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AI, Psychometrics, and Data Analysis (Table 24)
Thu, April 11, 9:00 to 10:30am, Pennsylvania Convention Center, Floor: Level 200, Exhibit Hall BSession Type: Roundtable Session
Abstract
This roundtable includes a collection of papers focused on AI methods, latent measurement variables in randomized control trials, missing data in predictive modeling, and new perspectives on simulating non-normally distributed data. Studies shared in this session include simulation studies as well as applied, empirical examples of the various methodologies.
Sub Unit
Chair
Papers
Analyzing Constructed Responses in Educational Survey With Latent Dirichlet Allocation: A Demonstration on Students’ Career Aspiration - Yuxiao Zhang, Purdue University; Nielsen Pereira, Purdue University/Gifted Education Research & Resource Institute; David Arthur, Purdue University; Hua-Hua Chang, Purdue University; Zafer Ozen, Purdue University; Hernan Castillo-Hermosilla, Purdue University; Brenda Cavalcante Matos, Purdue University; Tugce Karatas, Purdue University; Shahnaz Safitri, Purdue University
Considerations When Simulating Data From the g-and-h Family of Distributions - Oscar L. Olvera Astivia, University of Washington
Improved Latent Semantic Analysis Approach in Automated Scoring of Rater-Mediated Assessments: Example of Creativity Assessments - Ziyue Wang, University of Science and Technology of China; Haiying Long, University of Kansas; Jue Wang, University of Science and Technology of China
Multidimensional Fully Latent Principal Stratification - Sooyong Lee, WIDA; Adam C. Sales, Worcester Polytechnic Institute; Hyeon-Ah Kang, University of Texas at Austin; Tiffany A. Whittaker, University of Texas at Austin
Variable Selection and Binary Prediction With Incomplete Data: Balance Between Fairness and Precision - He Ren, University of Washington; Chun Wang, University of Washington