PREDICTING STUDENT PERFORMANCE USING NEURAL NETWORK

PREDICTING STUDENT PERFORMANCE USING NEURAL NETWORK
 
CHAPTER ONE
INTRODUCTION
1.0   Background to the Study
Predicting student academic performance has long been an important research topic. Among the issues of education system, questions concerning admissions into academic institutions (secondary and tertiary level) remain important (Ting, 2008). The main objective of the admission system is to determine the candidates who would likely perform well after being accepted into the school. The quality of admitted students has a great influence on the level of academic performance, research and training within the institution. The failure to perform an accurate admission decision may result in an unsuitable student being admitted to the program. Hence, admission officers want to know more about the academic potential of each student. Accurate predictions help admission officers to distinguish between suitable and unsuitable candidates for an academic program, and identify candidates who would likely do well in the school (Ayan and Garcia, 2013). The results obtained from the prediction of academic performance may be used for classifying students, which enables educational managers to offer them additional support, such as customized assistance and tutoring resources.
The results of this prediction can also be used by instructors to specify the most suitable teaching actions for each group of students, and provide them with further assistance tailored to their needs. In addition, the prediction results may help students develop a good understanding of how well or how poorly they would perform, and then develop a suitable learning strategy. Accurate prediction of student achievement is one way to enhance the quality of education and provide better educational services (Romero and Ventura, 2007). Different approaches have been applied to predicting student academic performance, including traditional mathematical models and modern data mining techniques. In these approaches, a set of mathematical formulas was used to describe the quantitative relationships between outputs and inputs (i.e., predictor variables). The prediction is accurate if the error between the predicted and actual values is within a small range.
In machine learning and cognitive science, artificial neural networks (ARTIFICIAL NEURAL NETWORKs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected "neurons" which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an