A Framework for Analyzing Qualitative and Quantitative Data for Student Evaluation of Teaching

Doctoral Dissertation
Thumbnail

Abstract

Student feedback is essential for teachers to improve their teaching and for schools to evaluate faculty performance, and in turn, helps student learning. The feedback also provides suggestions and information for other students to choose courses to take. Identifying valid, useful and multidimensional information from student feedback with is an important problem to solve. With digital tools, teaching evaluation data are more widely collected and effectively stored. Therefore, researchers are allowed to develop and apply mixed methods to analyze teaching evaluation data. This study aims to develop a framework for analyzing quantitative and qualitative teaching data with aspect-based sentiment analysis, deep learning models, and other state-of-the-art techniques to help researchers, teachers, students, and educators better understand teaching evaluation. With large-scale long-term student evaluation data, this dissertation is also able to discover longitudinal trends and detect potential gender bias of teaching evaluation.

Attributes

Attribute NameValues
Author Wen Qu
Contributor Ross Jacobucci, Committee Member
Contributor Ke-Hai Yuan, Committee Member
Contributor Lijuan Wang, Committee Member
Contributor Zhiyong Zhang, Research Director
Degree Level Doctoral Dissertation
Degree Discipline Psychology, Research and Experimental
Degree Name Doctor of Philosophy
Banner Code
  • PHD-PSYC

Defense Date
  • 2021-07-12

Submission Date 2021-09-07
Record Visibility Public
Content License
  • All rights reserved

Departments and Units
Catalog Record

Digital Object Identifier

doi:10.7274/jh343r10h23

This DOI is the best way to cite this doctoral dissertation.

Files

Please Note: You may encounter a delay before a download begins. Large or infrequently accessed files can take several minutes to retrieve from our archival storage system.