Skip to content

Details

This event will be a joint event with the Boston EdTech Meetup! (https://www.meetup.com/Boston-EdTech-Meetup) There’s a lot of interest and promise in applying data science and machine learning to improve student outcomes in the edtech space, so we hope this event can help folks from both meetup groups connect!

We’re sitting down with Tara Chiatovich, Research and Data Scientist at Panorama Education, to chat through one potential application of machine learning in the education space. Recently, Tara explored whether machine learning methods might be able to better predict student end of year scores compared to more traditional methods. Such predictions can play an important role in providing educators with the data they need to take actionable steps that improve student outcomes.

We’ll start with a product overview of what kind of data Panorama attempts to present to its educators and then dive into some more technical topics including:

  • What dataset was used for the research
  • Why machine learning seemed like a good fit for this use case
  • What traditional methods Panorama has already used to predict student end of year scores
  • What advantages, if any, did the machine learning methods have over the traditional methods
  • What are the pros and cons of the different methods in the context of presenting and explaining data to educators
  • What is the role of machine learning in educational research

While we’ll go into some technical details and methodologies, everyone should feel welcome to attend regardless of their technical background and familiarity with machine learning and data science concepts. We’ll be using a fireside chat format where we’ll ask clarifying questions and our goal is to help make these concepts approachable to folks of all backgrounds.

Tentative Agenda:
7:00 - Join early to network
7:10 - Opening Announcements
7:20 - Fireside Chat with Tara
8:00 - Open Q&A
8:20 - Breakout Rooms for Networking

Speaker Bio:
Tara Chiatovich is Research and Data Scientist at Panorama Education. In this role, she investigates links across distinct facets of students' learning and development based on a large, national dataset and informs the design of Panorama's products to align with best practices in research and data analysis. Tara has focused her attention on issues impacting learners throughout her career. Just prior to joining Panorama Education, she managed the Early Learning Study at Harvard, a large-scale study of young children's formal and informal learning, for which she provided guidance on the study's design, conducted analyses of its data, and communicated its aims and findings to researchers and the general public. Tara holds an Ed.M. in international education policy from Harvard University and a Ph.D. in educational psychology from Stanford University.

You may also like