Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Computers & Education - Documenting collaborative interactions: Issues and approaches
Face recognition from a single image per person: A survey
Pattern Recognition
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition across pose: A review
Pattern Recognition
Personalized image recommendation and retrieval via latent SVM based model
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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Modeling the in-class student social networks is a highly desired goal in educational literature. However, due to the difficulty to collect social data, most of the conventional studies can only be conducted in a qualitative way on a small-scale of dataset obtained through questionnaires or interviews. We propose to solve the problems of data collection, social network construction and analysis with multimedia technology, in the way that we can automatically recognize the positions and identities of the students in classroom and construct the in-class social networks accordingly. With the social networks and the statistics on a large-scale dataset, we have demonstrated that the pedagogical analysis for investigating the co-learning patterns among the students can be conducted in a quantitative way, which provides the statistical clues about why prior studies reach conflicting conclusions on the relation between the students' positions in social networks and their academic performances. The experimental results have validated the effectiveness of the proposed approaches in both technical and pedagogical senses.