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.