Cancer is the second leading cause of death worldwide, and within this type of disease, lung cancer is the second most diagnosed, but the leading cause of death. Early detection is crucial to increase patient survival rates. One of the primary methods for detecting this disease is through medical imaging, which, due to its features, is well-suited for analysis by deep learning techniques. These techniques have demonstrated exceptional results in similar tasks. Therefore, this paper focusses on analyzing the latest work related to lung cancer detection using deep learning, providing a clear overview of the state of the art and the most common research directions pursued by researchers. We have reviewed DL techniques for lung cancer detection between 2018 and 2023, analyzing the different datasets that have been used in this domain and providing an analysis between the different investigations. In this state-of-the-art review, we describe the main datasets used in this field and the primary deep learning techniques used to detect radiological signs, predominantly convolutional neural networks (CNNs). As the impact of these systems in medicine can pose risks to patients, we also examine the extent to which explainable AI techniques have been applied to enhance the understanding of these systems, a crucial aspect for their real-world application. Finally, we will discuss the trends that the domain is expected to follow in the coming years and the challenges that researchers will need to address.

Big Data courses in which students are asked to carry out Big Data projects are becoming more frequent as a part of University Engineering curriculum. In these courses, instructors and students must face a series of special characteristics, difficulties and challenges that it is important to know about beforehand, so the lecturer can better plan the subject and manage the teaching methods in order to prevent students’ academic dropout and low performance. The goal of this research is to approach this problem by sharing the lessons learned in the process of teaching e-learning courses where students are required to develop a Big Data project as a part of a final degree/master course. In order to do so, a survey was carried out among a group of students enrolled in those kinds of courses during the last years. The quantitative and qualitative analysis of the obtained data led us to present a series of lessons learned that may help other participants (both students and lecturers) to better study, design and teach similar courses. In addition, the results shed light on possible existing open problems in the area of Big Data project development. Both the methodology used and the survey designed in this research were validated by a group of experts in the area using a formal statistical approach, which support the validity of the lessons learned.

This paper proposes the novel application of group counterfactual explanations to the problem of predicting students at risk of dropout. Our objective is to explain how to recover the largest possible number of students while minimizing effort and cost. Using group counterfactuals, instructors and institutions could recover large groups of students with minimal remedial actions. For testing, we used the well-known public educational Open University Learning Analytics Dataset (OULAD), which contains students’ clicks made throughout interactions with online courses. We modified and adapted the only existing algorithm for the generation of group counterfactuals, named GROUP-CF. We also used the Diverse Counterfactual Explanations (DiCE) individual counterfactual algorithm with the K-means clustering method and new options in discovering the most representative counterfactuals for a group of students. The results obtained are very promising; our approach can be successfully applied to recover 99.3% of students at risk of failing in a shorter time in comparison to traditional individual counterfactuals. Moreover, although a group counterfactual proposes to change a greater number of students’ features, the values are lighter and therefore seem easier to apply than the ones obtained with individual counterfactuals. This work opens up a new line of research in education.

Introducing ethical, social and environmental issues in ICT engineering degrees

This paper proposes the novel application of group counterfactual explanations to the problem of predicting student at risk of drop-out. Our objective is to provide explanations for trying to re-cover the largest number of students with less effort and cost. Using group counterfactuals, in-structors and institutions could recover large groups of students with minimal remedial actions. For testing it, we have used the well-known public educational OULAD dataset that contains student’s clicks made throughout interaction with online courses. We have modified and adapted the only existed algorithm for generating group counterfactual named GROUP-CF. We also used the DICE individual counterfactual algorithm with the K-means clustering method and new op-tions for discovering the most representative counterfactuals for a group of students. The results obtained are very promising and they show that our approach can be successfully applied to re-cover 99.3% of students at risk of failing in a shorter time in comparison to traditional individual counterfactuals. And although, a group counterfactual proposes to change a greater number of student’s features, the values are lighter and therefore seem easier to apply than the ones obtained with individual counterfactuals. This work opens a new line of research in education.