Welcome to the InfoSci Platform
Reference Hub2
Deep Learning in Engineering Education: Implementing a Deep Learning Approach for the Performance Prediction in Educational Information Systems

Deep Learning in Engineering Education: Implementing a Deep Learning Approach for the Performance Prediction in Educational Information Systems

Deepali R. Vora, Kamatchi R. Iyer
Copyright: © 2021 |Pages: 34
ISBN13: 9781799821083|ISBN10: 1799821080|EISBN13: 9781799821106
DOI: 10.4018/978-1-7998-2108-3.ch010
Cite Chapter Cite Chapter

MLA

Vora, Deepali R., and Kamatchi R. Iyer. "Deep Learning in Engineering Education: Implementing a Deep Learning Approach for the Performance Prediction in Educational Information Systems." Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan Senthilnathan, et al., IGI Global, 2021, pp. 222-255. https://doi.org/10.4018/978-1-7998-2108-3.ch010

APA

Vora, D. R. & Iyer, K. R. (2021). Deep Learning in Engineering Education: Implementing a Deep Learning Approach for the Performance Prediction in Educational Information Systems. In K. Senthilnathan, B. Shanmugam, D. Goyal, I. Annapoorani, & R. Samikannu (Eds.), Deep Learning Applications and Intelligent Decision Making in Engineering (pp. 222-255). IGI Global. https://doi.org/10.4018/978-1-7998-2108-3.ch010

Chicago

Vora, Deepali R., and Kamatchi R. Iyer. "Deep Learning in Engineering Education: Implementing a Deep Learning Approach for the Performance Prediction in Educational Information Systems." In Deep Learning Applications and Intelligent Decision Making in Engineering, edited by Karthikrajan Senthilnathan, et al., 222-255. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2108-3.ch010

Export Reference

Mendeley
Favorite

Abstract

The goodness measure of any institute lies in minimising the dropouts and targeting good placements. So, predicting students' performance is very interesting and an important task for educational information systems. Machine learning and deep learning are the emerging areas that truly entice more research practices. This research focuses on applying the deep learning methods to educational data for classification and prediction. The educational data of students from engineering domain with cognitive and non-cognitive parameters is considered. The hybrid model with support vector machine (SVM) and deep belief network (DBN) is devised. The SVM predicts class labels from preprocessed data. These class labels and actual class labels act as input to the DBN to perform final classification. The hybrid model is further optimised using cuckoo search with levy flight. The results clearly show that the proposed model SVM-LCDBN gives better performance as compared to simple hybrid model and hybrid model with traditional cuckoo search.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.