Crowdsourcing Personalized Online Education Closing Comments Eric Horvitz

Crowdsourcing Personalized Online Education Great people, conversations, presentations! Thanks for investing the time & travel. Thanks for sharing your creative ideas, insights, directions, and enthusiasm.

Crowdsourcing Personalized Online Education Inflection point: Methods, connectivity, scale, interest in online ed. Evolving ideas & prototypes New directions in the air

Crowdsourcing Personalized Online Education Multiple communities: Opportunities and challenges Scaling Education Online Data!! Goals Lab! Computer Science Education

Crowdsourcing Personalized Online Education Multiple communities: Opportunities and challenges Scaling Education Online Methods Models Insights Designs Computer Science Education DataLearnPredictAct Designs for interaction Just build it. Automate! Graphical models POMDPs Machine learning Optimize! Mechanism design Practices & themes Small n studies Cognitive models Daily challenges In vivo studies Protocol analysis Tutor learning ITS AI-ED CAI Cognitive psychology HCI AI HCOMP CogSci

Crowdsourcing Personalized Online Education Seeking synthesis and deep understanding – building new community Education Computer Science Cognitive psychology Practices & themes Small n studies Cognitive models Daily challenges In vivo studies Protocol analysis Tutor learning DataLearnPredictAct Designs for interaction Just build it. Automate! Graphical models POMDPs Machine learning Optimize! Mechanism design ITS AI-ED CAI HCI: Mechanisms, de & Studies AI: Data  Learn  Predict  Act Psych/education: Cognitive models HCI AI HCOMP CogSci

Crowdsourcing Personalized Online Education Seeking synthesis and deep understanding – building new community Education Computer Science Cognitive psychology Practices & themes Small n studies Cognitive models Daily challenges In vivo studies Protocol analysis Tutor learning DataLearnPredictAct Designs for interaction Just build it. Automate! Graphical models POMDPs Machine learning Optimize! Mechanism design ITS AI-ED CAI HCI: Mechanisms, de & Studies AI: Data  Learn  Predict  Act Psych/education: Cognitive models HCI AI HCOMP CogSci

Crowdsourcing Personalized Online Education Seeking synthesis and deep understanding – building new community Education Computer Science Cognitive psychology Practices & themes Small n studies Cognitive models Daily challenges In vivo studies Protocol analysis Tutor learning DataLearnPredictAct Designs for interaction Just build it. Automate! Graphical models POMDPs Machine learning Optimize! Mechanism design HCOMP ITS AI-ED CAI Building community HCI: Mechanisms, de & Studies AI: Data  Learn  Predict  Act Psych/education: Cognitive models HCI AI CogSci

Crowdsourcing Personalized Online Education Seeking synthesis and deep understanding – building new community Education Computer Science Cognitive psychology Practices & themes Small n studies Cognitive models Daily challenges In vivo studies DataLearnPredictAct Designs for interaction Just build it. Automate! Graphical models POMDPs Machine learning Optimize! Mechanism design ITS AI-ED HCI AI HCOMP CogSci

Crowdsourcing Personalized Online Education Seeking synthesis and deep understanding – building new community Education Computer Science Cognitive psychology Practices & themes Small n studies Cognitive models Daily challenges In vivo studies DataLearnPredictAct Designs for interaction Just build it. Automate! Graphical models POMDPs Machine learning Optimize! Mechanism design ITS AI-ED HCI AI HCOMP CogSci

Synthesis to understand mysteries of great teaching Richard Feynman on his beloved Mr. Bader: “When I was in high school, my physics teacher—whose name was Mr. Bader—called me down one day after physics class and said, "You look bored; I want to tell you something interesting." Then he told me something which I found absolutely fascinating, and have, since then, always found fascinating. . . . the principle of least action.” (Chapter 19, Vol.2, Feynman Lectures on Physics).

Mysteries of affect, attention, engagement Herb Simon, Reason in Human Affairs, Harry Camp Lecture, Stanford, 1982

Keynote at ITS by an outsider in 2000…

Keynote at ITS by an outsider in 2000…

Keynote at ITS by an outsider in 2000…

Keynote at ITS by an outsider in 2000…

Keynote at ITS by an outsider in 2000… Model assessed manually with a psychologist. Predict kids’ forthcoming loss of engagement with Microsoft childrens’ software applications (1996). Might this approach be employed in online tutoring systems? K. Risden, H. 1996

Performance, & engagement 19962011: Opportunities to learn rich models from data e.g., CrowdSynth: 34m votes, 100k participants Machine vision Current activity User’s Long-term activities Performance, & engagement System actions E. Kamar, S. Hacker, H. Combining Human and Machine Intelligence in Large-scale Crowdsourcing, 2012.

Crowdsourcing Personalized Online Education Directions & Follow up Research directions Community HCI: Mechanisms, de & Studies AI: Data  Learn  Predict  Act Psych/education: Cognitive models

HCI: Mechanisms, de & Studies AI: Data  Learn  Predict  Act Psych/education: Cognitive models