Author: Bart Rienties
Abstract:
Over the last twelve years under the umbrella term learning analytics a range of researchers and practitioners have used data to predict students’ affect, behaviour, and cognition. Some have used “objective” click-stream data, others used observational data, and others used self-report data like surveys and even qualitative data from interviews and focus groups. While there seems to be an implicit belief in the learning analytics and wider learning science community that objective data is better than subjective data, our research at IET does not always support this. For example, self-report measures based on questionnaires have been widely used in educational research to study implicit and complex constructs such as motivation, emotion, cognitive and metacognitive learning strategies. However, the existence of potential biases in such self-report instruments might cast doubts on the validity of the measured constructs. The emergence of trace data has sparked a controversial debate on how we measure learning. In this keynote I will provide a range of examples from PhD students and researchers that have explored various forms of data in understanding how people learn in blended and online environments, and how we might be able to overcome some of the biases in trying to predict learning.
Speaker bio:
Dr. Bart Rienties is Professor of Learning Analytics and programme lead of the learning analytics and learning design research programme at the Institute of Educational Technology at the Open University UK. He leads a group of academics who provide university-wide learning analytics and learning design solutions and conduct evidence-based research of how students and professionals learn. As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Professional Development, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects, and has received a range of awards for his educational innovation projects.
He has published over 285 academic outputs, and is the 2nd most published author on Networks in Education in period 1969-2020 (Saqr et al. 2022), the 3rd most cited author on higher education internationalisation in Asia in the period 2013-2018 (Can & Hou, 2021), the 4th most cited author and contributor in Learning Analytics in the period 2011-2018 (Adeniji, 2019), the 5th most published author on internationalisation in the period 1900-2018 (Jing et al. 2020) and the 7th most published author on social network analysis in social sciences in the period 1999-2018 (Su et al. 2020), and the 14th most published author on educational technology in the period 2015-2018 (West & Bodily, 2020).
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