Profiling students’ self-regulation with learning analytics: a proof of concept

The ability to regulate one's own learning processes is a key factor in educational scenarios. Self-regulation skills notably affect students' ef cacy when studying and academic performance, for better orworse. 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' selfregulation, in the context of a rst-year university course. These metrics are based on data obtained from a learning management system, complemented by more speci c assessment-related data and direct answers to self-regulated learning questionnaires. As the end result, simple self-regulation pro les are obtained for each student, which can be used to identify strengths and weaknesses and, potentially, help struggling students to improve their learning habits.Xunta de Galicia | Ref. ED431B 2020/3

Monitoring students’ self-regulation as a basis for an early warning system

Among the elements that determine a student’s academic success, their ability to regulate their own learning processes is an important, yet typically underrated factor. It is possible for students to improve their self-regulated learning skills, even at university levels. However, they are often unaware of their own behavior. Moreover, instructors are usually not prepared to assess students’ self-regulation. This paper presents a learning analytics solution which focuses on rating selfregulation skills, separated in several different categories, using activity and performance data from a LMS, as well as self-reported student data via questionnaires. It is implemented as an early warning system, offering the possibility of detecting students whose poor SRL profile puts them at risk of academic underperformance. As of the date of this writing, this is still a work in progress, and is being tested in the context of a first year college engineering course

Predictors and early warning systems in higher education: a systematic literature review

The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. Nowadays, these algorithms are widely used by entrepreneurs and researchers alike, having practical applications in a broad variety of contexts, such as in finance, marketing or healthcare. One of such contexts is the educational field, where the development and implementation of learning technologies led to the birth and popularization of computerbased and blended learning. Consequently, student-related data has become easier to collect. This Research Full Paper presents a literature review on predictive algorithms applied to higher education contexts, with special attention to early warning systems (EWS): tools that are typically used to analyze future risks such as a student failing or dropping a course, and that are able to send alerts to instructors or students themselves before these events can happen. Results of using predictors and EWS in real academic scenarios are also highlighted

Supporting intensive continuous assessment with BeA in a flipped classroom experience

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.Xunta de Galicia | Ref. ED431B 2017/67Xunta de Galicia | Ref. ED431D 2017/12Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-80515-

Exploring the synergies between gamification and data collection in higher education

In recent years, gamification techniques have been gaining popularity in all kind of educational scenarios, helping students improve their learning process by fostering engagement and attention. Implementing gamification aspects in a course can also provide an opportunity to gather student data that would not have been available otherwise. This paper describes a data gathering process in the context of a university course, as a work-in-progress. Among these data there is information regarding the participation of students in quizzes presented as games in the classroom. These quizzes combined questions covering course con-tents, as well as some regarding self-regulated learning habits. The main advantage observed was a high student participation in the quizzes. As a result, this gamification approach proved to be a more effective way to gather student data compared to other methods applied in previous academic years, which often failed due to many students ignoring optional activities.Xunta de Galicia | Ref. ED431B 2020/3

Systematic Literature Review of Predictive Analysis Tools in Higher Education

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

Contribucións a analíticas de aprendizaxe enfocadas á avaliación e á aprendizaxe autorregulada

