Finding Patterns of Students' Behavior in Synthetic Social Networks

Abstract

Spectral clustering is a data mining method used for finding patterns in high dimensional datasets. It has been applied effectively to solve many problems in signal processing, bioinformatics, etc. In this paper spectral clustering was implemented to find students' patterns of behavior in an elearning system, to explore the relationship between the similarity of students' behavior and their academic performance.

Data Mining for Student Outcome Prediction on Moodle: a systematic mapping

“…Partitioning Methods [Gamulin et al 2016], [Moradi et al 2014], [Sorour et al 2014], [Romero et al 2013b], [Jovanovic et al 2012], [Mogus et al 2012], [López et al 2012], [Pardos et al 2012], [Kotsiantis et al 2010], [Obadi et al 2010], [Minaei-Bidgoli et al 2003] Association…”

“…Forums participation [Dascalu et al 2016], [Hung et al 2016], [Gasevic et al 2016], [Neto and Castro 2015], [Cambruzzi et al 2015], [Hu et al 2014], [Romero et al 2013b], [Romero et al 2013a], [Zafra and Ventura 2012], [López et al 2012], [Jovanovic et al 2012], [Mogus et al 2012], [Zafra et al 2011], [Obadi et al 2010], [Carmona et al 2010], [Zafra and Ventura 2009], Assessment data/grades [Kostopoulos et al 2015], ], [Lykourentzou et al 2009b], [Hu et al 2014], [Gasevic et al 2016], [You 2016], [Kotsiantis 2012], [Moradi et al 2014], [Jovanovic et al 2012], [Romero et al 2013a], [Černezel et al 2014], [Pardos et al 2012], [Carmona et al 2010], [Hung et al 2016] Interaction logs [Joksimović et al 2015], [Xing et al 2015], [Kotsiantis et al 2010], [Zacharis 2015], [You 2016], [Zorrilla and Garcia-Saiz 2014], [Cambruzzi et al 2015], [Gamulin et al 2016], [Sharma and Mavani 2011a], [Sorour et al 2014], [Romero et al 2008], [Sharma and Mavani 2011b] Quizzes data [Kato and Ishikawa 2013],…”

Automatic approaches to clustering occupational description data for prediction of probability of workplace exposure to beryllium

“…In a recent work, Obadi, et al [17] used Tolerance Rough Set clustering algorithm to cluster records published by the Digital Bibliography & Library Project (DBLP). Obadi, et al [17] conducted a comparative study evaluating the Tolerance Rough Set-based approach against other frequently used algorithms.…”

“…Obadi, et al [17] conducted a comparative study evaluating the Tolerance Rough Set-based approach against other frequently used algorithms. Kumar, et al used the Tolerance Rough Set method to successfully classify web usage data available through the UCI Machine Learning Archive [12].…”

A Network Science Perspective to Personalized Learning

“…Social network analysis (SNA) of students social networks on learning platforms helps in understanding various phenomenon, such as the group learning behavior of students, the correlation of students' position in social networks and their academic performance, information diffusion among students, the extent of the homophily in the classrooms, and if any student is isolated and needs help [1]. We can thus extract information straight from the social networking and communication pattern data from the collaborative online learning platform [2]. The analysis of such data can improve our understanding of teaching and learning patterns, such as how to increase collaboration among students, what topic to re-teach some topics if not clear to students, identify help-seeking students and provide special assistance and so on.…”