גלעד כץ

אקדמי בכיר

Predicting student exam's scores by analyzing social network data

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.

שפת פרסום אנגלית
דפים 584-595
סטטוס פרסום פורסם - 06.12.2012

Keywords

Data Mining
Machine Learning
Multi Graph
Score Prediction
Social Network Analysis
Web Log Analysis

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science
גישה למסמך
10.1007/978-3-642-35236-2_59
קבצים וקישורים אחרים
Link to publication in Scopus