This article examines the work of predictive analytics in shaping the social worlds in which they thrive, and in particular the world of the first year of Great State University's student success initiative. Specifically, this article investigates the following research paradox: predictive analytics, as driven by a logic premised on predicting the…

With the widespread use of learning analytics (LA), ethical concerns about fairness have been raised. Research shows that LA models may be biased against students of certain demographic subgroups. Although fairness has gained significant attention in the broader machine learning (ML) community in the last decade, it is only recently that attention…

The dropout of students is one of the major obstacles that ruin the improvement of higher education quality. To facilitate the study of students' dropout in Moroccan universities, this paper aims to establish a clustering approach model based on machine learning algorithms to determine Moroccan universities categories. Our objective in this…

This paper offers a critical discourse analysis of documents relating to the introduction of predictive learning analytics at a small, UK university. Semiotic and interdiscursive analysis was carried out on texts from three different sources: the institution, the commercial analytics software provider, and one academic subject area. Authority…

Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first…

Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…

The article aims to develop a machine-learning algorithm that can predict student's graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics…

A significant amount of research has gone into predicting student performance and many studies have been conducted to predict why students drop out. A variety of data including digital footprints, socio-economic data, financial data, and psychological aspects have been used to predict student performance at the test, course, or program level.…

This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning learning design (LD) and learning analytics (LA) during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past massive open online course, where the learner data…