Proceedings of the 8th International Conference on Intellectual ...

Harri Ketamo
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Adaptation in web-bases systems can be seen as being high end personalization: In adaptation, <strong>the</strong>
system optimizes <strong>the</strong> navigation paths with technologies that can be divided into two main groups:
static adaptation (indirect) and dynamic adaptation (direct). In static adaptation <strong>the</strong> rules are fixed
beforehand by developers. In dynamic adaptation <strong>the</strong> system tracks <strong>the</strong> user and optimizes <strong>the</strong>
navigation paths according to <strong>the</strong> user's behaviour. Dynamic adaptation <strong>of</strong> system requires at <strong>the</strong>
least a user model, a context model and artificial intelligence. The social dimension should not be
forgotten: In very large samples, <strong>the</strong> most successful navigation paths may contain valuable
guidelines for adaptation. Because <strong>the</strong> idea <strong>of</strong> adaptive educational systems is to produce individual
and optimized learning experiences <strong>the</strong> high end user models as well as methods are relatively
complex (e.g. Brusilovsky, 2001).
In terms <strong>of</strong> constructive psychology <strong>of</strong> learning, people actively construct <strong>the</strong>ir own knowledge
through interaction with <strong>the</strong> environment and through reorganization <strong>of</strong> <strong>the</strong>ir mental structures. The
key elements in learning are accommodation and assimilation. Accommodation describes an event
when a learner figures out something radically new, which leads to a change in his/her mental
conceptual structure. Assimilation describes events when a learner streng<strong>the</strong>ns his/her mental
conceptual structure by means <strong>of</strong> new relations (Mayer 2004). These key components,
accommodation and assimilation are <strong>the</strong> most important concepts behind <strong>the</strong> study. In o<strong>the</strong>r words,
when new concepts are added into semantic network, accommodation takes place. When <strong>the</strong> existing
concepts receive new relations, we discuss about assimilation.
2. Research task
In this study <strong>the</strong> general aim is to construct methods for teachable, adaptive and self-organizing
tagging by applying complex semantic relations between <strong>the</strong> tags. Subtasks are 1) construct methods
for building self-organizing tag clouds, and 2) construct methods for user-based teaching and
refinement <strong>of</strong> <strong>the</strong> semantics.
In this study we have designed teachable agents that can learn conceptual structures in terms <strong>of</strong>
conceptual learning. The agents are based on authors’ previous work, AnimalClass (e.g. Ketamo &
Suominen 2008; Ketamo & Kiili 2010) and Artificial Labor (Ketamo 2008; Ketamo 2010). Preliminary
results <strong>of</strong> this project have been published (Ketamo 2009; Ketamo 2011).
In AnimalClass <strong>the</strong> learner can teach conceptual structures about ma<strong>the</strong>matics, sciences, languages
and arts to virtual characters (teachable agents). Artificial labour is based on complex modelling which
means, that <strong>the</strong>re are several archetypes <strong>of</strong> users as well as variance inside <strong>the</strong> archetypes. The
common denominator for all is that everything is based on human user- and group behaviour.
The main difference between Teachable Media Agents and AnimalClass is in philosophy <strong>of</strong> learning.
When <strong>the</strong> game characters in AnimalClass were taught in terms <strong>of</strong> inductive learning and in Artificial
Labor in terms <strong>of</strong> reinforcement learning, <strong>the</strong> Teachable Media Agents are taught in both deductive
and inductive means.
3. Results
Technologically and computationally <strong>the</strong> Teachable Media Agents are based on Semantic Neural
Networks. The generalized framework <strong>of</strong> Teachable Media Agents is presented at figure 1. At <strong>the</strong>
beginning, <strong>the</strong> end user gets his/her own agent, with which <strong>the</strong> user interacts. This personal media
agent utilizes all <strong>the</strong> o<strong>the</strong>r agents available on <strong>the</strong> system.
There are two types <strong>of</strong> agents. First type <strong>of</strong> agents (figure 1, small agents on <strong>the</strong> left) reads social
media services and organizes <strong>the</strong> information into databases. The first types <strong>of</strong> agents are pre taught:
<strong>the</strong>y cannot learn more. Therefore end user interacts only with second type <strong>of</strong> agents.
Second type <strong>of</strong> agents (figure 1, centered agent) interacts with end users and builds all personalized
semantic networks. These personalized semantic networks consist <strong>of</strong> relations between concepts
found from tags, titles and comments. The pieces <strong>of</strong> contents are connected into one or more
concepts in this high level semantic network. These agents can learn by <strong>the</strong> feedback <strong>of</strong> evaluations
made by <strong>the</strong> end user. In o<strong>the</strong>r words, second types <strong>of</strong> agents tries to match <strong>the</strong> high level
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