FoL 2026
EDM

AI Methods & Metrics

Wed Jul 1, 2:00 PM–3:15 PM · North 202
Educational Data Mining Methods Generative AI & Large Language Models
★ Notable speakers
Ryan S. Baker ★★ — Educational data mining; BROMP observational protocol; student disengagement detection
Cristina Conati ★★ — Intelligent tutoring systems; user modeling; affective computing; explainable AI in education
Conrad Borchers ★ — AI-supported self-regulated learning; intelligent tutoring systems; educational data mining; ordered network analysis
Jaclyn Ocumpaugh ★ — Automated detection of student affect and engagement; co-developer of the BROMP field-observation protocol
Luc Paquette ★ — Educational data mining, student behavior modeling, hint usage analysis, self-regulated learning in ITS

EDM #11 | Long Paper Session

3 talks in this session

No Single Metric Tells the Whole Story: Interpreting Multiple Pattern Mining Metrics
Andres Felipe Zambrano, Jaclyn Ocumpaugh, Ryan S. Baker, Zhanlan Wei, Xiner Liu
Educational Data Mining Methods Psychometrics & Educational Measurement
Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding
Conrad Borchers, Bahar Shahrokhian, Francesco Balzan, Elham Tajik, Sreecharan Sankaranarayanan
Generative AI & Large Language Models AI Agents & Agentic Systems Educational Data Mining Methods
Characterizing Students’ LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks
Minju Park, Ivan Orozco Vasquez, Cristina Conati
Generative AI & Large Language Models Educational Data Mining Methods Self-Regulated Learning