Friday, August 17, 2012

Skill Retention Research

Today I am looking up some research on skill retention and skill decay for a proposal.

References I looked up:

1. http://www.tatrc.org/conferences/MMVR_2011/ppt/ONeil-MMVR-CCC-2011.pdf

The above is a set of slides on the topic of skill retention in medicine. They advocate an ITS-like approach to addressing skill decay. A couple of points:

1. School knowledge decays less than (what? not mentioned).
2. Motor skills decay less than cognitive skills. Fall steeper and faster.
3. They recommend an approach of modeling learning skills and targeting decaying skills with lessons targeting a single skill.

2. http://www.owlnet.rice.edu/~antonvillado/courses/12a_psyc630001/Arthur,%20Bennett,%20Stanush,%20&%20McNelly%20(1998)%20HP.pdf

A meta analysis of factors that influence skill retention/decay.

1. Overlearning leads to better retention
2. Closed-loop task knowledge decays faster than Open-loop task expertise (close-loop task = those with a fixed sequence of tasks that have a definite beginning and end)
3. Speed tasks decay less than accuracy tasks
4. Physical skills decay less than cognitive skills
5. Skill on artificial tasks decays more than natural tasks
6. Studies that used recognition tests reported less skill decay than those that used recall tests
7. Skill decay is more apparent when the retention test does not have the same context and the learning context.

Friday, August 3, 2012

Research at NCSU Intellimedia Center

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 ApproachInternational 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.