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The previous two years, we hosted causal inference workshops at the EDM international conferences with great success. Over 30 people attended each of the workshops, and both workshops featured stimulating talks on recently completed projects, works in progress, and open questions. Due to the success of the first workshop, we would like to organize a third workshop on causal inference. Given the recent influx of users to remote learning platforms due to COVID-19, and the interest in investigating the effect of remote learning on students, the need to analyze observational data has grown. A workshop on causal inference can help facilitate discussion and educate new researchers on ways of analyzing the recent breadth of data available. In general, the workshop will be organized to stimulate discussion among participants, including, hopefully, constructive suggestions for open problems.

To improve student learning outcomes within online learning platforms, struggling students are often provided with on-demand supplemental instructional content. Recently, services like Yup (yup.com) and UPcheive (upchieve.org) have begun to offer on-demand live tutoring sessions with qualified educators, but the availability of tutors and the cost associated with hiring them prevents many students from having access to live support. To help struggling students and offset the inequities intrinsic to high-cost services, we are attempting to develop a process that uses large language representation models to algorithmically identify relevant support messages from these chat logs, and distribute them to all students struggling with the same content. In an empirical evaluation of our methodology we were able to identify messages from tutors to students struggling with middle school mathematics problems that qualified as explanations of the content. However, when we distributed these explanations to students outside of the tutoring sessions, they had an overall negative effect on the students' learning. Moving forward, we want to be able to identify messages that will promote equity and have a positive impact on students.

Poster for the 2022 NSF CSSI PI meeting NSF awards DRL#1931419, DRL#1891611