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Enhancing Teaching Evaluations through Campus Data

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Title: Enhancing Teaching Evaluations through Campus Data
Language: English
Authors: Ruizhi Liao (ORCID 0000-0002-3214-3255), Zhizhen Chen (ORCID 0009-0007-7796-8086), Ao Zhang
Source: IEEE Transactions on Education. 2025 68(2):186-194.
Availability: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=13
Peer Reviewed: Y
Page Count: 9
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: College Students, College Faculty, Student Evaluation of Teacher Performance, Teacher Influence, Grade Prediction, Grades (Scholastic), Expectation, Teacher Student Relationship, Student Participation, Classroom Communication, Student Records, Library Services, On Campus Students, Correlation
DOI: 10.1109/TE.2025.3536301
ISSN: 0018-9359
1557-9638
Abstract: 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1468323
Database: ERIC
FullText Text:
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  Data: Enhancing Teaching Evaluations through Campus Data
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  Data: <searchLink fieldCode="AR" term="%22Ruizhi+Liao%22">Ruizhi Liao</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3214-3255">0000-0002-3214-3255</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zhizhen+Chen%22">Zhizhen Chen</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0007-7796-8086">0009-0007-7796-8086</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ao+Zhang%22">Ao Zhang</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22IEEE+Transactions+on+Education%22"><i>IEEE Transactions on Education</i></searchLink>. 2025 68(2):186-194.
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  Data: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=13
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  Data: 9
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22College+Faculty%22">College Faculty</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Evaluation+of+Teacher+Performance%22">Student Evaluation of Teacher Performance</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Influence%22">Teacher Influence</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+Prediction%22">Grade Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Grades+%28Scholastic%29%22">Grades (Scholastic)</searchLink><br /><searchLink fieldCode="DE" term="%22Expectation%22">Expectation</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Student+Relationship%22">Teacher Student Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Participation%22">Student Participation</searchLink><br /><searchLink fieldCode="DE" term="%22Classroom+Communication%22">Classroom Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Records%22">Student Records</searchLink><br /><searchLink fieldCode="DE" term="%22Library+Services%22">Library Services</searchLink><br /><searchLink fieldCode="DE" term="%22On+Campus+Students%22">On Campus Students</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink>
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  Data: 10.1109/TE.2025.3536301
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  Data: 0018-9359<br />1557-9638
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.
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  Data: 2025
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        Value: 10.1109/TE.2025.3536301
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 186
    Subjects:
      – SubjectFull: College Students
        Type: general
      – SubjectFull: College Faculty
        Type: general
      – SubjectFull: Student Evaluation of Teacher Performance
        Type: general
      – SubjectFull: Teacher Influence
        Type: general
      – SubjectFull: Grade Prediction
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      – SubjectFull: Grades (Scholastic)
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      – SubjectFull: Expectation
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      – SubjectFull: Teacher Student Relationship
        Type: general
      – SubjectFull: Student Participation
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      – SubjectFull: Classroom Communication
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      – SubjectFull: Student Records
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      – SubjectFull: Library Services
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      – SubjectFull: On Campus Students
        Type: general
      – SubjectFull: Correlation
        Type: general
    Titles:
      – TitleFull: Enhancing Teaching Evaluations through Campus Data
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            NameFull: Zhizhen Chen
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            NameFull: Ao Zhang
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