Each project utilizes unique forms of teaching and learning data, and more importantly, they are finding ways to create impact with these data. We are excited for the HILT community to engage the speakers on both the impact of their projects and how they are making use of data.

Meeting Details

Time: 2PM to 3:30PM, January 24th, 2019

Location: David T. Ellwood Democracy Lab, Rubenstein Building, Room 414A , HKS (map)

Please RSVP: https://goo.gl/forms/6Su21Nz0sp7hKBCU2

Each presentation will be 15 minutes with 5 minutes for questions. We will follow up with a Q&A for all speakers after the three presentations. Socializing over refreshments will close the event, and we encourage participants to learn more about how their colleagues are using learning analytics.

Best Regards,

Daniel Seaton & Alexandra Sedlovskaya

Teachley (link)

Dan Levy and Colleagues

Teachly is an ed-tech application that brings the dynamics of a small classroom to larger lecture halls. This talk will highlight use cases and how disparate data come together to inform real time instruction.

Nearest Neighbors (link)

Andrew Ho, Graduate School of Education

Utilizing a relatively straightforward set of enrollment data to achieve big impact, this project aims to (1) connect instructors who teach the same students; (2) investigate whether students will learn more when making connections across different courses; (3) encourage open dialogue about teaching practices among instructors who share many students and support cross-course connections; (4) identify and nurture neighborhoods of instructors to spread effective teaching, improve student learning, and increase coherence and connections across the course catalog.

How People Learn (link)

Emily Shoemaker, Becca Miller, and Patricia Vanderbilt

How People Learn (HPL) is an online, pre-matriculation course for incoming Ed.M. students at HGSE aiming to provide all students with a common foundational learning experience on the topics of human development and learning. The course incorporates personalized learning components, including a recommendation engine to help students select content pathways that are relevant to their career and personal interests. After a summer 2018 pilot, the course team collected data from student evaluations as well as the recommendation results. We have analyzed this data and are now using the results to inform design work on a summer 2019 iteration of the course.