentoring intelligence of a live expert to detect and redress those gaps.

Over time, and as they acquire expertise through experiential learning knowledge workers will exhaust the list of colleagues upon whom they can rely for coaching and will need to rely upon their their own skills, knowledge and expertise to improve their performance.

Dillon & Hallett note that

one of the recent maturational leaps for e-learning has been the recognition that no one form of learning technology is capable of addressing the diverse range of learning modality requirements found in the modern workplace.

The authors labelled this “leap of recognition” as “the blended solution.” I would suggest that this use of terminology reflects the time that the white paper was authored (2001): a more appropriate use of language would be to call each modality a learning delivery channel. Learning delivery channels can include different technologies – virtual classrooms, EPSS, software simulations, online collaboration, self-paced e-learning, knowledge management – they are much more than this: for example mentoring is a learning delivery channel, such activities can can be undertaken in a face-to-face or an online environment using video conferencing.

As Don Morrison says:

I find it difficult to give ‘blended learning’ the same respect I give, say, blended whiskey which is produced by the highly skilful blending of between 20 and 50 different ‘single’ malt and grain whiskies—of differing ages and from different regions and distilleries—to create a new brand with its own unique character. The greater the number of component whiskies, the greater the consistency of the resulting blend. If one of the contributing distilleries goes out of business, the blenders can achieve the same end result by adjusting the blend of the other single whiskies. If only such art, science, and commerce lay behind blended learning.

The key to leveraging this leap of recognition is to enable knowledge workers to access learning content through the most relevant learning delivery channnel for their skill level. This can be illustrated by positioning the learning delivery channels along the learning-curve model. The picture that is formed by this allocation of technologies along the curve is what Dillon & Hallett call the “E-Learning Curve” (see Figure 1).

The analogies to the conventional learning curve are apparent. Early in the learning process, workers using the conventional model of corporate training practices had to go off-the-job to attend classroom-based instruction. Knowledge workers in the e-learning ‘ecosystem’ learn from hours-long modules of Web-based training curricula. In this phase of the learning process, users of both conventional training and e-learning are removed from their job context.

As learners progress along the curve of the model, the allocation of learning delivery channels becomes more heavily influenced by e-learning modalities. With Web-based training (as with its instructor-led counterpart) the domains of usage are relatively limited. The only time it makes sense to pull workers off their jobs for training is limited precisely to those times when no alternative is available or resource-efficient. Off-the-job forms of training make good business sense only when workers are at the bottom of the learning curve and are not yet equipped to perform tasks at any acceptable level of competence.

More…

_______________________

References:

Dillon, P. & Hallett, C. (2001, October). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

Morrison, D., (2003) The search for the holy recipe. [Internet] Available from: http://www.morrisonco.com/downloads/blended_learning_holy_recipe.pdf Accessed 14 May 2008.

–

These earliest stages can also be considered the “off-the-job training” segment meaning they are also the most expensive to the sponsoring organization.

As learners move up the curve, they move out of the classroom and into an on-the-job learning environment. As they approach higher levels of expertise, learners require more one-on-one or individualized approaches to learning. Because each worker has a unique set of knowledge and skill gaps separating him/her from the highest levels of expertise, it will take the mentoring intelligence of a live expert to detect and redress those gaps.

Over time, and as they acquire expertise through experiential learning knowledge workers will exhaust the list of colleagues upon whom they can rely for coaching and will need to rely upon their their own skills, knowledge and expertise to improve their performance.

Dillon & Hallett note that

one of the recent maturational leaps for e-learning has been the recognition that no one form of learning technology is capable of addressing the diverse range of learning modality requirements found in the modern workplace.

The authors labelled this “leap of recognition” as “the blended solution.” I would suggest that this use of terminology reflects the time that the white paper was authored (2001): a more appropriate use of language would be to call each modality a learning delivery channel. Learning delivery channels can include different technologies – virtual classrooms, EPSS, software simulations, online collaboration, self-paced e-learning, knowledge management – they are much more than this: for example mentoring is a learning delivery channel, such activities can can be undertaken in a face-to-face or an online environment using video conferencing.

As Don Morrison says:

I find it difficult to give ‘blended learning’ the same respect I give, say, blended whiskey which is produced by the highly skilful blending of between 20 and 50 different ‘single’ malt and grain whiskies—of differing ages and from different regions and distilleries—to create a new brand with its own unique character. The greater the number of component whiskies, the greater the consistency of the resulting blend. If one of the contributing distilleries goes out of business, the blenders can achieve the same end result by adjusting the blend of the other single whiskies. If only such art, science, and commerce lay behind blended learning.

