Be Brief, And They Shall Learn: Generating Concise Language Feedback for a Computer Tutor

To investigate whether more concise Natural Language feedback improves learning, we developed two Natural Language generators (DIAG-NLP1 and DIAG-NLP2), to provide feedback in an Intelligent Tutoring System that teaches troubleshooting. We systematically evaluated them in a three way comparison that included the original system, which generates overly repetitive feedback. We found that DIAG-NLP2, the generator which intuitively produces the best, corpus-based language, does engender the most learning. Distinguishing features of the more effective feedback are: it obeys Grice's maxim of brevity, it is more directive and uses a specific type of referring expressions. Interestingly, simpler ways of restructuring the original repetitive feedback as done in DIAG-NLP1, such as exploiting the hierarchical structure of the domain, were not effective. Since the design of interfaces to Intelligent Tutoring Systems often includes verbal feedback, we suggest that: if the number of different c...

• Type: Journal
• Year: 2008
• Where: AIEDU
• Authors: Barbara Di Eugenio, Davide Fossati, Susan M. Haller, Dan Yu, Michael Glass
• Keywords: AIEDU 2007, AIEDU 2008, Feedback, Intelligent Tutoring Systems, Natural Language