# PREDICTING STUDENTS ACADEMIC PERFORMANCE USING ARTIFICIAL NEURAL NETWORK

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PREDICTING STUDENTS ACADEMIC PERFORMANCE USING ARTIFICIAL NEURAL NETWORK

TABLE OF CONTENT

Title Page………………..i

Certification………….…ii

Dedication………………iii

Acknowledgment……….iv

Table of content……...…v

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND TO THE STUDY

1.2 STATEMENT OF THE PROBLEM

1.3 OBJECTIVES OF THE STUDY

1.4 SIGNIFICANCE OF THE STUDY

1.6 SCOPE/LIMITATIONS OF THE STUDY

LIMITATION OF STUDY

REFERENCES

CHAPTER TWO

LITERATURE REVIEW

2.1 INTRODUCTION

2.2 THEORETICAL FRAMEWORK

2.3 THE CONCEPT OF ARTIFICIAL NEURAL NETWORKS AND

PERFORMANCE

2.5 LEARNING STRATEGIES AND ACADEMIC PERFORMANCE

2.6 ARTIFICIAL NEURAL NETWORK PROCESSING AND MEASURES TO

EVALUATE THE NEURAL NETWORK SYSTEM PERFORMANCE

2.7 EMPIRICAL REVIEW

CHAPTER THREE

RESEARCH METHODOLOGY

3.0 INTRODUCTION

3.1 RESEARCH DESIGN

3.2 AREA OF THE STUDY

3.3 POPULATION OF THE STUDY

3.4 SAMPLE OF THE STUDY

3.5 INSTRUMENT FOR DATA COLLECTION

3.7 TECHNIQUES FOR DATA ANALYSIS

3.8 ANALYSES PROCEDURE

3.9 ARCHITECTURE OF THE NEURAL NETWORKS

3.10 DISCRIMINANT ANALYSES

CHAPTER FOUR

DATA ANALYSIS AND INTERPRETATION

4.1 PRESENTATION OF DATA

4.2 Neural network analyses

4.3 Maximizing the ANN models

4 Predictive contribution by categories of variables

Initial analysis of individual continuous estimates of future

4.6 Discriminant analyses (DA)

CHAPTER FIVE

SUMMARY OF FINDINGS AND CONCLUSION

5.1 SUMMARY OF FINDINGS

5.2 CONCLUSION

REFERENCES

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND TO THE STUDY

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 input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.

The artificial neural network (ARTIFICIAL NEURAL NETWORK), a soft computing technique, has been successfully applied in different fields of science, such as pattern recognition, fault diagnosis, forecasting and prediction. However, as far as we are aware, not much research on predicting student academic performance takes advantage of artificial neural network. Kanakana and Olanrewaju (2001) utilized a multilayer perception neural network to predict student performance. They used the average point scores of grade 12 students as inputs and the first year college results as output. The research showed that an artificial neural network based model is able to predict student performance in the first semester with high accuracy. A multiple feed-forward neural network was proposed to predict the students’ final achievement and to classify them into two groups. In their work, a student achievement prediction method was applied to a 10-week course. The results showed that accurate prediction is possible at an early stage, and more specifically at the third week of the 10-week course.

1.2 STATEMENT OF THE PROBLEM

1.3 OBJECTIVES OF THE STUDY

The following are the objectives of this study:

1. To examine the use of Artificial Neural Network in predicting students academic performance.

2. To examine the mode of operation of Artificial Neural Network.

3. To identify other approaches of predicting students academic performance.

1.4 SIGNIFICANCE OF THE STUDY

This study will educate on the design and implementation of Artificial Neural Network. It will also educate on how Artificial Neural Network can be used in predicting students academic performance.

This research will also serve as a resource base to other scholars and researchers interested in carrying out further research in this field subsequently, if applied will go to an extent to provide new explanation to the topic

1.6 SCOPE/LIMITATIONS OF THE STUDY

This study will cover the mode of operation of Artificial Neural Network and how it can be used to predict student academic performance.

