Student-centered instructional policies are associated with more positive student perceptions of their instructor's universality beliefs, but these perceptions favor White instructors in STEM

Student-centered instructional policies are associated with more positive student perceptions of their instructor's universality beliefs, but these perceptions favor White instructors in STEM | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Student-centered instructional policies are associated with more positive student perceptions of their instructor's universality beliefs, but these perceptions favor White instructors in STEM Ronia Kattoum, Cole Dwyer, Mark Baillie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7190396/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The way students perceive their instructor's mindset has been linked to student outcomes, such as engagement and performance, in STEM courses. However, the factors that influence such perceptions are not yet understood, particularly in relation to the instructor’s teaching policies and demographic characteristics. To address this gap, we investigate how student perceptions of their instructors’ universality beliefs (the belief that all students or only some students can reach their full potential in STEM) about student abilities vary based on instructional policies while considering student and instructor demographic factors. Results Student perceptions of the instructor's universal and non-universal beliefs about student abilities were collected using a portion of the Undergraduate Lay Theories of Abilities (ULTrA) survey (n = 625). Teaching policies were characterized by adapting a rubric to assess the student-centeredness of instructors’ syllabi of 24 STEM instructors (34 courses) in a demographically diverse research institution in the Southern United States. Our findings indicate that using more student-centered instructional policies in evaluating and assessing students is associated with a more positive perception of the instructor’s universality beliefs. However, when instructor demographics are introduced in the model, that association between instructors’ policies and student perceptions is lost, with White instructors being perceived more positively. Conclusions Our findings indicate that student-centered instructional policies are associated with more positive perceptions of instructors' universal beliefs. Thus, adopting student-centered policies, particularly in evaluation and assessment, is a potential mechanism to enhance student perceptions of the learning environment, thereby increasing retention in STEM courses. In addition, our work identifies possible biases in student perceptions of the learning environment that extend beyond the instructors’ adoption of student-centered policies, specifically in relation to instructor demographic identity. Instructional policies are no longer significant when the instructor's race is introduced in our model. White instructors are perceived more positively than their Black Indigenous People of Color (BIPOC) peers. Thus, other contextual factors, such as verbal and non-verbal cues, cultural differences among faculty, or built-in systemic inequities, may contribute to shaping student perceptions and should be explored further. Students’ perceptions of instructor mindset lay theories universal and non-universal beliefs ULTrA survey undergraduate STEM instructional policies student-centered syllabi syllabus rubrics Introduction Students' lay theories of ability, beliefs about whether intelligence is innate (fixed mindset) or malleable (growth mindset), have been associated with student outcomes (Dweck & Leggett, 1988 ). When students believe that their intelligence is fixed, this correlates with increased helplessness and lower grades (De Castella & Byrne, 2015 ). Students who believe their intelligence is malleable show an upward trajectory in their math grades over a two-year period in junior high school, whereas those who believe their intelligence is fixed show no such growth in their grades (Blackwell et al., 2007 ). Perhaps the mechanism by which student mindset beliefs influence outcomes can be explained by recent work that shows that when students believe their intelligence can grow, they choose more effective study strategies that may be more error-prone because they are more comfortable with seeing error as part of the learning process (Choubalova et al., 2024). Women developed a disparity in how they viewed their “Ability” over time in a physics course as compared to men, which negatively impacted their course grades (Malespina et al., 2022 ). While students' theory about their intelligence is an important factor in their motivation and performance, an essential component of the mindset context is students’ perception of the instructor’s mindset (Muenks, Yan, & Telang, 2021 ). Even after controlling for students’ mindsets, if students perceive the instructor to endorse a more fixed mindset, this is associated with lower motivation and a sense of belonging in engineering courses (Muenks, Yan, & Telang, 2021 ). In addition, those who perceive the instructor as endorsing a more fixed mindset experience greater academic misfit, leading to lower chemistry grades (Kattoum et al., 2024) and a lower sense of belonging, which in turn leads to underperformance among women in STEM (Canning et al., 2021 ). Additionally, when students perceive that the instructor believes only some students can succeed in STEM, this leads to downstream effects on their own growth beliefs, resulting in less classroom comfort in contributing to discussions and lower grades, especially for traditional-age students (18–24 years old) (Kattoum and Baillie, 2025 ). These findings align with Social Cognitive Theory (SCT), which posits that learning occurs within a social system in which the environment reciprocally influences both the student's affective states (attitudes and feelings) and behaviors (engagement and performance). Thus, students are influenced by what the instructor models. This raises the question of how instructional policies affect student perceptions of their instructors’ lay theories of ability, particularly if these perceptions may perpetuate disparities in student representation of historically marginalized groups in STEM compared to the majority group (Canning et al., 2021 ). Prior work has started investigating how the learning environment may impact student perceptions. For example, instructors who provide opportunities