DESIGN AND IMPLEMENTATION OF A SYSTEM FOR PREDICTING STUDENT PERFORMANCE USING ARTIFICIAL NEURAL NETWORK
Artificial intelligence has enabled the development of more sophisticated and more efficient student models which represent and detect a broader range of student behavior than was previously possible.
In this research, the implementation of a user-friendly software tool for predicting the students’ performance in the course which is based on a neural network classifier will be made. This tool has a simple interface and can be used by an educator for classifying students and distinguishing students with low achievements or weak students who are likely to have low achievements.
The observed poor quality of graduates of students of this institution in recent times has been partly traced to inadequacies of some or most of the lecturer in the University which goes down to ability to handle the students… (Scroll down for the link to get the Complete Project Material)
During the last few years, the application of artificial intelligence in education has grown exponentially, spurred by the fact that it allows us to discover new, interesting, and useful knowledge about students. Educational data mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from an educational context.
While traditional database queries can only answer questions such as ”find the students who failed the examinations”, data mining can provide answers to more abstract questions like ”find the students who will possibly succeed the examinations”. One of the key areas of the application of EDM is the development of student models that would predict student characteristics or performances in their educational institutions.
Hence, researchers have begun to investigate various data mining methods to help educators to evaluate and improve the structure of their course context.
The main objective of the admission system is to determine candidates who would likely do well in the university or can perform well within the academic year or to produce students of high grade and intelligence.
The quality of candidates admitted into any higher institution affects the level of research and training within the institution, and by extension, has an overall effect on the development of the country itself, as these candidates eventually become key players in the affairs of the country in all sectors of the economy… (Scroll down for the link to get the Complete Project Material)
Background of the Research
In machine learning and cognitive science, artificial neural networks (ANNs) 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… (Scroll down for the link to get the Complete Project Material)
Statement of Research Problem
Looking into the institution this day, you will discover that 48% of the student are actually performing very low on their academic level, whom if asked to defend his admission status cannot (i.e. sitting for the attitude test), when the proper investigation is carried out, findings show that most of them have their way into the school through bribe or the so-called upper hand.
Also, another issue or problem for this research work is that some of the applied candidates, some are actually sound and capable of performing well when admitted, but because of some factors at the moment or surrounding the student, prevent the student from obtaining or securing his admission into the school. With this little problem, I seek to develop a neural network system an artificial one that will solve this problem.
Coupled with the stress gone through during the admission and delay in the process that ends up not being done perfect to the standard required… (Scroll down for the link to get the Complete Project Material)
The primary aim of my research work is to develop an artificial neural network system that will be capable of predicting student performance.
Some other objectives which I will be covering in this research work are as follows:
- A system that will enhance the admission process of this institution in terms of admitting the right student into the institution… (Scroll down for the link to get the Complete Project Material)
Factors Affecting Student Performance
In the literature, students’ academic performance can be affected by various factors. This section will review the most common factors that have an impact on students’ academic performance in its different forms.
It has been proven by studies highlighted in the literature reviewed in this thesis, that students’ attendance affects their overall academic performance. The earliest studies that tackled this problem were Breiman, et al., (1984), Garey (1981) and David (1993), who adopted student attendance rates as criterion and used Decision Trees (DT) and Naïve Bayes (NB) data mining techniques to conclude that lower attendance rates negatively affect students’ GPA.
The study by Romer (1993) is considered as one of the major publications that explored the relationship between student attendance and student performance in exams… (Scroll down for the link to get the Complete Project Material)
The Artificial Neural Networks in Predicting Student Performance
ANN consists of a set of highly interconnected entities, called Processing Elements (PE) or unit. The structure and functions of ANN are inspired by the biological central nervous system (brain). Each unit is designed to mimic its biological counterpart, the neuron or neural node. Each accepts a weighted set of inputs and responds with an output.
ANNs address problems that is often difficult for traditional computers to solve, such as speech and pattern recognition, weather forecasts, sales forecasts, scheduling of buses, power loading forecasts, electricity consumption prediction, student enrollment projection, clustering, and early cancer detection.
ANNs develop solution fast and have less reliance on domain experience. ANNs develop a solution to any problem under the consideration that one may not get thorough understanding of the subject matter (Usman and Alaba, 2014; Usman and Adenubi, 2013; Oladokun et al., 2008)… (Scroll down for the link to get the Complete Project Material)
MACHINE LEARNING METHODOLOGIES
In this section, we explore two popular classification algorithms: decision tree classifiers and neural networks. Classification technique has been one of the common applied algorithms for data mining. It employs big data with pre-classified examples and then conducts a model that can predict and classify the large amount of the new data records .
This approach frequently employs several popular algorithms, such as Naive Bayes, k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. The data classification process involves both learning and classification phases. First, the algorithm analyzes the training data in the learning phase, then for the second phase, it applies the test data to measure the performance of the classification model generated from the first phase.
The classification model from this classifier training algorithm includes a set of parameters that best fit the pre-classified training examples… (Scroll down for the link to get the Complete Project Material)
Inspired by the biological neural networks that constitute our human brains, Artificial Neural Networks (ANN) systems mimic the logic behind it by establishing an array of interconnected nodes that exchange information among each other as illustrated in Figure 5.2 .
