Predicting students' performance using artificial neural networks
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 work, we describe the implementation of a user-friendly software tool for predicting the students' performance in the course of “Mathematics” which is based on a neural network classifier. 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.
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 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 (see Romero & Ventura 2007; Romero et al. 2008 and the references therein).
The academic achievement of higher secondary school education (Lyceum) in Greece is a deciding factor in the life of any student. In fact, Lyceum acts like a bridge between school education and higher learning specializations that are offered by universities and higher technological educational institutes. Limiting the students that fail in the final examinations is considered essential and therefore the ability to predict weak students could be useful in a great number of different ways. More specifically, the ability of predicting the students' performance with high accuracy in the middle of the academic period is very significant for an educator for identifying slow learners and distinguishing students with low achievements or weak students who are likely to have low achievements. By recognizing the students' weaknesses the educators are able to inform the students during their study and offer them additional support such as additional learning activities, resources and learning tasks and therefore increase the quality of education received by their students.
However, the idea of developing an accurate prediction model based on a classifier for automatically identifying weak students is a very attractive and challenging task. Generally, datasets from this domain skewed class distribution in which most cases are usually located to the one class. Hence, a classifier induced from an imbalanced dataset has typically a low error rate at the majority class and an unacceptable error rate for the minority classes.
In this work, we propose the application of an artificial neural network for predicting
student's performance at the final examinations in the course of “Mathematics”. Our aim is to identify the best training algorithm for constructing an accurate prediction model. We have also evaluated the classification accuracy of our neural network approach by comparing it with other well-known classifiers such as decision trees, Bayesian networks, classification rules and support vector machines. Moreover, we have incorporated our neural network classifier in a user-friendly software tool for the prediction of student's performance in order to making this task easier for educators to identify weak students with learning problems in time.
The remainder of this paper is organized as follows. In Section 2, we briefly describe the feedforward neural networks and in Section 3, we present the dataset of our study. Section 4 reports the experimental results while Section 5 presents our software tool and its main features. Finally, Section 6 presents our concluding remarks and our proposals for future research.