User Modeling and User-Adapted Interaction
Toward evaluation of writing style: finding overly repetitive word use in student essays
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Automated Japanese essay scoring system based on articles written by experts
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
A Practical Activity Capture Framework for Personal, Lifetime User Modeling
UM '07 Proceedings of the 11th international conference on User Modeling
Correlation of grade prediction performance and validity of self-evaluation comments
Proceedings of the 14th annual ACM SIGITE conference on Information technology education
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These days, many university teachers are concerned about the increasing number of students whose motivation is declining. Some of them fall into a situation that they cannot recover from by themselves, and require assistance, but they hesitate to call for help. In order to recognize such students quickly and give guidance to them in class, we have collected time-series comments in the classroom and analyzed them. In the analysis, we divided the comments into the three time slots: P (Previous), C (Current), and N (Next), and quantify them so that we can infer the learning behaviors between the previous and the current classes. We call this analysis method the PCN method. The PCN method is useful for grasping students' learning status in the class. Some of our case studies illustrate the validity of the PCN method.