Volume 18, No. 6, 2021
A Hybrid Data Mining Approach To Predict Undergraduate Student Academic Performance Predictions
Rashmi V. Varade , Dr. Blessy Thankachan
Abstract
For students, professors, and academic administrators, predicting student success is a critical issue. Teachers may find it useful to use the findings of a prediction model to determine the level of their present pupils and take proactive initiatives in their teaching tactics. It might also be useful in implementing appropriate academic initiatives at the programme level, as well as analysing student weaknesses. The rapid advancement of technology in recent years has made it possible to collect massive amounts of data, such as student data, alumni data, course data, and much more, all of which are stored in databases. We need to be able to extract important information from databases. Data mining, often known as "Knowledge Discovery in Databases," is a technique for extracting information from databases. Many tools and algorithms, such as decision trees, KNN, Random forest Naive Bayes, support vector machine, and others, may be used in data mining in an academic setting to enhance creating techniques for discovering academic databases, which is referred to as 'Educational Data Mining. So we use Hybridisation technique in which the genetic algorithm is used with SVM algorithm which gives highest accuracy than other algorithms.
Pages: 400-404
Keywords: Genetic Algorithm, Student Graduation, Hybrid Optimization