In recent years, the data mining and analysis disciplines have seen an important increase in relevance, both for enterprises and in research. The set of techniques belonging to these knowledge fields have applications in a very wide variety of contexts, among which is the educational one. The particularizations of these two disciplines for the educational area are known as educational data mining (EDM) and learning analytics (LA). The latter is the main topic of this project. They are defined as a series of techniques for the measurement, acquisition, analysis and representation of data about students and their contexts, with the objective of understanding and optimizing learning and the environments in which it occurs. The proposed research project will explore the possibilities that learning analytics can offer with the goal of improving the learning process in university courses, paying special attention to assessment tasks and self-regulated learning (SRL) approaches. In order to do this, student data will be analyzed. This data can be obtained from learning management systems (LMS), which are commonly used in current university courses. As a result of analysis, indicators of student progress, as well as performance predictions, will be obtained. These results can be useful for instructors to identify struggling students at an early stage, as well as providing statistics so that the students themselves can effectively assess their own progress. This project fits into an important research category inside the education technologies field. The main goal will be to propose original contributions to the learning analytics discipline, achieving a positive impact within the reaserch community in this knowledge field.A lo largo de los últimos años, las disciplinas de minería y análisis de datos han adquirido un creciente interés tanto en el ámbito de la empresa como en el de la investigación. Las técnicas derivadas de estos campos de conocimiento tienen aplicaciones en una gran variedad de contextos, entre los que se encuentra el educativo. Las particularizaciones de estas dos disciplinas al ámbito educativo se conocen como minería de datos en educación (EDM: Educational Data Mining) y analíticas de aprendizaje (LA: Learning Analytics). Las analíticas de aprendizaje constituyen la temática central del trabajo. Se definen como una serie de técnicas para la medida, adquisición, análisis y representación de datos acerca de estudiantes y sus contextos, con la finalidad de entender y optimizar el aprendizaje y los entornos en los que ocurre. El proyecto de trabajo de investigación propuesto explorará las posibilidades que las técnicas de analíticas de aprendizaje pueden ofrecer con el objectivo de mejorar el proceso de aprendizaje en contextos de educación universitaria, prestando especial atención a los procesos de evaluación y a las aproximaciones basadas en el aprendizaje autorregulado (SRL: Self-Regulated Learning). Para esto, se establecerá un proceso de análisis de datos sobre estudiantes, que se pueden obtener de las plataformas de apoyo en línea comúnmente usadas en cursos universitarios en la actualidad (LMS: Learning Management System). Como resultado del análisis, se obtendrán indicadores de progreso del alumno durante el curso, así como predicciones de rendimiento. Estos resultados pueden ser útiles para que los instructores sean capaces de identificar en una etapa temprana a estudiantes con dificultades, así como otorgar estadísticas para que los propios estudiantes puedan evaluar con efectividad su progreso. Este trabajo se encuadra dentro de una rama importante de investigación en el campo de las tecnologías educativas. Mediante este proyecto se buscará realizar contribuciones originales a la disciplina de analíticas de aprendizaje, logrando así un impacto positivo dentro de la comunidad de investigadores en este campo.Ao longo dos últimos anos, as disciplinas de minaría e análise de datos teñen adquirido un crecente interese tanto no ámbito da empresa como no da investigación. As técnicas derivadas destes campos de coñecemento teñen aplicacións nunha grande variedade de contextos, entre os que se atopa o educativo. As particularizacións destas dúas disciplinas ao ámbito educativo coñécense como minaría de datos en educación (EDM: Educational Data Mining) e analíticas de aprendizaxe (LA: Learning Analytics). As analíticas de aprendizaxe constitúen a temática central do traballo. Defínense como unha serie de técnicas para a medición, adquisición, análise e representación de datos acerca de estudantes e os seus contextos, coa finalidade de entender e optimizar a aprendizaxe e os entornos nos que ocorre. O proxecto de traballo de investigación proposto explorará as posibilidades que as técnicas de analíticas de aprendizaxe poden ofertar co obxectivo de mellorar o proceso de aprendizaxe en contextos de educación universitaria, prestando especial atención aos procesos de avaliación e ás aproximacións baseadas na aprendizaxe autorregulada (SRL: Self-Regulated Learning). Para isto, establecerase un proceso de análise de datos sobre estudantes, que se poden obter das plataformas de apoio en liña comunmente usadas en cursos universitarios na actualidade (LMS: Learning Management System). Como resultado da análise, obteranse indicadores de progreso do alumno durante o curso, así como predicións de rendemento. Estes resultados poden ser útiles para que os instructores poidan identificar nunha etapa temprana a estudantes con dificultades, así como outorgar estatísticas para que os propios estudantes poidan evaluar con efectividade o seu progreso. Este traballo encádrase dentro dunha rama importante de investigación no campo das tecnoloxías educativas. Mediante este proxecto buscarase realizar contribucións orixinais á disciplina das analíticas de aprendizaxe, logrando así un impacto positivo dentro da comunidade de investigadores neste campo.Xunta de Galicia | Ref. ED481A-2019/29

Systematic literature review of predictive analysis tools in higher education

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.Departamento de Educación da Xunta de Galicia | Ref. (TIN2016-80515-R AEI / EFRD, UE)Agencia Estatal de Investigaciones | Ref. (TIN2016-80515-R AEI / EFRD, UE)European Regional Development Fund (ERDF) | Ref. (TIN2016-80515-R AEI / EFRD, UE