Student Performance Monitoring with Business Intelligence

Abstract

Business Intelligences System (or BIS) is a potential eld in computer science. It has much commercial potential and its multi billion US $ market with rapidly increase year by year. Today Business Intelligence (BI) is capable of multidimensional analysis of data to see 360 degree business insight, statistical analysis, and forecasting to help better decision support systems. BI systems provide historical, current and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data. And the concept of BI can be used in di erent elds. Today in Education sector E learning is fast growing and famous concept. Almost all of Universities and others Education program providers use E learning as most e ective and e cient technique to reach their students. But there are many barriers that must be overcome in E learning approach. One of major problem is that students feel alienate due to lack of interaction and support. Students realize the situation and actual performances of them after put in to trouble only. The author proposes solution to address the issue with Distance Learning is Student Performance Monitoring System (SPMS) by using the Business Intelligence system concepts. A wide range of approaches and techniques of BI have been explored in attempts to build SPMS systems. Of these, Data warehousing and Data mining techniques have proved very promising. The propose SPMS architecture consist ve components as data sources, staging data bases, OLAP server, mining models and Front end. Registration, Examination and LMS data are the Source data for SPMS. The staging data bases are doing ETL (Extracting, Transforming and Loading) processes. The staging data bases consist of 3 databases, Extraction database which is linking database, Transformation database which is initial Data warehouse of SPMS and SPMS data warehouse is the interface for OLAP. Data cubes use to show the multi dimensional data models of SPMS data warehouse. Di erent data mining models are used to extract hidden patterns from the data cubes. These models are use to forecast Student Performance Level (SPL). Base on the SPL student will be given feed backs to maintain particular SPL or Increase the SPL semester by semester. SPMS for BIT program of UCSC successfully achieves its stated requirements and objectives with the artifact. However the system is still in prototype stage and real life application require optimizations and enhancements to the Data mining models and User interfaces. The project as a whole enabled me to enhance my skills multiple subject areas. This project would be of much interest to those in both the computer as well as management elds. The project as a whole enabled me to enhance my skills multiple subject areas.