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Rets, Irina; Herodotou, Christothea; Bayer, Vaclav; Hlosta, Martin; Rienties, Bart – International Journal of Educational Technology in Higher Education, 2021
Learning analytics dashboards (LADs) can provide learners with insights about their study progress through visualisations of the learner and learning data. Despite their potential usefulness to support learning, very few studies on LADs have considered learners' needs and have engaged learners in the process of design and evaluation. Aligning with…
Descriptors: Learning Analytics, Educational Technology, Usability, College Students
Rizvi, Saman; Rienties, Bart; Rogaten, Jekaterina; Kizilcec, René F. – Journal of Computing in Higher Education, 2020
Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of…
Descriptors: Online Courses, Learning Processes, Science Education, Learner Engagement
Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan – Applied Cognitive Psychology, 2020
Worked-examples have been established as an effective instructional format in problem-solving practices. However, less is known about variations in the use of worked examples across individuals at different stages in their learning process in student-centred learning contexts. This study investigates different profiles of students' learning…
Descriptors: Individual Differences, Preferences, Demonstrations (Educational), Learning Analytics
Tempelaar, Dirk; Rienties, Bart; Nguyen, Quan – Educational Technology & Society, 2021
Precision education requires two equally important conditions: accurate predictions of academic performance based on early observations of the learning process and the availability of relevant educational intervention options. The field of learning analytics (LA) has made important contributions to the realisation of the first condition,…
Descriptors: Learning Analytics, Individualized Instruction, Blended Learning, Electronic Learning
Nguyen, Quan; Rienties, Bart; Whitelock, Denise – Journal of Learning Analytics, 2020
The use of analytical methods from learning analytics (LA) research combined with visualizations of learning activities using learning design (LD) tools and frameworks has provided important insight into how instructors design for learning. Nonetheless, there are many subtle nuances in instructors' design decisions that might not easily be…
Descriptors: Instructional Design, Online Courses, Distance Education, Electronic Learning
Herodotou, Christothea; Hlosta, Martin; Boroowa, Avinash; Rienties, Bart; Zdrahal, Zdenek; Mangafa, Chrysoula – British Journal of Educational Technology, 2019
This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment.…
Descriptors: Learning Analytics, Teacher Empowerment, Distance Education, College Faculty
Holmes, Wayne; Nguyen, Quan; Zhang, Jingjing; Mavrikis, Manolis; Rienties, Bart – Distance Education, 2019
There has been a growing interest in how teaching might be informed by "learning design" (LD), with a promising method for investigating LD being offered by the emerging field of "learning analytics" (LA). In this study, we used a novel LA for LD methodology to investigate the implementation of LD in an online distance learning…
Descriptors: Learning Analytics, Instructional Design, Electronic Learning, Distance Education
Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek; Hlosta, Martin – Educational Technology Research and Development, 2019
By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction.…
Descriptors: Prediction, Learning Analytics, Teacher Role, Teacher Attitudes
Nguyen, Quan; Rienties, Bart; Richardson, John T. E. – Assessment & Evaluation in Higher Education, 2020
Although the attainment gap between black and minority ethnic (BME) students and white students has persisted for decades, the potential causes of these disparities are highly debated. The emergence of learning analytics allows researchers to understand how students engage in learning activities based on their digital traces in a naturalistic…
Descriptors: Learning Analytics, Academic Achievement, Achievement Gap, Racial Differences
Tempelaar, Dirk; Rienties, Bart; Nguyen, Quan – International Association for Development of the Information Society, 2019
Learning analytic models are built upon traces students leave in technology-enhanced learning platforms as the digital footprints of their learning processes. Learning analytics uses these traces of learning engagement to predict performance and provide learning feedback to students and teachers when these predictions signal the risk of failing a…
Descriptors: Learner Engagement, Outcomes of Education, Learning Processes, Learning Analytics
Herodotou, Christothea; Naydenova, Galina; Boroowa, Avi; Gilmour, Alison; Rienties, Bart – Journal of Learning Analytics, 2020
Despite the potential of Predictive Learning Analytics (PLAs) to identify students at risk of failing their studies, research demonstrating effective application of PLAs to higher education is relatively limited. The aims of this study are: (1) to identify whether and how PLAs can inform the design of motivational interventions; and (2) to capture…
Descriptors: Learning Analytics, Predictive Measurement, Student Motivation, Intervention
Herodotou, Christothea; Rienties, Bart; Verdin, Barry; Boroowa, Avinash – Journal of Learning Analytics, 2019
Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders…
Descriptors: Prediction, Data Analysis, Higher Education, Distance Education