AIM Analytics: Improving Online Learning Outcomes Using Large-Scale Learning Analytics

The Center for Academic Innovation is charged with creating a culture of innovation in learning. As a catalyst for academic innovation we aim to shape the future of learning and redefine public residential education at a 21st century research university by unlocking new opportunities and enablingpersonalized,engaged, andlifelonglearning for the U-M community and learners around the world.

U-M Center for Academic Innovation

Join us on Monday, November 12 from 12:00 p.m. to 1:30 p.m. in the Koessler Room of the Michigan League (911 N University Ave) for AIM Analytics as we welcome in <A HREF="https://dan7davis.github.io/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Dan Davis</A>, PhD student in Computer Science, Learning Analytics from <A HREF="https://www.tudelft.nl/en/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">TU Delft</A> in the Netherlands.
AIM Analytics is a bi-weekly seminar series for researchers across U-M who are interested in learning analytics. The field of learning analytics is a multi and interdisciplinary field that brings together researchers from education, learning sciences, computational sciences and statistics, and all discipline-specific forms of educational inquiry.
Title: Improving Online Learning Outcomes Using Large-Scale Learning Analytics
Abstract: 
This talk will cover a holistic approach to improving learning outcomes and behavior in large-scale learning environments---namely MOOCs. I begin by sharing the results of a study exploring the extent to which learners follow (or deviate from) the designed learning path and the impact this behavior has on eventual learning outcomes. We next take a deeper dive into the design of online courses with a large-scale learning design approach, where I'll present an automated method developed to categorize courses based on their design. With these trends in learning & teaching behavior in mind, the talk will conclude with the results of a series of randomized experiments (A/B tests) carried out in live MOOCs designed to provide additional support to learners. From these experiments we arrive at a better understanding of which instructional & design strategies are most effective for improving learning outcomes and behavior at scale.
Bio: Dan's research uses and advances learning analytics techniques in open, online education at scale by pushing the boundaries towards personalized & adaptive learning environments. Dan develops methods to gain a deeper understanding about how the design of online learning environments affects learner success and engagement, often by implementing and testing instructional interventions at scale using randomized controlled experiments. Dan earned his BA in English, Writing & Mass Communication with a minor in Graphic Design from Assumption College in Worcester, Mass. His MA is from Georgetown University in Communication, Culture & Technology, and he is currently finishing his PhD in Computer Science, Learning Analytics from TU Delft in the Netherlands.
Lunch will be provided.

Michigan League

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AIM Analytics: Improving Online Learning Outcomes Using Large-Scale Learning Analytics

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