Use of university students' educational data for learning analytics has spurred a debate about whether and how to provide students with agency regarding data collection and use. A concern is that students opting out of learning analytics may skew predictive models, in particular if certain student populations disproportionately opt out and biases…

Learning analytics systems are software designed to aggregate student data to be analyzed for the purpose of delivering information to students, with the goal of increasing student success, academic goal completion, and retention. Despite being identified as stakeholders and beneficiaries of learning analytics, student perceptions make up a small…

Each year, when GCSE and A level results are published, a common talking point in media coverage is how results of male and female students differ. This reflects a popular fascination with such differences, but there is also a deeper, longstanding research interest in sex differences in education, not just in England, but around the world.…

In celebrating the 20th anniversary of the "International Journal of Educational Technology in Higher Education (IJETHE)," previously known as the "Revista de Universidad y Sociedad del Conocimiento (RUSC)," it is timely to reflect upon the shape and depth of educational technology research as it has appeared within the…

A major criticism brought to digital learning environments was that the individual learning activities cannot be monitored consistently. However, recent advancements of educational data mining and learning analytics allow a precise tracking of learner activities. Previous studies focused on learners' navigation profiles, academic achievements, or…

A combination of mathematical and statistical modelling techniques may be used to analyse student dropout behaviour. The aim of this study is to combine Survival Analysis and Analytic Hierarchy Process methodologies when identifying students at-risk of dropping out. This combination favours the institutional understanding of dropout as a dynamic…

Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…

Discussion forums provide students with accessible platforms for group discussions in e-learning environments. They also help lecturers to track and check student discussions. To improve student learning, it is important for lecturers to identify students' cognitive engagement in discussion forums. Therefore, this study aims to investigate…

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…

Violations of normality and homogeneity are common in educational data. When this occurs, the use of parametric statistics may be inappropriate. A generalized form of nonparametric analyses based on the Puri and Sen L statistic provides an alternative approach. Using a chi-square distribution, this technique is easy to apply and has significant…

Early alert systems (EAS) are an important technological tool to help manage and improve student retention. Data spanning 16,091 students over 156 weeks was collected from a regionally based university in Australia to explore various microeconometric approaches that establish links between EAS and student retention outcomes. Controlling for…

For many justifications, the collection, analysis, and use of educational data are central to the evaluation and improvement of students' progress and learning outcomes. The use of data in educational evaluation and decision making are expected to span all layers--from the institution, teachers, students, and classroom levels, providing a…

Higher education institutes are increasingly turning their attention to web-based learning management systems. The purpose of this study is to investigate whether data collected from LMS can be used to predict student performance in classrooms that use LMS to supplement face-to-face teaching. Data was collected from eight courses spread across two…

As the demand for lifelong learning increases, many working adults have turned to online graduate education in order to update their skillsets and pursue advanced credentials. Simultaneously, the volume of data available to educators and scholars interested in online learning continues to rise. This study seeks to extend learning analytics…

This volume focuses on the implications of digital technologies for educators and educational decision makers that are not widely represented in the literature. The chapters contained in the volume are based on the presentations at the 2020 edition of the CELDA conference and cover multiple developments in the field such as deploying learning…