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Advances in E-learning-Experiences and Methodologies

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Swarm-Based Techniques <strong>in</strong> E-Learn<strong>in</strong>g<br />

arcs are only updated when a user goes through<br />

that arc or through a neighbour (those arcs that<br />

lead to the same exercise). When a student f<strong>in</strong>ishes<br />

exercise A, positive <strong>and</strong> negative pheromones are<br />

laid on the last four arcs that she followed. Thus,<br />

the pheromones on those arcs are <strong>in</strong>creased. At<br />

the same time, pheromone values on all the other<br />

arcs that lead to the same exercise are decreased.<br />

This can be seen as competition between arcs,<br />

as those arcs that are used more often will have<br />

high pheromones values, while those that are<br />

seldom used will see that their pheromones level<br />

“evaporate” rapidly.<br />

The work by Valigliani (2005) has shown<br />

several differences between the application of<br />

ant-<strong>in</strong>spired heuristics to technical problems (like<br />

those referenced <strong>in</strong> the background section), <strong>in</strong><br />

which “ants” are little software agents, <strong>and</strong> their<br />

application to social problems, <strong>in</strong> which real people<br />

are used as “ants.”<br />

First, real students are not as altruistic as ants<br />

(either natural or artificial) are: students will not<br />

willfully sacrifice themselves for the good of<br />

the community. In other words, a student does<br />

not expect to be lost <strong>in</strong> the exercise graph for the<br />

sake of exploration. All students want to learn.<br />

They are not <strong>in</strong>terested <strong>in</strong> optimiz<strong>in</strong>g the results<br />

of the group; they are <strong>in</strong>terested <strong>in</strong> optimiz<strong>in</strong>g<br />

their own results. This is only common sense, but<br />

it means that there are aspects of the ant-colony<br />

optimization heuristics that cannot be used or do<br />

not perform as expected.<br />

Secondly, the process of learn<strong>in</strong>g is one that<br />

must be optimized for each learner. The fact that a<br />

pedagogical path is optimized for a group does not<br />

mean that it is optimized for each of its members.<br />

In order to give an additional level of adaptation<br />

at a personal level, personal pheromones are used.<br />

These pheromones are left by each user as the<br />

user traverses the graph, <strong>and</strong> prevents the user<br />

from repeat<strong>in</strong>g the same exercises over <strong>and</strong> over<br />

aga<strong>in</strong>. Personal pheromones are laid by use <strong>and</strong><br />

evaporate with natural time. They are a multiplicative<br />

factor <strong>in</strong> the fitness function.<br />

A f<strong>in</strong>al characteristic of the Paraschool system<br />

is its ability to create new arcs that connect its<br />

exercises, apart from those that were set by the<br />

pedagogical team. Paraschool users can navigate<br />

<strong>in</strong> a guided fashion (as expla<strong>in</strong>ed above) or freely.<br />

Free navigation means that the students can access<br />

any of the exercises that are available on the<br />

site. Every time this happens, the system records<br />

the transition <strong>and</strong> creates a new arc from the last<br />

exercise performed by the student to the exercise<br />

that she has gone to. From that po<strong>in</strong>t on, that<br />

arc is considered like a normal arc of the graph<br />

(only with no start<strong>in</strong>g pedagogical weight) that is<br />

selectable <strong>in</strong> normal guided navigation, receive<br />

pheromones, <strong>and</strong> so forth. This is an <strong>in</strong>terest<strong>in</strong>g<br />

feature that allows f<strong>in</strong>d<strong>in</strong>g learn<strong>in</strong>g sequences<br />

that the pedagogical team had not thought about<br />

<strong>in</strong> the first place. Moreover, it opens the door to<br />

new exercises to be added to the system even if<br />

no pedagogical team creates a graph for them. As<br />

connections between the arcs will appear from free<br />

navigation, the algorithm will assign a fitness to<br />

those arcs over time, as pheromones are deposited<br />

on them with regard of success or failure of the<br />

students travers<strong>in</strong>g them. In the end, the new set<br />

of exercises will auto-organize themselves.<br />

Learn<strong>in</strong>g networks<br />

Learn<strong>in</strong>g networks (Koper, 2005) are flexible<br />

learn<strong>in</strong>g facilities oriented at support<strong>in</strong>g the<br />

needs of learners at various levels of competence<br />

throughout their lives. They support ubiquitous<br />

access to learn<strong>in</strong>g facilities at work or at home.<br />

Learn<strong>in</strong>g networks consist of learn<strong>in</strong>g events<br />

(called activity nodes) <strong>in</strong> a given doma<strong>in</strong>. Activity<br />

nodes can be associated to anyth<strong>in</strong>g that supports<br />

learn<strong>in</strong>g (e.g., Web resource, course, workshop,<br />

etc.). Both providers <strong>and</strong> learners can create new<br />

activity nodes or adapt exist<strong>in</strong>g ones (even delet<strong>in</strong>g<br />

nodes). Thus, a learn<strong>in</strong>g network represents<br />

a large <strong>and</strong> ever-chang<strong>in</strong>g set of activity nodes<br />

that provide learn<strong>in</strong>g activities for learners,<br />

from different providers <strong>and</strong> at different levels<br />

of expertise.<br />

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