The input, hidden, and output layers derived from the structure of our neurons, which get signals from dendrites and output the signals with axons, in order to exchange information. Similar to how children can pick up skills from observing their parents; the neural networks can learn to perform varieties of tasks by considering and observing new examples, generally without being hard coded programs and rule sets .
Unlike Decision Tree Classifier, Neural Network can optimize its model iteratively and summarize the data in a parallel way just like how we human can perform thinking, looking, and moving body parts at the same time… (Scroll down for the link to get the Complete Project Material)
Figure 5.2: Standard Neural Networks Structure
With this sophisticated structure, ANN is well known for solving complex application in numerous business applications, such as predicting stock trends, translating human speech, and playing chess again real human beings… (Scroll down for the link to get the Complete Project Material)
RESEARCH RESULTS AND DISCUSSION
The testing phase in this experiment utilizes the confusion matrix in order to compute the accuracies of the models. As shown in Table 6.1, the confusion matrix is a simple n×n dimension table describing the performance of a classification model by comparing the predicted results with the actual results. In our case, n = 2 since our predicting outputs have two classes. With the confusion matrix, we can obtain the accuracy by adding the true positive and true negative values in the table and divide by the total data points as shown in equation 6.1.
Table 6.1: Confusion Matrix
The confusion matrix result for the decision tree is projected on the left side of Table 6.2. With the equation 6.1, we can compute the accuracy of the model to be 92.8% as shown in Table 6.3. We can also improve model accuracy by pruning the decision tree. The benefit of pruning a decision tree is that it removes the tree branches that contribute little to the overall classification process and reduces the tree size . Besides, it simplifies the complexity of the final model classifier, reduces the likelihood of model overfitting, and improves the predictive accuracy .
Table 6.3: Accuracies of Decision Tree Classifier Model
There are several principal methods for pruning decision trees, such as Error-Complexity Pruning, Critical Value Pruning, and Reduced-Error Pruning . The method used in this experiment is Cost-Complexity Pruning, which is similar to Error-Complexity Pruning… (Scroll down for the link to get the Complete Project Material)
This paper surveyed some of the most relevant work in this area, delivered graphical visualization for some input attributes and developed decision tree and neural network algorithms with our synthetic dataset respecting to EDM. In this experiment, both decision tree classifiers and neural networks perform well interpreting the relationships between the variables and predicting student academic performance. We conclude that it is important to choose a classification model that is suitable for various types and complexity of the dataset.
For future experiments, it is expected to increase the complexity and more useful students’ data. In this way, we could take full advantage of neural network classifier’s ability and measure how different those data attributes can affect the model’s performance… (Scroll down for the link to get the Complete Project Material)
Qasem A Al-Radaideh, Emad M Al-Shawakfa, and Mustafa I Al-Najjar (2006). Mining student data using decision trees. In International Arab Conference on Information Technology (ACIT’2006), Yarmouk University, Jordan. (Artificial Neural Network)
Sylvain Arlot and Alain Celisse (2010). A survey of cross-validation procedures for model selection. Statist. Surv., 4:40–79. (Artificial Neural Network)
Artificial neural network (2018). Wikipedia, the free encyclopedia. Retrieved from https://en.wikipedia.org/wiki/Artificial_neural_ network [Online; accessed 23-October-2018]. (Artificial Neural Network)
Brijesh Kumar Bhardwaj and Saurabh Pal (2012). Data mining: A prediction for performance improvement using classification. CoRR, abs/1201.3418. (Artificial Neural Network)
Markus Brameier and Wolfgang Banzhaf (2001). A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation, 5(1):17–26. (Artificial Neural Network)
R Brause, T Langsdorf, and Michael Hepp (2004). Neural data mining for credit card fraud detection. In Tools with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on, pages 103–106. IEEE, 1999. (Artificial Neural Network)
Matthew M. Chingos (2006). What matters most for college completion? academic preparation is a key of success – third way. Retrieved from https://www.thirdway.org/report/what-mattersmost-for-college-completion-academic-preparation-is-a-key-of-success. (Artificial Neural Network)(Artificial Neural Network)
Paulo Cortez (2010). Data mining with neural networks and support vector machines using the r/rminer tool. In Industrial Conference on Data Mining, pages 572–583. (Artificial Neural Network)(Artificial Neural Network)
Ryan Shaun, Joazeiro de Baker, and Adriana M. J. (2008). Labeling student behavior faster and more precisely with text replays. In Educational Data Mining 2008, The 1st International Conference on Educational Data Mining, Montreal, Quebec, Canada, June 20-21,´ 2008. Proceedings, pages 38–47, 2008. (Artificial Neural Network)
Hepu Deng, Duoqian Miao, Fu Lee Wang, and Jingsheng Lei (2009). Emerging Research in Artificial Intelligence and ComputationaI Intelligence: International Conference, AICI 2011, Taiyuan, China, September 23-25, 2011. Proceedings, volume 237. Springer, 2011. (Artificial Neural Network)
Dutt, M. A. Ismail, and T. Herawan. A systematic review on educational data mining. IEEE Access, 5:15991–16005, 2017. (Artificial Neural Network)
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