Spoke to James Lester today and he spoke about a couple of projects. Looked up the following papers.
Kristy Elizabeth Boyer, Robert Phillips, Amy Ingram, Eun Young Ha, Michael D. Wallis, Mladen A. Vouk, and James C. Lester. Investigating the Relationship Between Dialogue Structure and Tutoring Effectiveness: A Hidden Markov Modeling Approach. International Journal of Artificial Intelligence in Education, 21(1-2), 65-81, 2011.
http://people.engr.ncsu.edu/keboyer/papers/boyer-ijaied2011.pdf
Eun Young Ha, Jonathan Rowe, Bradford Mott, and James Lester. Goal Recognition with Markov Logic Networks for Player-Adaptive Games. InProceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, Ontario, Canada, pp. 2113-2119, 2012. (invited paper)
http://www.intellimedia.ncsu.edu/papers/ha-aaai-2012.pdf
The first paper describes a project where they gathered massive amounts of data from one-on-one human tutoring interaction. This data was tagged by humans into various dialog act categories. They then used HMMs to discover tutorial interaction modes from the data. These modes were correlated with student learning data to identify the most effective modes.
The second paper is about using Markov Logic Networks for plan recognition in free-play games. Again generated lots of data by getting lots of folks to play a game. Player actions were then manually tagged with an associated goal. A standard MLN learning algorithm was used to learn a network from the data. Comparisons with other techniques (strawman, unigram, bigram models), MLN was found to be most accurate.
Kristy Elizabeth Boyer, Robert Phillips, Amy Ingram, Eun Young Ha, Michael D. Wallis, Mladen A. Vouk, and James C. Lester. Investigating the Relationship Between Dialogue Structure and Tutoring Effectiveness: A Hidden Markov Modeling Approach. International Journal of Artificial Intelligence in Education, 21(1-2), 65-81, 2011.
http://people.engr.ncsu.edu/keboyer/papers/boyer-ijaied2011.pdf
Eun Young Ha, Jonathan Rowe, Bradford Mott, and James Lester. Goal Recognition with Markov Logic Networks for Player-Adaptive Games. InProceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, Ontario, Canada, pp. 2113-2119, 2012. (invited paper)
http://www.intellimedia.ncsu.edu/papers/ha-aaai-2012.pdf
The first paper describes a project where they gathered massive amounts of data from one-on-one human tutoring interaction. This data was tagged by humans into various dialog act categories. They then used HMMs to discover tutorial interaction modes from the data. These modes were correlated with student learning data to identify the most effective modes.
The second paper is about using Markov Logic Networks for plan recognition in free-play games. Again generated lots of data by getting lots of folks to play a game. Player actions were then manually tagged with an associated goal. A standard MLN learning algorithm was used to learn a network from the data. Comparisons with other techniques (strawman, unigram, bigram models), MLN was found to be most accurate.
