Enhancing Teaching Evaluations Through Campus Data
Bibliographic Data
| ID | 20297220 |
|---|---|
| Authors | Ruizhi Liao (0000-0002-3214-3255, Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence and the Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology, School of Humanities and Social Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China), Zhizhen Chen (0009-0007-7796-8086, School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China), Ao Zhang (0000-0002-6585-5355, School of Management and Economic, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China) |
| Year | 2025 |
| Volume | 68 |
| Issue | 2 |
| Pages | 186-194 |
| Publication date | 2025-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Education (JOURNAL) |
| Journal identifiers | ISSN: 0018-9359 • E-ISSN: 1557-9638 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/te.2025.3536301 |
| OpenAlex | W4407466723 |
| Language | EN |
| References cited | 26 |
Contribution: This study examines the impact of student data and behaviors on student evaluations of teaching. It leverages campus data and employs statistical methods to explore the relationships among these indicators. A regression model is developed that integrates teaching evaluation, expected grades, and course participation, aiming to mitigate instructors’ influence on student evaluations.Background: In higher education, the assessment of teaching quality commonly includes student evaluations of teaching. However, subjective factors, such as students’ expected grades, can distort evaluation outcomes. The ample student behavior data on campus enable an analysis of the validity of student evaluations on teaching.Research Questions: How do student evaluations of teaching correlate with student grades, library borrowing, and dormitory living? How can campus data analysis be utilized to mitigate the influence of instructors on student evaluations of teaching?Methodology: Data collected from campus are utilized, and statistical methods, including the Shapiro-Wilk test and linear regression models, are applied to analyze the relationships between student data and teaching evaluations.Findings: The study finds a strong correlation between students’ expected grades and teaching evaluation scores, suggesting the potential for instructor influence. The proposed regression model highlights the interrelationships among teaching evaluations, expected grades, and course participation, offering insights into mitigating instructor influence on student evaluations
Mathematics education · Multimedia · Computer Science · Evaluation of Teaching Practices · Online and Blended Learning · Psychology
On the Validity of Student Evaluation of Teaching
Course signals at Purdue
Student evaluations of teaching (mostly) do not measure teaching effectiveness
Utilising learning analytics to support study success in higher education
Student Evaluations of Teaching Encourages Poor Teaching and Contributes to Grade Inflation
Validity of students’ evaluations of teaching
Can professors buy better evaluation with lenient grading? The effect of grade inflation on student evaluation of teaching
Is the Sky Falling? Grade Inflation and the Signaling Power of Grades
The Power of Feedback
| Citation velocity | historical |
|---|---|
| Highly cited | No |