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Data Science in Education
Tue, April 26, 4:15 to 5:45pm PDT (4:15 to 5:45pm PDT), Division Virtual Rooms, Division D - Section 2: Quantitative Methods and Statistical Theory Virtual Roundtable Session RoomSession Type: Roundtable Session
Abstract
This session focuses on machine learning topics in terms of model prediction and compared the performance of traditional statistical techniques and machine learning. The impact of coding schemes and machine learning methods on model prediction and variable selection was investigated in a nonlinear setting. In another study, machine learning models were introduced, compared with each other and traditional method such as multiple regression models in terms of their prediction accuracy. The performance of machine learning methods in detecting latent heterogeneity in academic growth was also examined. Machine learning methods coupled with large-scale data were also investigated to add contributions to existing research studies.
Sub Unit
Chair
Papers
A Monte Carlo Simulation on Nonlinear Effects of Likert-Scaled Items With Machine Learning - Tai Sun Jeong, Korea Institute for Curriculum and Evaluation; Jin Eun Yoo, Korea National University of Education; Hyeong Gwan Kim, Korea National University of Education
MOOC Learning Outcome Prediction Using Machine Learning Approaches - Xue Wen, Louisiana State University; Xuan Wang, The University of Texas Rio Grande Valley
Comparison of the Performance of Growth Mixture Model, Structural Equation Modeling (SEM) Tree, and SEM Forest in Modeling Latent Heterogeneity of High School Academic Growth - Amal Alhadabi, Kent State University/ Sultan Qaboos University
Predicting Students' Math Self-Efficacy: A Comparison of Traditional Multiple Linear Regression With Machine Learning Methods - Ya Zhang, Western Michigan University; Aaron Yokonia Mapondera, Western Michigan University
Exploration of Predictors for Teaching and Learning International Survey Teachers' Team Innovativeness Using glmmLasso, Machine Learning for Multilevel Data - Miryeong Koo, Korea National University of Education; Jin Eun Yoo, Korea National University of Education