Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.
The problem of predicting students’ performance has been recently tackled by using matrix factorization, a popular method applied for collaborative filtering based recommender systems. This problem consists of predicting the unknown performance or score of a particular student for a task s/he did not complete or did not attend, according to the scores of the tasks s/he did complete and the scores of the colleagues who completed the task in question. The solving method considers matrix factorization and a gradient descent algorithm in order to build a prediction model that minimizes the error in the prediction of test data. However, we identified two key aspects that influence the accuracy of the prediction. On the one hand, the model involves a pair of important parameters: the learning rate and the regularization factor, for which there are no fixed values for any experimental case. On the other hand, the datasets are extracted from virtual classrooms on online campuses and have a number of implicit latent factors. The right figures are difficult to ascertain, as they depend on the nature of the dataset: subject, size, type of learning, academic environment, etc. This paper proposes some approaches to improve the prediction accuracy by optimizing the values of the latent factors, learning rate, and regularization factor. To this end, we apply optimization algorithms that cover a wide search space. The experimental results obtained from real-world datasets improved the prediction accuracy in the context of a thorough search for predefined values. Obtaining optimized values of these parameters allows us to apply them to further predictions for similar datasets.
We present a methodology based on matrix factorization and gradient descent to predict the number of sessions established in the access points of a Wi-Fi network according to the users' behavior. As the network considered in this work is monitored and controlled by software in order to manage users and resources in real time, we may consider it as a cyber-physical system that interacts with the physical world through access points, whose demands can be predicted according to users' activity. These predictions are useful for relocating or reinforcing some access points according to the changing physical environment. In this work we propose a prediction model based on machine learning techniques, which is validated by comparing the prediction results with real user's activity. Our experiments collected the activity of 1,095 users demanding 26,673 network sessions during one month in a Wi-Fi network composed of 10 access points, and the results are qualitatively valid with regard to the previous knowledge. We can conclude that our proposal is suitable for predicting the demand of sessions in access points when some devices are removed taking into account the usual activity of the network users.
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