The key to leveraging this leap of recognition is to enable knowledge workers to access learning content through the most relevant learning delivery channnel for their skill level. This can be illustrated by positioning the learning delivery channels along the learning-curve model. The picture that is formed by this allocation of technologies along the curve is what Dillon & Hallett call the “E-Learning Curve” (see Figure 1).

The analogies to the conventional learning curve are apparent. Early in the learning process, workers using the conventional model of corporate training practices had to go off-the-job to attend classroom-based instruction. Knowledge workers in the e-learning ‘ecosystem’ learn from hours-long modules of Web-based training curricula. In this phase of the learning process, users of both conventional training and e-learning are removed from their job context.

As learners progress along the curve of the model, the allocation of learning delivery channels becomes more heavily influenced by e-learning modalities. With Web-based training (as with its instructor-led counterpart) the domains of usage are relatively limited. The only time it makes sense to pull workers off their jobs for training is limited precisely to those times when no alternative is available or resource-efficient. Off-the-job forms of training make good business sense only when workers are at the bottom of the learning curve and are not yet equipped to perform tasks at any acceptable level of competence.

More…

_______________________

References:

Dillon, P. & Hallett, C. (2001, October). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

Morrison, D., (2003) The search for the holy recipe. [Internet] Available from: http://www.morrisonco.com/downloads/blended_learning_holy_recipe.pdf Accessed 14 May 2008.

–

In their white paper, they apply the concept of the conventional learning curve to the context of the corporate learning environment in order to supply an appropriate structure for understanding when and how different modalities of learning are used. Within their framework, the conceptualisation of the learning environment consists of systems to manage and support:

1. instructor-led training
2. cohesive team management
3. knowledge generation and sharing
4. performance support
5. content storage and retrieval
6. on-demand learning

In a similar fashion, I have suggested that we can apply Bloom’s Taxonomy of Educational Objectives to a conceptual model of a learning curve (see Figure 1) and begin to investigate how an “e-learning curve” based upon the modalities of that domain would align with the phases of learning in the traditional model.

In my view, a constructivist approach provides the most effective means of enabling adults to learn, particularly in the workplace. In the context of Bruner’s principles of constructivism (see Table 1) technologies like the Internet, websites, and virtual learning environments, applying collaborative learning, problem-based learning and goal-based mechanisms, making Open Source Software and Course- and Content Management Systems accessible to learners, and using e-learning applications like online conferencing and collaboration tools could be the foundation for these multiple constructivist conditions for learning. (Duffy & Jonassen 1992, Driscoll 1994; Schank 1994)

Table 1. Principles of constructivism (Toward a Theory of Instruction, p.225)

Principle		Definition
Readiness		Instruction must be concerned with the experiences and contexts that make the student willing and able to learn
Spiral organisation	Structure.	The content must be structured so that it can be grasped by the learner.
	Sequence.	Material must be presented in the most effective sequences.
Generation		“Going beyond the information given” – Instruction should be designed to facilitate extrapolation and or fill in the gaps

These characteristics provide an appropriate framework for knowledge workers to learn (and for the learning intervention), given that their ongoing development is based in the context of already-established cognitive schemata (from the learners’ perspective), the knowledge and skills are applied to solve real-world problems, and their expertise (behaviours) are typically used in collaboration with their peers to enhance the performance of organisations.

More tomorrow, when we apply e-learning modalities to the learning curve.

FÓGRA: Somebody asked me what does “fógra” mean.
Fógra (pron. fowgrah. equal emphasis on both syllables) is the Gaelic Irish word for “Notice.”
_____________________

References:

Bruner, J. S. (1966) Toward a Theory of Instruction. Cambridge, MA: Harvard University Press.

Dillon, P. & Hallett, C. (2001, October). Powering the leap to maturity: The eLearning ecosystem. Cisco Systems white paper.

Driscoll, M. P. (1994). Psychology of learning for instruction. Boston, MA. Allyn & Bacon.

Duffy, T. M. & Cunningham, D. J. (1996) Constructivism: Implications for the design and delivery of instruction. IN: Jonassen D. H. (Ed) Handbook of Research for Educational Communications and Technology (pp.170- 198). New York: Simon & Shuster Macmillan.

Schank, R. (1994) Active Learning Through Multimedia. IEEE Multimedia, 1(1), pp.69-78.
–