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Click on the underlined talk title to download slides: 


Invited speakers

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

Alina von Davier

Senior Vice President, ACTNext
An Illustration of an AI-based Educational Assistant and Its Underlying Learning Analytics

Shivani Rao

Senior Applied Researcher, LinkedIn 
An overview of AI problems applied to the domain of Online Learning

Adam Blum 

Senior Director, Emerging Technologies, ACT

Methods of Intersystem Measurement of Instructional Resource Efficacy

Zachary Pardos 

Assistant professor of Education and Information, UC Berkeley

Approaches to Scalable Personal Guidance in MOOCs and On Campus 

Emma Brunskill

Assistant professor in Computer Science, Stanford University

AI for Adaptive Curriculum

Anita Bowles, Jeremy Roschelle, Kelli Hill, Kodi Weatherholtz, and Denis Newman

Rosetta Stone, Digital Promise, Khan Academy, Empirical Education

Individual Presentations and Panel Discussion: Best Practices in Efficacy Research

Anita's Presentation

1a: Recommendation and prediction in online environments

Yuchi Huang, David Edwards, Lu Ou and Saad Khan 


GMMC: Generating Multimodal Micro Content

Priya Venkat, Sanghamitra Deb and William Ford


Using weak supervision techniques to improve student experiences at Chegg

Shamya Karumbaiah and Ryan S Baker

University of Pennsylvania

Predicting Quitting in Students Playing a Learning Game

1b: Tutoring 


David Lang, Sigtryggur Kjartansson, Jayadev Bhaskaran and Lucianna Bennoti

Stanford University

Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues

Katherine Stasaski and Marti Hearst

UC Berkeley 

Foreign Language Tutoring via Grounded Dialogue

Zoha Zargham, Sakshi Bhargava and Sanghamitra Deb


Personalization at Chegg

2a: Learning analytics

Petr Johanes 

Stanford University 

Putting the Philosophy of Modeling to Work for Learning Analytics

Ryan Montgomery and Eric Greenwald

UC Berkeley 

Learning and Analytics, Centered around Evidence ⚡️

Jamie Poskin


TeachFX: a revolutionary new way to measure student engagement⚡️

2b: MOOCs

Varun Ganapathi, Byung-Hak Kimand Ethan Vizitei

Stanford University/Udacity

Predicting and Improving Student Performance with Machine Learning

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