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Deep Learning in Engineering Education: Performance Prediction Using Cuckoo-Based Hybrid Classification

Deep Learning in Engineering Education: Performance Prediction Using Cuckoo-Based Hybrid Classification

Deepali R. Vora, Kamatchi R. Iyer
Copyright: © 2020 |Pages: 32
ISBN13: 9781799830955|ISBN10: 1799830950|EISBN13: 9781799830979
DOI: 10.4018/978-1-7998-3095-5.ch009
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MLA

Vora, Deepali R., and Kamatchi R. Iyer. "Deep Learning in Engineering Education: Performance Prediction Using Cuckoo-Based Hybrid Classification." Machine Learning and Deep Learning in Real-Time Applications, edited by Mehul Mahrishi, et al., IGI Global, 2020, pp. 187-218. https://doi.org/10.4018/978-1-7998-3095-5.ch009

APA

Vora, D. R. & Iyer, K. R. (2020). Deep Learning in Engineering Education: Performance Prediction Using Cuckoo-Based Hybrid Classification. In M. Mahrishi, K. Hiran, G. Meena, & P. Sharma (Eds.), Machine Learning and Deep Learning in Real-Time Applications (pp. 187-218). IGI Global. https://doi.org/10.4018/978-1-7998-3095-5.ch009

Chicago

Vora, Deepali R., and Kamatchi R. Iyer. "Deep Learning in Engineering Education: Performance Prediction Using Cuckoo-Based Hybrid Classification." In Machine Learning and Deep Learning in Real-Time Applications, edited by Mehul Mahrishi, et al., 187-218. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-3095-5.ch009

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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 acts 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.

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