This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student…

Research and development in learning analytics has established viable solutions for scaling personalised feedback to all students. However, questions remain regarding how such feedback is perceived, interpreted and acted upon by stakeholders. The present study reports on the analysis of focus group data from four courses to understand students'…

The objective of the research is to develop a methodology to analyse a set of data extracted from a learning management system, in order to implement a dashboard, which can be used by teachers to make timely and relevant decisions to improve the teaching-learning processes. The methodology used consisted of analysing 9,257 records extracted…

Purpose: The aims of this study are to examine affective responses of university students when viewing their own predictive learning analytics (PLA) dashboards, and to analyse how those responses are perceived to affect their self-regulated learning behaviour. Design/methodology/approach: A total of 42 Northern Irish students were shown their own…

The main objective of educational institutions is to achieve the integral development of their students in their learning and knowledge construction process. One way to achieve these objectives is the accompaniment and continuous monitoring of students in this process, adapting the methods to their training needs. In online and mixed teaching…

In this paper, we develop a context-aware, tablet-based learning module for adult education. Specifically, we focus on adult education in healthcare-teaching learners to perform a medical screening procedure. Based upon how learners navigate through the learning module (e.g. swipe-speed and click duration, among others), we use machine learning to…

One of the main generic competencies in Higher Education is "Learning to Learn". The key component of this competence is the capacity for self-regulated learning (SRL). For this competence to be developed, peer feedback seems useful because it fosters evaluative judgement. Following the principles of peer feedback processes, an online…

The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related…

Predictive analytics has taken higher education by storm, with its promise of closing equity gaps, raising student retention rates, and increasing tuition revenue by keeping students enrolled. Many colleges and universities have made an investment in predictive analytics for student success initiatives, and even more are looking into implementing,…

The purpose of this study was to examine the predictive power of Academic and Learning Analytics models on the persistence, retention, and graduation rates for students enrolled in higher education institutions in the United States. Specifically, this study is concerned with the relationships between the present usage of Academic and Learning…

This paper provides a novel instructional methodology that is designed to conceptually address the four main challenges faced by 21st century students, who must learn in a multitude of educational settings (face to face, hybrid and online). The online learning neuroscience supported instructional methodology detailed in this article also provides…

Learner-centered coaching and feedback are relevant to various educational contexts. Spaced retrieval enhances long-term knowledge retention. We examined the efficacy of Blank Slate, a novel spaced retrieval software application, to promote learning and prevent forgetting, while gathering and analyzing data in the background about learners'…

In this article, Susan Haarman discusses the ways in which datafication technologies such as Big Data and algorithms have the potential to either challenge or exacerbate what Miranda Fricker calls epistemic injustice. She briefly defines epistemic injustice using Fricker's subsets of testimonial and hermeneutical injustice before moving to the…

Educational data mining focuses on exploring increasingly large-scale data from educational settings, such as Learning Management Systems (LMS), and developing computational methods to understand students' behaviors and learning settings better. There has been a multitude of research dedicated to studying the student learning process, leading to…

This case study reviews the implementation of a learning analytics (LA) system at a large southeastern university. The LA system uses cloud computing to automate data collection and distribution, providing faculty with insights into student performance and engagement. The system includes weekly alert emails and dashboards offering detailed…