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.
TopIntroduction
Educational Information System lies at the heart of any educational institute to monitor the educational goals. One important goal of the educational system among many is tracking the performance of the student. Many techniques and algorithms are used to track the progress of students. This domain has gained importance with the increase in data volume and the development of new algorithms. (Vora & Iyer, 2018)
Data generated from various educational sources is explored using different methods and techniques in EDM. The multidisciplinary research that deals with the development of such methods and techniques are the focus of EDM. Analysis of educational data could provide information about student's behaviours, based on which education policies could be enhanced further (Sukhija, Jindal, & Aggarwal, 2015, October). EDM discusses the techniques, tools, and research intended for automatically extracting the meaning from large repositories of educational systems' data.
According to Davies (Davis, 1998), “Education has become a commodity in which people seek to invest for their own personal gain, to ensure equality of opportunity and as a route to a better life.” Because of this Higher education providers are competing mainly for students, funding, research and recognition within the wider society. It seems important to study data of students studying professional courses as for the growth of any nation producing better professionals is the key to success. Higher education system faces two main challenges: finding placements and students dropping out. Analysis of educational data can help in answering the two major challenges satisfactorily. Predicting the performance leads to better placements and minimise the dropouts.
A statistical technique to predict future behaviour is known as Predictive modelling. Predictive analytics is used widely in the area of product management and recommendation. It is a powerful tool to understand the data at hand and get useful insights from it. Figure 1 represents Predictive analytics in education.
One of the most popular methods for predictive analytics is Machine learning to predict future behaviour. From the plethora of algorithms available, it is always interesting to discover which algorithm or technique is most suitable for analysis of data under consideration. Educational Data Mining is the area of research where predictive modelling is most useful. Predictive analytics in Education can help in many ways such as; to identify weaker and dropout students, to identify best learning practices, to predict students' performance at every stage, tracking the placements of education etc. There is a need for the evolution of more and more new techniques to create an accurate classification of data and prediction based on that.
Machine Learning (ML) has become very popular among researchers because of the astonishing results the algorithms are giving for diverse data and applications. But when data is growing enormously simple ML are not efficient and beneficial. Meantime there are lot many advances in hardware and software. So it was possible to have more complex and hybrid architectural models performing various DM or Big Data tasks. Big data is already posing a challenge on traditional ML models for efficiency and accuracy. Various hybrid models are proposed and tested in many domains to tackle these challenges and are proved to be useful. Thus applying a hybrid model in the education domain will be useful.
ML is changing in a better way to tackle new age data and one of such advances is Deep Learning. Nonlinear data analysis can be effectively done using deep learning. Characteristics of the data can be effectively analysed using layers in the deep learning model. Deep learning is being applied in many domains; predominantly in image processing and natural language processing (Deng & Yu, 2014). Thus it is interesting to apply Deep Learning in the field of education.
This chapter addresses the main objectives as:
- 1.
Identification of recent state of EDM and EDM techniques
- 2.
Identification of areas where Deep Learning is applied and is useful
- 3.
Applying Machine learning techniques on Educational Data for classification
- 4.
Applying hybrid classification method using Deep Learning on Educational Data for Classification