Arturo Durán Domínguez

• University of Extremadura

Peer assessment has traditionally represented a key tool to enhance active learning and critical thinking. However, the success of this approach is governed by different factors, which have been accentuated in recent years. The implementation of peer assessment is consequently a challenging task in the current context. This work investigates peer a...

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 sco...

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 ma...

Matrix factorization is used by recommender systems in collaborative filtering for building prediction models based on a couple of matrices. These models are usually generated by stochastic gradient descent algorithm, which learns the model minimizing the error done. Finally, the obtained models are validated according to an error criterion by pred...

The prediction of the students’ performance allows to improve the learning process using the online campus tools. In this context, recommender systems are useful for prediction purposes. This collaborative filtering tool, predicts the unknown performances analyzing the database that contains the performance of the students for particular tasks, con...

Nowadays, on-line campus are very important in the learning process of students, since they can access to teacher's resources easily. Moreover, on-line campus provide useful tools for building evaluation processes by teachers. Under this point of view, knowing the strengths and weakness of a student before his evaluation allows to plan better his l...

Gradient Descent is an algorithm very used by Machine Learning methods, as Recommender Systems in Collaborative Filtering. It tries to find the optimal values of some parameters in order to minimize a particular cost function. In our research case, we consider Matrix Factorization as application of Gradient Descent, where the optimal values of two...

Access points play an important role in Wi-Fi networks and can provide us with useful information about the energy consumption according to the users’ behavior. If we predict the energy consumption in a determined access point, we can make easier the maintenance plans for the network infrastructure making the most adequate decisions about the place...

Some maintenance tasks in Wi-Fi networks may involve removing an access point due to several reasons. As a result, the new infrastructure registers a different number of roamings in the access points according to the users’ behaviour, with a certain energy impact added to the consumption caused by the own operations of the devices. This energy effe...

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...

In this work, we present a methodology based on matrix factorization and gradient descent algorithm to predict the access points workload based on the users' behavior in Wi-Fi wireless networks, under academic and university environments. This knowledge is very useful when it is necessary to relocate determined access points due to the changing phy...