May152023 by Prof. Dr. Hendrik DrachslerNo Comments
New research program: Towards Highly Informative Learning Analytics

New research program: Towards Highly Informative Learning Analytics

Artificial Intelligence, Assessment, Book, Computational Psychometrics, Computer-supported collaborative learning, Feedback, Further Education, Higher Education, Keynote, Learning Analytics, Learning Design, Research Methods, School
On May 12, 2023, the Highly Informative Learning Analytics (HILA) research program of the EduTec@DIPF, studiumdigitale@Goethe University Frankfurt and the Open Learning and Instruction group@Open Universiteit was presented by Hendrik Drachsler at the main campus of the Open University of the Netherlands. The release of the HILA research program marks a significant milestone for the collaboration in the field of learning analytics between of the Dutch-German research collective.  The HILA research program is focused on developing new tools and methods to collect, analyze, and interpret data that can help educational institutions to understand the learning process better. As part of the program's launch, a keynote by Ioana Jivet on student-facing learning analytics was provided. Ioana reported on two empirical studies investigating the effect of data-driven feedback on students. [pdf-embedder url="https://edutec.science/wp-content/uploads/2023/05/2023_05-Keynote-Symposium-Hendrik.pdf"…
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Dec142022 by Atezaz AhmadNo Comments
New Pub: Causal Inference and Bias in Learning Analytics

New Pub: Causal Inference and Bias in Learning Analytics

Journal, Learning Analytics, Literature review, Open access, Publication, Research Methods
Learning Analytics is an applied field of research with the goal of producing actionable knowledge to improve student learning. This requires knowledge about cause-and-effect. However, randomized experiments, the usual vehicle for causality, are not always feasible nor desirable. Researchers are then left with observational data, from which they are, understandably, hesitant to draw causal inferences. Fortunately, there has been a lot of progress on the topic of causality in the last two decades. One prominent framework uses Directed Acyclic Graphs (DAGs) to graphically reason about cause-and-effect and/or bias. This primer, authored by Joshua Weidlich, Dragan Gasevic, and Hendrik Drachsler, published in the Journal of Learning Analytics, introduces DAGs to Learning Analytics.  Using fictitious and published examples, we show how DAGs are a principled approach to a) improve causal inferences for…
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