A Survey on approaches for predicting performance of students
Abstract
The Paper focuses on predicting performance of students; we have considered different cases and analyzed the methods adopted in various cases by various authors. The main intension behind this survey is to understand latest Research methods adopted along with their results.
Educational Data Mining (EDM) is an emerging field exploring data in educational context by applying different Data Mining (DM) techniques/tools. EDM inherits properties from areas like Learning Analytics, Psychometrics, Artificial Intelligence, Information Technology, Machine learning, Statics, Database Management System, Computing and Data Mining. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education.
In this Paper a survey has been carried out in three engineering colleges with an establishment of more than fifteen years, a total of two hundred and forty six students answered the questions and the reliability and validity of questionnaire was found to be good
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Introduction
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. It sits at the common frontiers of several fields including Data Base Management, Artificial Intelligence, Machine Learning, Pattern Recognition, and Data Visualization. From a statistical perspective it can be viewed as computer automated exploratory data analysis of (usually) large complex data sets. In spite of the somewhat exaggerated hype, this field is having a major impact in education
Severe challenges are being faced by students and alumni in higher education. Institutions would like to know, for example, which students will enroll in particular course programs and which students will need assistance in order to graduate. Are some students more likely to transfer than others? What groups of alumni are most likely to offer pledges? In addition to these challenges, traditional issues such as enrollment management and time-to-degree continue to motivate higher education institutions to search for better solutions.
Conclusion
Our result shall determine a correlation between the GPA at the undergraduate level and the one at the graduate level. This outcome shall emphasize the relevance of indicators of undergraduate achievements for graduate admissions decisionmaking. We shall attribute this improvement primarily to the completeness of our data, to the strong consecutive nature of the Computer Science curriculum, and to the fact that data were collected within one institution. Our outcomes shall be identification of most significant explanatory variable. Innovative methods will help in enhancing results and A positive relationship exists between Teaching and students.