Using Causal Forest to Examine Heterogeneous Treatment Effects of Multi-measures Assessment System at U.S. Community Colleges
What The paper is currently under embargo and the abstract can therefore not be publicly disclosed. Below is a hint of what you can expect; please join the talk to learn more!
In the U.S., community colleges require incoming students to take a standardized test to determine their college-readiness. Critics have argued that using standardized tests as a single-measure assessment (SMA) system poorly predicts students’ postsecondary outcomes, alternatively proposing a multi-measure assessment (MMA) system that considers several factors to assess one’s college readiness. [UNDER EMBARGO] The finding suggests that 1) colleges should use both MMA and SMA and allow students to take college-level courses if either system considers them college-ready and 2) MMA’s treatment effect is weak at an institution where the majority of students are placed into remedial education anyway via either MMA or SMA.
Who Takeshi Yanagiura is currently finishing his Ph.D. in Economics of Education at Teachers College, Columbia University, New York, specializing in Higher Education Policy, Causal Methods, and Machine Learning. He received funding from NSF/AERA and was a visiting scholar at Taisho University, Tokyo. Prior to his academic career, he has been Director of Research at Postsecondary Analytics, Institutional Research Analyst at University of the District of Columbia, and Research Director at Tennessee Higher Education Commission.