for practice and feedback, explicitly emphasize student progress (rather than performance), value student learning and development, and provide constructive feedback in response to poor performance in STEM classrooms are perceived to endorse more of a growth mindset by their students (Kroeper, Fried, et al., 2022; Kroeper, Muenks, et al., 2022 ). Another study found that students perceived instructors as having a more growth mindset when they employed “elaborative learning practices” such as interactive group work and project-based instruction in science classrooms (Muenks, Yan, Woodward, et al., 2021). Additionally, practices that communicate a growth mindset include focusing on processes, normalizing mistakes, encouraging students to contribute knowledge, and providing opportunities for feedback in the assessment process (Sun, 2018 , 2019 ). While insightful, these studies have been primarily based on student perceptions of the instructor's teaching practices for the general science faculty, not necessarily on what policies are implemented in a college STEM classroom for specific instructors (Kroeper, Muenks, et al., 2022 ; Muenks, Yan, Woodward, et al., 2021). Although others have conducted teaching observations and interviews to determine the teaching practices that convey mindset messages in mathematics education, these studies have primarily been derived from the K-12 setting (Sun, 2018 , 2019 ). Lastly, the impact of instructor demographics has not been examined in conjunction with instructional policies to determine how they influence student perceptions. Our work builds upon previous studies by utilizing syllabi as artifacts of instructional policies (a proxy for instructional practices), which we hypothesize may influence students' perceptions of their instructor’s lay theories of abilities. Human Lay Theories of Abilities The Undergraduate Lay Theories of Abilities (ULTrA) survey offers nuanced insights into lay theories of human ability specific to STEM, providing greater depth in understanding students' perceptions of the learning environment (Limeri et al., 2023 ). The ULTrA characterized human lay theories about ability as separate but related constructs of mindset, universality, and brilliance beliefs. Mindset beliefs reflect whether students believe they can grow their intelligence through effort or perceive their intelligence as a fixed trait that cannot be developed, a concept similar to Dweck’s ( 1988 ) mindset theory. For example, a student with a growth belief might agree, "I could improve my intellectual abilities to the same level as successful STEM professionals." Universality beliefs (universal and non-universal beliefs) refer to an individual’s beliefs about who has the potential to reach excellence in a given field (Limeri et al., 2023 ). A student who endorses universal beliefs may align with the thought that all students can reach maximum potential: “Anyone could become effective at learning as highly successful STEM students.” On the other hand, students who endorse non-universal beliefs may perceive that only certain students can reach the highest level of potential: “Even if they try, some people could never become as effective at analyzing information as their peers.” A student can have a growth mindset (believing they can improve their STEM abilities) but hold non-universal beliefs (believing they can never master a concept in science class, no matter how much they study). Lastly, brilliance's beliefs gauge whether students perceive that specific fields require raw talent: “Becoming a top student in STEM requires an innate talent that just cannot be taught.” This distinction is separate from students' beliefs about whether they can develop their abilities (growth beliefs) or whether there is a distribution of abilities among students (universalism beliefs). For example, a student may believe they can improve their math intelligence (growth beliefs) and master any concept in math class if they try (universal beliefs), but believe that only those who are born brilliant can become top mathematicians (brilliance beliefs). All three constructs of lay theories of human ability, relating to how students perceive their own abilities, have been linked to student success. This work focuses on student perceptions of their instructors’ beliefs about who can succeed in STEM (i.e., universality beliefs). This is particularly interesting to the study because of the reputation of STEM disciplines as exclusive to certain people. If an instructor believes that all students can achieve excellence in STEM, this may be reflected in their course policies, such as providing students with multiple opportunities to succeed in an assignment. If they believe only some will be able to “make it” through the STEM pipeline, they may structure their course to emphasize performance over process and effort (i.e., only a midterm and final exam count toward course grades) to “weed out” students who are not successful. While we acknowledge that students’ perceptions of their instructors’ mindsets and brilliance beliefs may also be related to their instructional policies, we hypothesize that students may pick up on cues from the instructors’ policies that signal they believe STEM disciplines are exclusive to certain people. This would be particularly important for students from marginalized communities who may be more vulnerable to stereotype threats based on contextual cues from the classroom environment. Instructional policies: inferred from syllabi While syllabi traditionally serve as policy-based contracts, permanent records reflecting course content, and guides to learning strategies (Parkes & Harris, 2002 ), they also reflect the teaching approaches for instructors. Prior work has shown that students from all backgrounds form their initial impressions of an instructor’s competency and approachability based on their syllabi, as well as their decision to take another course with the instructor (Jenkins et al., 2014 ; Merchán Tamayo et al., 2022 ; Saville et al., 2010 ). Relatively small changes to course syllabi, such as the use of ‘we’ over ‘I’ (Baecker, 1998 ) and statements of mental health support (Gurung & Galardi, 2021 ), can have a significant improvement on student perceptions (Perrine et al., 1995 ). Student perceptions of their instructors’ universality beliefs approach may be related to the policies they adopt and describe in their syllabi. We hypothesize that instructors who utilize more student-centered policies in their