@inproceedings{cdf4e20bb90b4d3eab75f74a24832bc6,
title = "Predicting student exam's scores by analyzing social network data",
abstract = "In this paper, we propose a novel method for the prediction of a person's success in an academic course. By extracting log data from the course's website and applying network analysis methods, we were able to model and visualize the social interactions among the students in a course. For our analysis, we extracted a variety of features by using both graph theory and social networks analysis. Finally, we successfully used several regression and machine learning techniques to predict the success of student in a course. An interesting fact uncovered by this research is that the proposed model has a shown a high correlation between the grade of a student and that of his {"}best{"} friend.",
keywords = "Data Mining, Machine Learning, Multi Graph, Score Prediction, Social Network Analysis, Web Log Analysis",
author = "Michael Fire and Gilad Katz and Yuval Elovici and Bracha Shapira and Lior Rokach",
year = "2012",
month = dec,
day = "6",
doi = "10.1007/978-3-642-35236-2_59",
language = "English",
isbn = "9783642352355",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "584--595",
booktitle = "Active Media Technology - 8th International Conference, AMT 2012, Proceedings",
note = "8th International Conference on Active Media Technology, AMT 2012 ; Conference date: 04-12-2012 Through 07-12-2012",
}