LIMITATION OF STUDY

Financial constraint- Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).

Time constraint- The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work.

REFERENCES

Ayan, M.N.R.; Garcia, M.T.C. 2013. Prediction of university students’ academic achievement by linear and logistic models. Span. J. Psychol. 11, 275–288.

Kanakana, G.M.; Olanrewaju, A.O. 2001. Predicting student performance in engineering education using an artificial neural network at Tshwane university of technology. In Proceedings of the International Conference on Industrial Engineering, Systems Engineering and Engineering Management for Sustainable Global Development, Stellenbosch, South Africa, 21–23 September 2011; pp. 1–7.

Romero, C.; Ventura, S. 2007, Educational Data mining: A survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146.

Ting, S.R. 2008, Predicting academic success of first-year engineering students from standardized test scores and psychosocial variables. Int. J. Eng. Educ., 17, 75–80.

CHAPTER TWO

LITERATURE REVIEW

2.1 INTRODUCTION

This chapter gives an insight into various studies conducted by outstanding researchers, as well as explained terminologies with regards to predicting students academic performance using artificial neural network. The chapter also gives a resume of the history and present status of the problem delineated by a concise review of previous studies into closely related problems.

2.2 THEORETICAL FRAMEWORK

Intelligence and the g-factor are the most frequently studied factors in relation to academic achievement and the prediction of performance (Miñano et al., 2012). There is a large body of research that shows a strong positive correlation between g and educational success (e.g., Kuncel, Hezlett, & Ones, 2001; Linn & Hastings, 1984). The g-factor is defined, in part, as an ability to acquire new knowledge (e.g., Cattell, 1971; Schmidt, 2002; Snyderman & Rothman, 1987). Although the g-factor is not the same construct as Working Memory (WM), several studies have demonstrated a high correlation between these measures (Heitz et al., 2006; Unsworth, Heitz, Schrock, & Engle, 2005). Following the early study of Daneman and Carpenter (1980) on individual differences in working memory capacity (WMC) and reading comprehension, further research has shown the importance of WMC as a domain-general construct (Conway, Cowan, Bunting, Therriault, & Minkoff, 2002; Conway & Engle, 1996; Engle & Kane, 2004; Feldman Barrett, Tugade, & Engle, 2004; Kane et al., 2004), including the prediction of average scores over several academic areas (Colom et al., 2007).

Similarly, a large body of literature shows WMC as a very important construct in several areas and several studies have shown its importance in a wide range of complex cognitive behaviours such as comprehension (e.g., Daneman & Carpenter, 1980), reasoning (e.g., Kyllonen & Christal, 1990), problem solving (Welsh, Satterlee- Cartmell, & Stine, 1999) and complex learning (Kyllonen & Stephens, 1990; Kyndt, Cascallar, & Dochy, 2012; St Clair-Thompson & Gathercole, 2006). WMC is an important predictive variable of intellectual ability and academic performance, consistent over time (e.g. Engle, 2002; Musso & Cascallar, 2009a; Passolunghi & Pazzaglia, 2004; Pickering, 2006). Working memory is a paradigmatic form of cognitive control that explains how this cognitive control occurs, and which involves the active maintenance and executive processing of information available to the cognitive system, combining the ability to both maintain and effectively process information with minimal loss (Jarrold & Towse, 2006). It is crucial for the processing of information within the cognitive system, it has a limited capacity and it differs between individuals (Conway et al., 2005). The literature seems to indicate two fundamental approaches according to the interpretation of working memory and executive control. Traditional perspectives represent working memory and executive control as separate modules (e.g., Baddeley, 1986). The perspective taken in this research coincides with another view that understands working memory and executive control as constituting two sides of the same phenomenon, an emergent property from the neuro-cognitive architecture (Anderson, 1983, 1993, 2002, 2007; Anderson et al., 2004; Hazy; Frank & O‟Reilly, 2006).

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