Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques
This work addresses enrollment management for higher education institutions, but it appears incremental as it applies existing data mining methods without specifying novel breakthroughs.
The study tackled the problem of rapidly growing educational data in higher education by proposing a classification model for student enrollment using data mining techniques, aiming to extract valuable patterns to improve education standards.
Data Mining is the process of extracting useful patterns from the huge amount of database and many data mining techniques are used for mining these patterns. Recently, one of the remarkable facts in higher educational institute is the rapid growth data and this educational data is expanding quickly without any advantage to the educational management. The main aim of the management is to refine the education standard; therefore by applying the various data mining techniques on this data one can get valuable information. This research study proposed the "classification model for the student's enrollment process in higher educational courses using data mining techniques". Additionally, this study contributes to finding some patterns that are meaningful to management.