A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
MyPeerReview: an online peer-reviewing system for programming courses
Proceedings of the 10th Koli Calling International Conference on Computing Education Research
e-learning experience using recommender systems
Proceedings of the 42nd ACM technical symposium on Computer science education
Predicting correctness of problem solving in ITS with a temporal collaborative filtering approach
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
System for recommendation of information based on a management content model using software agents
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Hi-index | 0.00 |
Collaborative information filtering techniques play a key role in many Web 2.0 applications. While they are currently mainly used for business purposes such as product recommendation, collaborative filtering also has potential for usage in eLearning applications. The quality of a student provided solution can be heuristically determined by peers who review the solution, thus effectively disburdening the workload of tutors. This paper presents a collaborative filtering approach which is specifically designed for eLearning applications. A controlled lab study with the system confirmed that the underlying algorithm is suitable as a diagnostic tool: The system-generated quality heuristic correlated highly with an expert-provided manual grading of the student solutions. This was true independent of whether the students provided fine-grained or coarse-grained evaluations of peer solutions, and independent of the task type that the students worked on. Further, the system required only few peer evaluations in order to achieve an acceptable prediction quality.