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…

Background: Providing feedback in massive open online courses (MOOCs) is challenging due to the massiveness and heterogeneity of learners' population. Learning analytics (LA) solutions aim at scaling up feedback interventions and supporting instructors in this endeavour. Paper Objectives: This paper focuses on instructor-led feedback mediated by…

Engagement is critical in learning, including computer-supported collaborative learning (CSCL). Previous studies have mainly measured engagement using students' self-reports which usually do not capture the learning process or the interactions between group members. Therefore, researchers advocated developing new and innovative engagement…

Learning analytics research presents challenges for researchers embracing the principles of open science. Protecting student privacy is paramount, but progress in increasing scientific understanding and improving educational outcomes depends upon open, scalable and replicable research. Findings have repeatedly been shown to be contextually…

Drawn from the SQELT Erasmus+ project, this article explores how learning analytics is implemented at a set of six European universities in the context of their performance data management models, including its multiple functions and ethical issues. It further identifies possible good practice and policy recommendations at decision-making level.…

There is growing interest in applying gamification to adaptive learning systems to motivate and engage students during the learning process. However, previous studies have reported unexpected results about student outcomes in these systems. One of the causes of these unfavorable effects is the lack of monitoring and adaptation of gamification…

As educators turn to technology to supplement classroom instruction, the integration of natural language processing (NLP) into educational technologies is vital for increasing student success. NLP involves the use of computers to analyze and respond to human language, including students' responses to a variety of assignments and tasks. While NLP…

Algorithmic decision-making systems such as Learning Analytics (LA) are widely used in an educational setting ranging from kindergarten to university. Most research focuses on how LA is used and adopted by teachers. However, the perspective of students and parents who experience the (in)direct consequences of these systems is underexplored. This…

Background: Dashboards play a prominent role in learning analytics (LA) research. In collaboration activities, dashboards can show traces of team participation. They are often evaluated based on students' perceived satisfaction and engagement with the dashboard. However, there is a notable methodological gap in understanding how these dashboards…

Despite the growing use of large language models (LLMs) in educational contexts, there is no evidence on how these can be operationalized by students to generate custom datasets suitable for teaching and learning. Moreover, in the context of network science, little is known about whether LLMs can replicate real-life network properties. This study…

Institutions of higher education possess large amounts of learning-related data in their student registers and learning management systems (LMS). This data can be mined to gain insights into study paths, study styles and possible bottlenecks on the study paths. In this study, we focused on creating a predictive model for study completion time…

The integration of advanced learning analytics and data-mining technology into higher education has brought various opportunities and challenges, particularly in enhancing students' self-regulated learning (SRL) skills. Analyzing developed features for SRL support, it has become evident that SRL support is not a binary concept but rather a…

While high-accuracy machine learning (ML) models for predicting student learning performance have been widely explored, their deployment in real educational settings can lead to unintended harm if the predictions are biased. This study systematically examines the trade-offs between prediction accuracy and fairness in ML models trained on the…

Intelligent tutoring systems are increasingly used in schools, providing teachers with valuable analytics on student learning. However, many teachers lack the time to review these reports in detail due to heavy workloads, and some face challenges with data literacy. This project investigates the use of large language models (LLMs) to generate…

Analyzing how participants learn from each other during online forums on discussion boards or social media platforms is often challenging. One of the predominant methods of analyzing such learning is through qualitative content analysis or interaction analysis. The Interaction Analysis Model (IAM), developed by Gunawardena, Lowe and Anderson which…