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Personalized Collaboration 269
failed to understand what they were saying, and study authors concluded that unless
a general intelligence can be utilized the dialogue context must greatly limit what can be
said in social dialogue, without destroying the natural and relaxed feel.
9.4 Summary and Open Problems
Collaborative learning tools currently do not really understand the underlying users’ interaction.
Support is predominantly on the structural level (e.g. symbolic actions within
a workspace), and tools attempt to "understand" unstructured interactions such as natural
language dialogues by the use of information extraction and machine learning methods,
which unfortunately are rather inaccurate for high-level natural language tasks. The main
differences between individual systems thus correspond to the level of analysis they perform,
and thus the amount of "reasoning" they engage into.
There are several open problems in the area of collaborative support. As for group
formation strategies, previous attempts on group creation focus largely on satisfying preset
conditions determined before the actual collaboration occurs, or utilize only shallow
statistical measures of previous collaborations’ successes.
To make the whole collaborative experience for the user personalized and more sociable
we have to ensure that in the group creation process we take more advantage of previous
user’s experiences with other collaboration partners which, in fact, devise an overall
profile of what the specific user awaits from the collaboration, and thus should be looked
upon in the first place. Furthermore, analyzing time-related data of how users interact
with each other inside the group and with outsiders, and how the group evolves through
the several developmental stages, provides interesting avenues for further research.
In the area of analyzing collaborative interaction, contemporary Web 2.0 social environments
systems facilitate the creation of vast amounts of user-generated discourse. Analyzing
this interaction content and extracting knowledge from it assists in building structured
knowledge bases for improving the dialogue capability of artificial agents that can
be further used to elicit additional interactions. Researches try to build computational
models of discourse.
To extract relevant knowledge from textual conversations an integral approach for
identifying both semantic and pragmatic features is required. Fully-automated annotation
methods seem to fail in this respect and semi-automated methods, in which statistical
and/or machine learning algorithms pre-process the annotation in the first step while users
make final adjustments in the second step, are proposed. The problem here is: why, what
and how to annotate to enable personalized and more sociable interactions for an effective
collaboration to happen.
References
[1] Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social
networks: membership, growth, and evolution. In Proc. of 12th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, ACM Press, pp. 44–54, 2006.
[2] Baker, M., Lund, K.: Flexibly structuring the interaction in a CSCL environment. In Proc.
of the European Conference on Artificial Intelligence in Education, pp. 401–407, 1996.