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@mastersthesis{Ziai-09, 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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