Algorithms for clustering data
Algorithms for clustering data
Personalized information delivery: an analysis of information filtering methods
Communications of the ACM - Special issue on information filtering
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Real time video scene detection and classification
Information Processing and Management: an International Journal - Special issue on progress toward digital libraries
A recipe based on-line food store
Proceedings of the 5th international conference on Intelligent user interfaces
A new content-based access method for video databases
Information Sciences: an International Journal
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Discovering learning patterns from Web logs by concept transformation analysis (poster session)
Proceedings of the 5th annual SIGCSE/SIGCUE ITiCSEconference on Innovation and technology in computer science education
Targeting the right students using data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A music recommendation system based on music data grouping and user interests
Proceedings of the tenth international conference on Information and knowledge management
Towards more conversational and collaborative recommender systems
Proceedings of the 8th international conference on Intelligent user interfaces
Recommendations without user preferences: a natural language processing approach
Proceedings of the 8th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Proposing an interactive speaking improvement system for EFL learners
Expert Systems with Applications: An International Journal
Data mining for adaptive learning in a TESL-based e-learning system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Collaborative filtering based on significances
Information Sciences: an International Journal
Learning with personalized recommender systems: A psychological view
Computers in Human Behavior
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
| Hi-index | 12.06 |
This paper proposes an ESL recommender teaching and learning system capable of generating for ESL instructors practical information on problems and questions of grammar their students encounter. Not only does the system assist teachers to identify students' specific difficulties and weaknesses in learning, it can also provide data of recommendation that helps the student to find out his or her weak points in learning and offers improvement recommendations. In general, instructors can easily find out the number of students who have failed their exams, but have trouble identifying their real difficulties in learning. Based on the students' testing records, the system works to identify and find those problems, and then comes up with its suggestions for designing new teaching strategies. Besides, the information so produced can also be helpful for the students themselves to improve their grammar. The experiment in this study is based on real students' testing data, and a detail processing is developed that incorporates the advantages of the clustering technology. The system as here proposed has proved to be impressively effective, its performance having been tracked for one year.