Catarina Félix Oliveira

Abstract

Abstract
Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics' Web of Science and Elsevier's Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

Abstract

Abstract
The acquisition of competences for the development of computer programs is one of the main challenges faced by computer science students. As a result of not being able to develop the abilities needed (for example, abstraction), students drop out the subjects and sometimes even the course. There is a need to study the causes of student success (or failure) in introductory curricular units to check for behaviours or characteristics that may be determinant and thus try to prevent and change said causes. The students of one programming curricular unit were invited to answer four surveys. We use machine learning techniques to try to predict the students' grades based on the answers obtained on the surveys. The results obtained enable us to plan the semester accordingly, by anticipating how many students might need extra support. We hope to increase the students' motivation and, with this, increase their interest on the subject. This way we aim to accomplish our ultimate goal: reducing the drop out and increasing the overall average student performance.

Abstract
Neural networks have been applied as a machine learning tool in many different areas. Recently, they have gained increased attention with what is now called deep learning. Neural networks algorithms have several parameters that need to be tuned in order to maximize performance. The definition of these parameters can be a difficult, extensive and time consuming task, even for expert users. One approach that has been successfully used for algorithm and parameter selection is metalearning. Metalearning consists in using machine learning algorithm on (meta)data from machine learning experiments to map the characteristics of the data with the performance of the algorithms. In this paper we study how a metalearning approach can be used to obtain a good set of parameters to learn a neural network for a given new dataset. Our results indicate that with metalearning we can successfully learn classifiers from past learning tasks that are able to define appropriate parameters. © 2018, Springer International Publishing AG.