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  author = {Ramon Ziai},
  title = {A Flexible Annotation-Based Architecture for Intelligent Language
	Tutoring Systems},
  school = {Universit\"{a}t T\"{u}bingen, Seminar f\"{u}r Sprachwissenschaft},
  year = {2009},
  month = {April},
  abstract = {This thesis presents a general architecture for Intelligent Language
	Tutoring Systems. The architecture makes use of annotation-based
	processing to encode different linguistic information at deep and
	shallow levels in a flexible manner and associate it with the learner
	input string. At the same time, it leaves the original input untouched,
	which is different from traditional pipeline approaches used in other
	Intelligent Computer-Assisted Language Learning systems. We show
	that this is necessary in order for feedback to be both error-specific
	and useful to the learner. Furthermore, the architecture incorporates
	a general mechanism for the use of activity models in guiding processing
	and feedback. The approach uses error types as a means of formulating
	pedagogical goals of individual activities. These error types are
	then mapped to analysis requirements that can be used to adapt processing
	of the learner input to the activity. More importantly, error types
	are used to influence the feedback strategy towards the errors that
	a particular activity centers on. We also demonstrate a similar use
	of error counts in the learner model to guide feedback. We have evaluated
	the system on the basis of example scenarios. They show that we are
	indeed able to give more useful feedback than the baseline system
	TAGARELA (Amaral, 2007) in a number of cases. This is the result
	of both annotation-based processing and integration of activity and
	learner model into the feedback process.},
  file = {Ziai-09.pdf:Ziai-09.pdf:PDF},
  owner = {rziai},
  timestamp = {2009.07.23},
  url = {http://www.sfs.uni-tuebingen.de/~rziai/papers/Ziai-09.pdf}

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