DocumentCode
514998
Title
Notice of Retraction
Using Comprehension Degree to Improve the Quality of Recommender System for Group Learning Support
Author
Xin Wan ; Jamaliding, Q. ; Xinyou Zhao ; Anma, F. ; Okamoto, T.
Author_Institution
Grad. Sch. of Inf. Syst., Univ. of Electro-Commun., Tokyo, Japan
Volume
2
fYear
2010
fDate
6-7 March 2010
Firstpage
3
Lastpage
6
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Recommender systems are now a popular research area and have become powerful tools to present personalized offers to users in many domains (e. g. e-commerce, e-learning). In this paper, we introduced an approach of personalization which extracts learners´ preferences based on learning processes and learning activities (e. g. writing summary) and provides more relevant, personalized recommendations. Keyword maps proposed with keywords and various relations among them in this article describe content of each learning object and knowledge of each learner existing. The research hypothesizes that keyword maps should help to increase both the relevance and complement of learning materials recommendation. Thereafter, learners´ comprehension degree and proficiency level are inferred by these keyword maps. According to the learners´ comprehension degree and learners´ proficiency levels, the system filters out the irrelevant learning processes and recommends the learning materials separately.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Recommender systems are now a popular research area and have become powerful tools to present personalized offers to users in many domains (e. g. e-commerce, e-learning). In this paper, we introduced an approach of personalization which extracts learners´ preferences based on learning processes and learning activities (e. g. writing summary) and provides more relevant, personalized recommendations. Keyword maps proposed with keywords and various relations among them in this article describe content of each learning object and knowledge of each learner existing. The research hypothesizes that keyword maps should help to increase both the relevance and complement of learning materials recommendation. Thereafter, learners´ comprehension degree and proficiency level are inferred by these keyword maps. According to the learners´ comprehension degree and learners´ proficiency levels, the system filters out the irrelevant learning processes and recommends the learning materials separately.
Keywords
computer aided instruction; information filtering; recommender systems; comprehension degree; group learning support; keyword map; learner preference; learning activity; learning material; learning object; learning process; personalized recommendation; proficiency level; recommender system; Collaboration; Computer science; Computer science education; Educational technology; Electronic learning; Filters; Information systems; Radio access networks; Recommender systems; Systems engineering education; comprehension degree; keyword map; proficiency level; recommender system;
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer Science (ETCS), 2010 Second International Workshop on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-6388-6
Type
conf
DOI
10.1109/ETCS.2010.444
Filename
5460089
Link To Document