Higher Education engineering students need to be prepared to address sustainable solutions to the complex problems faced in this century. They should become proficient problem solvers, able to work in multidisciplinary teams, ready to adapt to new technologies, and able to acquire new knowledge and skills when needed. Usually known as soft skills, these competences play a key role in Engineering and have being taught in the last two decades, to a greater or lesser extent, using different methodologies and tools. This study reviews the promotion and teaching of soft skills in Higher Education across 5 European countries: Greece, Estonia, Denmark, Portugal and Spain. It provides an overview of best practices on these countries, focusing also on technological solutions to actually enable the development of soft skills. The purpose of this research is to shed some light about how soft skills are being taught presently and the difficulties involved in that process.

Smart gloves have been under development during the last 40 years to support human-computer interaction based on hand and finger movement. Despite the many devoted efforts and the multiple advances in related areas, these devices have not become mainstream yet. Nevertheless, during recent years, new devices with improved features have appeared, being used for research purposes too. This paper provides a review of current commercial smart gloves focusing on three main capabilities: (i) hand and finger pose estimation and motion tracking, (ii) kinesthetic feedback, and (iii) tactile feedback. For the first capability, a detailed reference model of the hand and finger basic movements (known as degrees of freedom) is proposed. Based on the PRISMA guidelines for systematic reviews for the period 2015–2021, 24 commercial smart gloves have been identified, while many others have been discarded because they did not meet the inclusion criteria: currently active commercial and fully portable smart gloves providing some of the three main capabilities for the whole hand. The paper reviews the technologies involved, main applications and it discusses about the current state of development. Reference models to support end users and researchers comparing and selecting the most appropriate devices are identified as a key need.

The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task.

The ability to regulate one’s own learning processes is a key factor in educational scenarios. Self-regulation skills notably affect students’ efficacy when studying and academic performance, for better or worse. However, neither students or instructors generally have proper understanding of what self-regulated learning is, the impact that it has or how to assess it. This paper has the purpose of showing how learning analytics can be used in order to generate simple metrics related to several areas of students’ self-regulation, in the context of a first-year university course. These metrics are based on data obtained from a learning management system, complemented by more specific assessment-related data and direct answers to self-regulated learning questionnaires. As the end result, simple self-regulation profiles are obtained for each student, which can be used to identify strengths and weaknesses and, potentially, help struggling students to improve their learning habits.

Self-Regulated Learning addresses key students’ competences for performance in higher education, particularly in engineering disciplines. Assessing these competences helps identify students’ gaps and provide appropriate support towards their development. Currently, the main self-regulated learning assessment tools are questionnaires with a large number of items administered in one session, for example, the MSLQ has 81 questionnaire items. Rather than snapshotting the students’ self-regulated learning competences in a single session, this manuscript proposes a novel assessment approach, the SMSRLQ, which includes 150 questionnaire items (81 from the MSLQ and 69 from other questionnaires) but spacing the items weekly and providing feedback monthly. The spaced approach was experienced on several Egyptian universities using a social network service (Facebook) and distributing students into control (113) and experimental groups (107). Many variations in the students’ answers are observed, with statistically significant differences between 11% to 30% in MAE and MAPE indicators. Variations in standard deviation, reliability and clustering between the approaches also were identified, demonstrating that there are clear differences between the two administration approaches. Regarding monthly feedback, the results show that it enhances students’ participation and improves their self-regulated learning competences: 77% of the experimental group respondents found that their self-learning knowledge was developed after the study, compared to 50% of the control group respondents.