International Journal of Advanced Computer Science and Applications · 2013 · 19 citations · 3 references
EngineeringBusiness IntelligenceData WarehouseEducationPattern MiningContent MiningInstitutional AnalyticsKnowledge Discovery In DatabasesInformation RetrievalData ScienceData MiningLarge-scale DataE-learning Application DomainData IntegrationKnowledge Discovery ProcessData WarehousingWeb DataLearner ProfilingKnowledge DiscoveryEducational Data MiningLearning AnalyticsComputer ScienceWeb Mining (Data Mining)Business Data MiningWeb Mining (Geotechnical Engineering)Web MiningFrequent Pattern MiningData Modeling
Data mining and warehousing are key for pattern detection and data management, e‑learning is a major application, yet no tools exist to assess learner performance unlike those for customer behavior. The study proposes a practical model and architecture that emphasizes integrating Web Services into e‑learning to enhance distance education. The authors examine data‑mining techniques that can improve web‑based learning environments. They analyze standards and system design, highlight the importance of Web Services, and show that e‑learning can be more efficient through web‑usage mining.
Data Mining and Data Warehousing are two most significant techniques for pattern detection and concentrated data management in present technology. ELearning is one of the most important applications of data mining. The foremost idea is to provide a proposal for a practical model and architecture. The standards and system structural design are analyzed here. This paper provides importance to the combination of Web Services on the e-Learning application domain, because Web Service is the most complex choice for distance education during these days. The process of e-Learning can be promising more efficiently by utilizing of Web usage mining. Mor07/e sophisticated tools are developed for internet customer’s behaviour to boost sales and profit, but no such tools are developed to recognize learner’s performance in e-Learning. In this paper, some data mining techniques are examined that could be used to improve web-based learning environments.
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