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Machine Learning Techniques II: Advancing Coding/Essay Scoring and Estimation of Treatment Effects (Table 2)
Thu, April 24, 1:45 to 3:15pm MDT (1:45 to 3:15pm 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 comprehensive review, 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 data quality, imputation, propensity scoring, essay scoring performance, and estimation of individual treatment effects.
Sub Unit
Chair
Papers
Improving Occupational Data Quality: Automated Coding and Corpus Review Using Pretrained Models - Wen Li, Beijing Normal University; Fei Yang, Beijing Normal University; Sheng Zhang, Beijing Normal University
Machine-Learning Models for Handling Data-Missingness in Educational Research: A Comprehensive Review - Comfort Happiness Omonkhodion, University of Central Florida; Haiyan Bai, University of Central Florida; Oluwaseun Peter Farotimi, University of Central Florida
Machine Learning Approaches for Propensity Score Estimation With Semi-Continuous Exposure - Huibin Zhang, University of Tennessee
Developing a Generic Essay Scorer for Online Practice Writing Tests of Statewide Assessments - Yi Gui, The College Board
Evaluating Emerging Machine Learning and Multiple Imputation Methods for Estimating Individual Treatment Effects - Sangbaek Park, University of Louisville