TY - GEN
T1 - Enabling grid computing for examination feedback analysis in learning system
AU - Wang, Chih Ming
AU - Chiang, Ming Chao
AU - Yang, Chu Sing
N1 - Funding Information:
This work was supported by ONR Contracts #N00014-85-K-0488, #N00014-91-J-1142 and the Laboratoire de Recherche en Informatique, CNRS, Univ. Paris Sud.
PY - 2008
Y1 - 2008
N2 - In this paper, we present a distributed computing scheme for examination feedback analysis based on grid. The proposed scheme is motivated by the observation that as the number of examinations increases, the feedback analysis would become extremely slow if a single server is used. Grid computing can be used to solve the problem by evenly distributing the workload of the system to all the available nodes, thus greatly enhancing the performance of feedback analysis in a learning system. We use Hyper Grid Learning System (HGLS)-a learning system with examination service-we developed as an exampIe to describe the proposed scheme. We note that although discussed in the context of HGLS, the proposed scheme can be easily adapted and applied to all the e-Iearning systems based on grid computing. To evaluate the performance of the proposed scheme, we compare the performance of the proposed scheme with single node servers. Our experimental result shows that the proposed scheme can greatly enhance the performance of the feedback analysis in a learning system base on grid.
AB - In this paper, we present a distributed computing scheme for examination feedback analysis based on grid. The proposed scheme is motivated by the observation that as the number of examinations increases, the feedback analysis would become extremely slow if a single server is used. Grid computing can be used to solve the problem by evenly distributing the workload of the system to all the available nodes, thus greatly enhancing the performance of feedback analysis in a learning system. We use Hyper Grid Learning System (HGLS)-a learning system with examination service-we developed as an exampIe to describe the proposed scheme. We note that although discussed in the context of HGLS, the proposed scheme can be easily adapted and applied to all the e-Iearning systems based on grid computing. To evaluate the performance of the proposed scheme, we compare the performance of the proposed scheme with single node servers. Our experimental result shows that the proposed scheme can greatly enhance the performance of the feedback analysis in a learning system base on grid.
UR - http://www.scopus.com/inward/record.url?scp=63749093503&partnerID=8YFLogxK
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U2 - 10.1109/ICDFMA.2008.4784423
DO - 10.1109/ICDFMA.2008.4784423
M3 - Conference contribution
AN - SCOPUS:63749093503
SN - 9781424423132
T3 - 2008 1st International Conference on Distributed Frameworks and Application, DFmA 2008
SP - 115
EP - 120
BT - 2008 1st International Conference on Distributed Frameworks and Application, DFmA 2008
T2 - 2008 1st International Conference on Distributed Frameworks and Application, DFmA 2008
Y2 - 21 October 2008 through 22 October 2008
ER -