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Online Parameter Estimation for Student Evaluation of Teaching

Online Parameter Estimation for Student Evaluation of Teaching 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Psychological Measurement SAGE

Online Parameter Estimation for Student Evaluation of Teaching

Applied Psychological Measurement , Volume 47 (4): 21 – Jun 1, 2023

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References (36)

Publisher
SAGE
Copyright
© The Author(s) 2023
ISSN
0146-6216
eISSN
1552-3497
DOI
10.1177/01466216231165314
Publisher site
See Article on Publisher Site

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.

Journal

Applied Psychological MeasurementSAGE

Published: Jun 1, 2023

Keywords: item response theory; parameter estimation; student evaluation of teaching

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