EDM
Prediction, Modeling, & Recommendation
Thu Jul 2, 2:00 PM–3:15 PM · North 209
Predictive Modeling & Early Warning Learner & Student Modeling Adaptive & Personalized Learning Educational Data Mining Methods
★ Notable speakers
Adam Sales
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— Causal inference in educational data mining; combining machine learning with randomized trial analysis; principal stratification in ITS
Mingyu Feng
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— Large-scale randomized controlled trials evaluating intelligent tutoring systems (ASSISTments, MathSpring); IES-funded efficacy research on ed-tech
Johann Gagnon-Bartsch
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— Causal inference; statistical methods for unobserved confounders; RUV (removing unwanted variation)
Shinichi Konomi
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— Pervasive/ubiquitous computing; learning analytics; civic computing; human-data interaction
Conrad Borchers
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— AI-supported self-regulated learning; intelligent tutoring systems; educational data mining; ordered network analysis
Danielle R. Thomas
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— Human-AI hybrid tutoring; educational equity; AI-augmented tutoring for students with disabilities
EDM #13 | Long/Short Paper Session
4 talks in this session
From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning
Eric S. Qiu, Danielle R Thomas, Boyuan Guo, Vincent Aleven, Conrad Borchers
An Ensemble Stacking with Perception-aware Meta-learning (ESPM) for Apprenticeship Dropout Prediction in France
Duaa Baig, Diana Nurbakova, Sylvie Calabretto, Baba Mbaye
Improved Power in a Reanalysis of an ASSISTments Efficacy Trial by Leveraging Auxiliary Data
Adam Sales, Kevin Huang, Mingyu Feng, Johann Gagnon-Bartsch
Augmenting Student Profiles and Course Attributes with Large Language Models for Course Recommendation
Tianyuan Yang, Ren Baofeng, Chenghao Gu, Feike Xu, Boxuan Ma