TY - GEN
T1 - Performance analysis of parallel particle swarm optimization based clustering of students
AU - Govindarajan, Kannan
AU - Boulanger, David
AU - Seanosky, Jeremie
AU - Bell, Jason
AU - Pinnell, Colin
AU - Kumar, Vivekanandan Suresh
AU - Kinshuk,
AU - Somasundaram, Thamarai Selvi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/14
Y1 - 2015/9/14
N2 - While accurate computational models that embody learning efficiency remain a distant and elusive goal, big data learning analytics approaches this goal by recognizing competency growth of learners, at various levels of granularity, using a combination of continuous, formative, and summative assessments. Our earlier research employed the conventional Particle Swarm Optimization (PSO) based clustering mechanism to cluster large numbers of learners based on their observed study habits and the consequent growth of subject knowledge competencies. This paper describes a Parallel Particle Swarm Optimization (PPSO) based clustering mechanism to cluster learners. Using a simulation study, performance measures of quality of clusters such as the Inter Cluster Distance, the Intra Cluster Distance, the processing time and the acceleration values are estimated and compared.
AB - While accurate computational models that embody learning efficiency remain a distant and elusive goal, big data learning analytics approaches this goal by recognizing competency growth of learners, at various levels of granularity, using a combination of continuous, formative, and summative assessments. Our earlier research employed the conventional Particle Swarm Optimization (PSO) based clustering mechanism to cluster large numbers of learners based on their observed study habits and the consequent growth of subject knowledge competencies. This paper describes a Parallel Particle Swarm Optimization (PPSO) based clustering mechanism to cluster learners. Using a simulation study, performance measures of quality of clusters such as the Inter Cluster Distance, the Intra Cluster Distance, the processing time and the acceleration values are estimated and compared.
KW - Clustering
KW - E-learning
KW - Hadoop distributed file system (HDFS)
KW - Learning analytics
KW - Parallel particle swarm optimization (PPSO)
KW - Parallel processing
UR - http://www.scopus.com/inward/record.url?scp=84961716014&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2015.136
DO - 10.1109/ICALT.2015.136
M3 - Published Conference contribution
AN - SCOPUS:84961716014
T3 - Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015
SP - 446
EP - 450
BT - Proceedings - IEEE 15th International Conference on Advanced Learning Technologies
A2 - Chen, Nian-Shing
A2 - Liu, Tzu-Chien
A2 - Kinshuk, null
A2 - Huang, Ronghuai
A2 - Hwang, Gwo-Jen
A2 - Sampson, Demetrios G.
A2 - Tsai, Chin-Chung
T2 - 15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015
Y2 - 6 July 2015 through 9 July 2015
ER -