L@S

Behavior @ Scale

Fri Jul 3, 10:45 AM–12:00 PM · North 203

Educational Data Mining Methods Learning at Scale & MOOCs Learner & Student Modeling

★ Notable speakers

René Kizilcec ★★ — Learning at scale; algorithmic fairness and equity in digital education

Di Xu ★★ — Causal research on online learning effectiveness; equity gaps in community college student outcomes

Conrad Borchers ★ — AI-supported self-regulated learning; intelligent tutoring systems; educational data mining; ordered network analysis

Tomohiro Nagashima ★ — Worked examples and self-explanation in intelligent tutoring; participatory design in learning technology; self-regulated learning support

Benjamin W. Domingue ★ — Psychometrics, item response theory, causal inference in education, genomics of educational outcomes, the Item Response Warehouse open-data initiative

A grouped session of L@S talks investigating learner behavior patterns and their effects on outcomes in large-scale educational platforms.

3 talks in this session

Understanding Student Effort Using Reaction Time Propensities During Problem Solving at Scale

Conrad Borchers, Lijin Zhang, Kexin Yang, Tomohiro Nagashima, Benjamin W. Domingue

Adaptive & Personalized Learning Learner & Student Modeling Affect & Engagement Detection

A Large-Scale Analysis of Student Behavior with Pedagogically Constrained LLM Tutors

Chang Liu, Loc Hoang, Rene Kizilcec, Bo Wu

Generative AI & Large Language Models Prompt Engineering & RAG Self-Regulated Learning

Student, Course Design, or Context? Studying the Determinants of Academic Procrastination at Scale

Jinwon Kim, Qiujie Li, Conrad Borchers, Zilu Jiang, Di Xu

Learner & Student Modeling Predictive Modeling & Early Warning Motivation, Gamification & Engagement