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 selfregulated 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' selfregulation, 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.

This paper presents the changes performed in a university course to adopt European Higher Education Area principles taking advantage of new technologies and educational approaches. Particularly, a Flipped Classroom model that also involves an Intensive Continuous Assessment approach is adopted, moving the presentation of theoretical contents to videos that can be watched outside of the classroom and using the classroom face-to-face time to provide explanations, problem solving and to perform assessment activities every week. A main part of innovation in the experience comes from the use of an online tool (BeA -Blended e-Assessment) that facilitates the assessment and reviewing of paper-based exams. This tool supports teachers in assessment tasks, that can be performed in a faster, simpler, more transparent and less error-prone way. The paper shows the results of an experience involving a control group and an experimentation group, in which this new approach and tool have been applied. The results obtained demonstrate the effectiveness of both proposals. In conjunction, the paper describes how a traditional university course based on lectures can be successfully adapted to a more innovative approach based on the principles of active learning and accountability thanks to the use of our blended e-Assessment tool.

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