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«Far East Journal of Experimental and Theoretical Artificial Intelligence Volume 1, Issue 2, 2008, Pages 87-125 Published online: August 12, 2008 This ...»

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The input understanding approach proved to be robust and successful at recognizing student utterances and responding appropriately. In addition, the users of this system exhibited significant improvements in learning outcomes. A detailed study of the educational results is presented elsewhere [54].

Table 7 summarizes the 35 sessions and the performance results described earlier. There were 1642 student dialogue turns in the 35 sessions, with the number of student turns ranging from 9 to 94 with a mean of 46.91. Most of these 1642 turns were very short. In fact, 449 of these turns consisted of only a single non-alphabetic symbol, most often a plus or a minus sign. The system corrected the student spelling appropriately in 91 cases, an average of 2.6 spelling errors per session, and failed with five words.

–  –  –

The input understanding component made six content errors in these 1642 turns (less than 0.4%); three of those led to serious misunderstanding. In the other three the student figured out what was going on and came up with an appropriate response. All of these errors involved erroneous spelling corrections and missing vocabulary items.

There were 86 inputs (5.3%) where the system decided it could not interpret the student input correctly and issued an error message explaining what kind of input it was looking for. In a few of these situations an expert human tutor could have made productive use of the student answer, given a more appropriate response, or changed to another tutorial strategy.

MICHAEL S. GLASS and MARTHA W. EVENS In summary, a simple, robust, finite-state, information extraction approach to processing student input performed well in this experiment, effectively detecting what the student was trying to tell us or else providing the appropriate error message over 99% of the time.

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–  –  –

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