BayLan Conference

Bay Area Learning Analytics Network (BayLAN) Conference
#087aa9
Project Type: 
Data Science
Topics: 
analytics
machine learning
learning outcomes
Client: 
Learning analytics community
Link: 
BayLan
Purpose

The 2019 BayLAN conference, co-organized by GSE IT, brought together learning analytics researchers from industry and academia in the greater Bay Area to share the results of research, discuss ongoing work, and collaborate on new ideas.

Team

The Bay Area Learning Analytics Network (BayLAN) is a researcher-led group focused on improving education through data-driven decisions and implementations. GSE IT partnered with BayLAN for the 2019 conference in order to more meaningfully connect cutting edge data scientists in and around Stanford Graduate School of Education.

Artifacts

Mitchell Stevens
Director of the Center of Advanced Research through Online Learning (CAROL), Stanford University

Personalization, Prediction, Tracking: Parsing Responsible Use of Student Data in Higher Education

Emma Brunskill
Assistant professor in Computer Science, Stanford University

AI for Adaptive Curriculum

Petr Johanes
Ph.D. candidate, Learning Sciences and Technology Design, Stanford University

Putting the Philosophy of Modeling to Work for Learning Analytics

David Lang
Ph.D. candidate, Economics of Education, Stanford University

Predicting Clickstream Engagement in MOOCs using Transcript Level Features

Impressions

“In learning analytics, academic and industry experts often speak two different languages. The BayLAN conference provides an opportunity to bridge this gap and make knowledge sharing between the two worlds more efficient.”

-Elena Semeyko, LDT ‘19