Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Creating natural language tutorial dialogue systems that realize effective strategies is a central challenge for intelligent tutoring systems research. Traditional approaches generally require large development time, do not generalize ll across domains, and do not match the flexibility and natural language sophistication of human tutors. A promising approach that may offer several benefits is data-driven system development, in which a dialogue policy is learned from corpora of human tutorial dialogue. To date these learning approaches typically focus on optimizing the tutor’s choice of act, and do not explicitly model the instances in which the tutor chose not to act.  reports on a hidden Markov modeling (HMM) approach within human textual tutorial dialogue that explicitly represents the tutors’ choices not to intervene. The results show that an HMM that models tutor non-interventions predicts tutor moves significantly better than a model that does not explicitly represent the non-interventions. The findings have implications for automatically modeling tutorial strategies and for learning dialogue policies from corpora.
['Kristy Elizabeth Boyer', 'Robert Phillips', 'Eunyoung Ha', 'Michael D. Wallis', 'Mladen A. Vouk', 'James C. Lester']
I am writing a research paper on a idea Learning a Tutorial Dialogue Policy for Delayed Feedback
[{"from": "human", "value": "I am writing a research paper on a idea Learning a Tutorial Dialogue Policy for Delayed Feedback"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Creating natural language tutorial dialogue systems that realize effective strategies is a central challenge for intelligent tutoring systems research. Traditional approaches generally require large development time, do not generalize ll across domains, and do not match the flexibility and natural language sophistication of human tutors. A promising approach that may offer several benefits is data-driven system development, in which a dialogue policy is learned from corpora of human tutorial dialogue. To date these learning approaches typically focus on optimizing the tutor\u2019s choice of act, and do not explicitly model the instances in which the tutor chose not to act.  reports on a hidden Markov modeling (HMM) approach within human textual tutorial dialogue that explicitly represents the tutors\u2019 choices not to intervene. The results show that an HMM that models tutor non-interventions predicts tutor moves significantly better than a model that does not explicitly represent the non-interventions. The findings have implications for automatically modeling tutorial strategies and for learning dialogue policies from corpora."}]