PAWS Meeting 2007-02-23
1. Personalized Course Navigation Based on Grey Relational Analysis
Paper presentation by Sharon
This paper introduced a study on a personalized course navigation based on a measure called WGRA (Weighted Grey Relational Analysis). It aims to let users browse open corpus educational Web resources and recommend them according to user interests. The task is done by the WGRA measure and implicit feedback information. It can provide easily maintained user models, low complexity for the computation required, and ease to add knowledge.
Even though this study seems rather simple, but it has a strong relevance to some of the projects done within our group such as KnowledgeSea, so it was recommended to the persons to check the paper.
2. Unsupervised and Supervised Machine Learning in User Modeling for Intelligent Learning Environment
Paper presentation by Tomek
The motivation here is to reduce the effort for constructing user models and improve transferability across applications. The proposed framework takes two learning steps, unsupervised and unsupervised in order to model students' learning during interaction with an education support systems (exploratory learning environment). The first step is done by clustering, where users' interaction behaviors are fed in. After the experts examined the clusterings, they label each of them according to the students' achievements and use them in the next step, online classification (k-means).
The ideas for applying the proposed approach to relevant projects were discussed and this paper was also recommended.
Paper presentation by Sharon
This paper introduced a study on a personalized course navigation based on a measure called WGRA (Weighted Grey Relational Analysis). It aims to let users browse open corpus educational Web resources and recommend them according to user interests. The task is done by the WGRA measure and implicit feedback information. It can provide easily maintained user models, low complexity for the computation required, and ease to add knowledge.
Even though this study seems rather simple, but it has a strong relevance to some of the projects done within our group such as KnowledgeSea, so it was recommended to the persons to check the paper.
2. Unsupervised and Supervised Machine Learning in User Modeling for Intelligent Learning Environment
Paper presentation by Tomek
The motivation here is to reduce the effort for constructing user models and improve transferability across applications. The proposed framework takes two learning steps, unsupervised and unsupervised in order to model students' learning during interaction with an education support systems (exploratory learning environment). The first step is done by clustering, where users' interaction behaviors are fed in. After the experts examined the clusterings, they label each of them according to the students' achievements and use them in the next step, online classification (k-means).
The ideas for applying the proposed approach to relevant projects were discussed and this paper was also recommended.
posted by Jae-wook at 11:55 AM
0 Comments:
Post a Comment
<< Home