Online Parameter Estimation for Student Evaluation of Teaching

Abstract

Student evaluation of teaching (SET) assesses students’ experiences in a class to evaluate teachers’ performance in class. SET essentially comprises three facets: teaching proficiency, student rating harshness, and item properties. The computerized adaptive testing form of SET with an established item pool has been used in educational environments. However, conventional scoring methods ignore the harshness of students toward teachers and, therefore, are unable to provide a valid assessment. In addition, simultaneously estimating teachers’ teaching proficiency and students’ harshness remains an unaddressed issue in the context of online SET. In the current study, we develop and compare three novel methods—marginal, iterative once, and hybrid approaches—to improve the precision of parameter estimations. A simulation study is conducted to demonstrate that the hybrid method is a promising technique that can substantially outperform traditional methods.