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Machine Learning Techniques I: Advancing Equity, Prediction, and Measurement (Table 4)
Fri, April 25, 9:50 to 11:20am MDT (9:50 to 11:20am MDT), The Colorado Convention Center, Floor: Ballroom Level, Four Seasons Ballroom 4Session Type: Roundtable Session
Abstract
The papers in this roundtable represent a collection of simulation and applied studies utilizing various machine learning techniques in education. Theoretical understanding of ML and its use in educational studies are advanced through topics including design of dynamic treatment regimes, identification of structural barriers for students minoritized within STEM, prediction accuracy, and improvement of measurement and assessment methods through the use of process data.
Sub Unit
Chair
Papers
Exploring the Application of Machine Learning Techniques to Predict STEM Retention - Amanda J. Davis Simpfenderfer, College of William & Mary
Designing Optimal Dynamic Treatment Regimes Using TMLE for Personalized Math Course-Taking Plans - Chenguang Pan, Teachers College, Columbia University; Youmi Suk, Teachers College, Columbia University
Predicting STEM Career Interest in High School: A Machine Learning Study on In-School and Out-of-School-Time Experiences - Rongxiu Wu, Harvard University; Tingting Reid, Harvard University; Sue Sunbury, Harvard University; Philip M. Sadler, Harvard University; Gerhard Sonnert, Harvard University
Unlocking Insights in Educational Process Data With a Sequential Reservoir Method - Jiawei Xiong, Curriculum Associates; Qidi Liu, GlobalFoundries; Cheng Tang, University of Georgia