Which clicks and ties matter?
Date and time: Oct 22 2020, 12pm – 1pm
Speaker: Mohammad Saqr, KTH Royal Institute of Technology
Title: Which clicks and ties matter?
Zoom: https://kth-se.zoom.us/j/67432682790?pwd=dVgzbjRSbUVFT2FOYTByYlZrTU9BUT09
Meeting ID: 674 3268 2790
Password: DF2020
Watch the recorded presentation:
Abstract: Learning analytics has emerged as a research endeavour to explore the opportunities of making sense of data to understand (and support) learners and their learning environments as well as to optimize the learning process through the development of actionable insights. The applications of learning analytics are rapidly expanding, e.g., provision of personalized support for the deserving students, recommendations of learning resources, help teachers monitor students’ learning, improve retention rates and improve organizational return on investment. Im my presentation, I will present my work which revolved around four threads; firstly, methodological innovation in learning and teaching; secondly, focus on replicability and validity to deliver reproducible impact that can be generalized to broader context; thirdly, use and contribute to learning theory and educational frameworks; fourth, use big data for better inferences.
Bio: Mohammed Saqr is a postdoc researcher with Olga Viberg and Stefan Hrastinsk at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology. I had a PhD in learning analytics from Stockholm University, I worked at Paris Descartes in Paris on spatio-temporal networks, at the University of Eastern Finland on learning analytics before coming to KTH. My research interests are interdisciplinary including Analytics and big data in education, Network Science, Medicine, and Open Science. My research in learning analytics focuses on social networks, temporal networks, process mining, sequence mining and temporal processes. The best place to know about my research is my Researchgate where I keep it updated https://www.researchgate.net/profile/Mohammed_Saqr3