PAWS Meeting April 13, 2007

First Paper: Design and Evaluation of an Adaptive Incentive Mechanism for Sustained Educational Communities
Presenter: Rosta Farzan

Online communities have faced insufficient participation. Even though providing incentives is such a good way to solve the problem, its affect gives too much, low quality participation. The authors proposed a new incentive mechanism to generate quality and quantity contribution from online users. From a related work, collaborative quality evaluation mechanism, not only need user participation but also quality control. In academia, we measure quality of papers or journals by counting times they are cited as well as in online communities, counting on numbers of clicks. But not every click means a positive attitude. In an explicit way, measuring on user rating has a drawback, "rich get richer".

The authors came up with the Comtella, online community system. Comtella provides an ability to share class-related web-resources. C0mtella also has a mechanism encouraging users to rate resources by providing a reward to user contribution. Users can contribute on the system by two ways: one is to post a new URL web resource or, two is to rate others' contribution. Each user receives a certain number of rating points to use. The number of rating points varies on a level of membership. Calculating adaptive rewards is done by two models, community model and individual model. Community model controls a number of contributions and usefulness of news resources. Individual model monitors quality of rating, based on the assumption that the average of all ratings the resource gets should be equal. From their experiment, users in the test group were more active in rating articles and the articles with higher ratings were more likely to be chosen to summarize. Limitations of Comtella are a scale of rating is too rigid, 1 and -1. The system should provide more on the aspect of rating such as originality, interestingness, relevancy etc. Another thing is the system should not reveal the absolute c-point. It should provide only relative ordering of the article.

Discussion
The features Comtella has should be integrated to CoPE. CoPE would get benefit from an ability for users to rate articles.

Second Paper: Classifying Learning Engagement Through Integration of Multiple Data Sources.
Presenter: Tomek Loboda

The paper is about combination of self reports from high school students and teacher reports. The system classifies action sequences into five finite state machines. The motivation of self reports is related to cluster of behaviors associated with learning success such as effective monitoring, study behaviors, etc. The motivation of learners and self-regulation can be a stronger predictor of learning performance than prior academic achievement. The web-based tutoring system for high school mathematics was set to prepare students for high-quality exams such as SAT.

In summary, sources can be integrated and used to classify students' beliefs into learning situation. Self reports from learners can be identified as disengaged but like other systems that students use ITS in a manner eliciting learning pattern. Results indicated that there is possibility that if we seed this models in advance with learner profile data which is timely and inexpensive to elicit and with strategies quite predictive will be used once when student begin studying with the ITS.