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

Managing Information Overload - Teachable Media Agents
Harri Ketamo
Satakunta University <strong>of</strong> Applied Sciences, Finland
harri.ketamo@samk.fi
Abstract: Social media services, such as YouTube, Flickr and Slideshare, contain enormous number <strong>of</strong> content
valuable for education. If <strong>the</strong> requested <strong>the</strong>me can be effectively searched or recognized, teacher can easily
construct <strong>the</strong> course material from social media sources. However, <strong>the</strong> search engines are not optimal for
educational purposes: Search engines can list numerous pieces <strong>of</strong> content that matches more or less perfectly to
keywords. After search <strong>the</strong>re are thousands <strong>of</strong> pieces <strong>of</strong> content to check manually if <strong>the</strong>y really fit to <strong>the</strong>
requested subject. A common method to increase information accessibility in social media applications is tagging.
However, when tags are used only as single words, we easily end up to information overload. In this study <strong>the</strong>
general aim is to construct teachable agents that can learn conceptual structures in terms <strong>of</strong> conceptual learning.
Problems related to information retrieval or semantics are widely studied, but not from learning point <strong>of</strong> view. By
combining <strong>the</strong>ories about cognitive psychology <strong>of</strong> learning and machine learning, we could make a completely
new approach to decrease information overload. According to results, we can say that simulating human way to
learn fits for this purpose.
Keywords: information overload, data mining, teachable agents, social media
1. Introduction
Social media services, such as YouTube, Flickr and Slideshare, contain enormous number <strong>of</strong> content
valuable for education. If <strong>the</strong> requested <strong>the</strong>me can be effectively searched or recognized, teacher can
easily construct <strong>the</strong> course material from social media sources. However, <strong>the</strong> search engines are not
optimal for educational purposes: Search engines can list numerous pieces <strong>of</strong> content that matches
more or less perfectly to keywords. After search <strong>the</strong>re are thousands <strong>of</strong> pieces <strong>of</strong> content to check
manually if <strong>the</strong>y really fit to <strong>the</strong> requested subject. Recently, e.g. Google had added semantics into its
searches, but many educational subjects require more detailed conceptual models for successful
content personalization. Fur<strong>the</strong>rmore, <strong>the</strong>re are o<strong>the</strong>r personalization and adaptation solutions for
social media (e.g. Ahn, Brusilovsky, He, Grady & Li, 2008).
A common method to increase information accessibility in social media applications is tagging.
However, when tags are used only as single words, we easily end up to information overload.
Fur<strong>the</strong>rmore, in social media, we do not have standardized way to tag content. In fact, tagging <strong>the</strong>
content in an optimal way is a difficult task for several reasons: Cultural background, educational
background, community and its social behavior, as well as context where tagging is constructed
affects enormously to <strong>the</strong> selection <strong>of</strong> tags. Term ‘context’ can be understood in many ways. In this
study, context is understood to cover all conditions, physical, social and mental, which can be
considered as causes or consequences <strong>of</strong> activity.
Tagging is very subjective and numerous research is done in order to improve user experiences and
information retrieval in social media (e.g. Agichtein, Castillo, Donato, Gionis & Mishen, 2008;
Heymann, Koutrika & Garcia-Molina 2008; Sigurbjörnsson & van Zwol 2008). Unclear, or in worst
case misleading, tagging leads to information loss in social media. Especially, constructing storytelling
or narration between pieces <strong>of</strong> user generated content becomes impossible without socially
constructed tagging semantics. Fur<strong>the</strong>rmore, tagging can be seen as a one key element when
building platforms for personalized and adaptive media services.
Probability to choose a tag is only about frequencies. The real challenges are related to explanative
power <strong>of</strong> tags: if some tag is very frequent, its explanative power is low. Fur<strong>the</strong>rmore, when <strong>the</strong> tag is
used rarely, it is not useful for searches. By using complex semantics between tags, we can improve
<strong>the</strong> usability <strong>of</strong> <strong>the</strong> whole tagging system.
Explanative power is currently in many systems based only on matches <strong>of</strong> tags (or in worst case, just
one tag) without any intelligence. The better solution is to use semantics between tags. For example,
we are searching content from YouTube about applications <strong>of</strong> Gestalt psychology in usability
engineering. We use tags ‘usability’ and ‘gestalt psychology’. By typing both tags to search, we get
only 2 videos that match <strong>the</strong> search (tested on 30. April 2009). By using tag ‘gestalt psychology’ we
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