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.

This paper presents a new module for the BeA (Blended e-Assessment) platform, aimed to be used in a higher education subject that combines the flipped classroom system and gamification features. Currently, this subject implements the flipped classroom methodology as well as intensive continuous assessment. Under this system, students are asked to watch videos that explain course contents, doing so as homework on a weekly basis. Then, students undertake an exam in the classroom covering the corresponding week’s contents, counting towards the final grade of the subject. In a new approach, we decided to have students take exams every two weeks instead. However, this creates the need of maintaining a high level of student engagement, in order for them to keep watching the videos every single week. That is why we are developing a tool that allows us to apply gamification on days when students are not assessed. This tool consists of an online question system (as a multi-choice test) accessible through a web browser, which students can answer while being involved in a game.

The participation of students in the form of questions to be answered in the classroom is a key activity of the flipped classroom methodology. Those questions arise when the students watch educational videos out of the classroom (another key part of the aforementioned methodology). However, low student participation is a common problem within a flipped classroom setting. As a solution to this problem, and in order to encourage the active participation of students making questions, we are developing a system with which students can anonymously ask questions about the content of the videos, which will be later answered in class by the teacher.

This Research to Practice Work in Progress presents an analytics add-on for QMSFC (Question Management System for Flipped Classroom), an online tool with the purpose of increasing student engagement and participation in a flipped classroom setting. This is done by providing an interface that students can use to post questions about educational videos, which would then be answered by the teacher in the classroom. The data generated by QMSFC could be collected and used to expand upon the functionalities of the platform, such as alerting the teacher about particularly tough topics for students. Additionally, these data could be analyzed together with student grades in order to find out the correlation between the volume of activity on the platform and exam performance. Work remaining on this project involves using the fully developed tool in a real learning situation and analyzing the results and feedback obtained.