syllabi may be more likely to be perceived by students as endorsing universal beliefs and fewer non-universal beliefs about their abilities. Previous researchers have developed syllabus scoring rubrics to assess the student-centeredness of instructor syllabi (Cullen & Harris, 2009 ; Richmond, 2022 ; Richmond et al., 2019 ) and as a guide for instructors seeking to effectively improve their course design through their syllabi (Merrill, 2020 ; Slattery & Carlson, 2005 ). Various disciplines, including business (Rich, 2023) and psychology (Richmond et al., 2019 ), have applied these rubrics to evaluate teaching policies. Cullen and Harris identified three primary factors in assessing the student-centeredness of syllabi: community, power and control, and evaluation and assessment. The community factor gauges the instructors’ accessibility and the opportunities they provide for collaboration within their course(s). The power and control factor builds upon the idea that students have more positive course outcomes when they feel autonomous (Merchán Tamayo et al., 2022 ). Thus, this factor examines evidence of shared power between the teacher and students through the manner and tone of the syllabus, as well as the granting of students some choice in policy and assessment. The final factors, evaluation and assessment, uncover the instructor's techniques for evaluating student performance. Does the instructor employ iterative assessment techniques to monitor student progress with constructive feedback opportunities or rely heavily on formal summative assessment as the primary mode of student evaluation? Previous studies have found that professors typically score higher in the community-based factor but lower in evaluation and assessment, such as providing opportunities to revise assignments and offering learning rationales tied to outcomes (Cullen & Harris, 2009 ; Richmond et al., 2019 ). Students have been found to prefer longer, more detailed syllabi (Harrington & Gabert-Quillen, 2015 ). Additionally, students held more positive perceptions of hypothetical instructors whose syllabi were rated as more learner/student-centered (Richmond et al., 2016 ). For our study, we modified the Cullen and Harris ( 2009 ) rubric to assess the student-centeredness of faculty syllabi within STEM disciplines. We hypothesize that instructional policies, inferred by the research team from the syllabi, may be correlated with student perceptions of the instructor's universal and non-universal beliefs. Student and Instructor Demographic Factors We recognize existing systemic inequities that contribute to disparities in the representation of people from historically marginalized groups such as Black Indigenous People of Color (BIPOC: Black, Native American, Native Hawaiian, Alaskan Native, Asian, Pacific Islander, Hispanic/Latino/a/e, other, multiracial), women, first-generation college students, and nontraditional-age student (> 22 years old) in US higher education, particularly in STEM. Thus, to examine and highlight the narrative of those marginalized groups, we assess if student demographic characteristics moderate the relationship between instructional policies and student perceptions within the undergraduate STEM context at a metropolitan research institution with a diverse student population. We hypothesize that more student-centered instructional policies focusing on growth and development may benefit these student populations and increase their participation in STEM fields. Additionally, we explore whether faculty demographic characteristics (race and gender) influence student perceptions and how these factors interact with their instructional practices. Previous work has shown that faculty with marginalized identities (BIPOC and women) are subject to more biased student teaching evaluations (Kreitzer & Sweet-Cushman, 2022). However, no work has explored how students' perceptions of their instructor's mindset are influenced by faculty demographic factors, particularly in tandem with their instructional policies. Awareness of such biases (if they exist) can lead to more robust conclusions and discoveries in mindset research by accounting for the instructors’ demographics. Current Study This study builds on the current literature by addressing the following research questions: RQ1) How do student perceptions of their instructors’ universality beliefs vary based on instructional policies (coded from instructor syllabi) while controlling for their course grades? RQ2) How do student demographic factors (race, gender, age group, generational status) play a role in determining student perceptions of the instructors’ universality beliefs, and how does that interact with their instructional policies and student grades? RQ3) How do the instructors’ demographic characteristics (race and gender) play a role in student perceptions of the instructor’s universality beliefs, and how does that interact with their instructional policies? Methods Sampling Researchers recruited full-time faculty members in STEM disciplines who taught undergraduate courses in a southern United States metropolitan institution by visiting department meetings. Twenty-four faculty members (whom we will refer to as instructors, although they may have various ranks) consented to participate in the research project during the 2022–2023 academic year, with IRB approval. Four of the twenty-four faculty participants had fewer than five student responses and were excluded from the analysis to protect student identities, resulting in twenty instructor participants. Instructors did not have an incentive for participation. Academic ranks included two full professors, two associate professors, seven assistant professors, eight full-time lecturers/instructors, and one visiting instructor. Four instructors were tenured, seven were untenured but on the tenure track, and nine were untenured and not on the tenure track. Instructors came from eight departments: Anthropology, Biology, Chemistry, Information Science, Mathematics and Statistics, Mechanical Engineering, Physics and Astronomy, and Public Affairs. Thirteen instructors identified as White, and seven identified as BIPOC. Thirteen instructors identified as men and seven as women. Teaching experience ranged from 1 to 35 years, with an average of 12.6 years (SD = 7.7). Teaching responsibilities ranged from 10–85%, averaging 59.5% (SD = 19.8). Instructors were asked to identify courses from which the research team could collect syllabi, student consent forms, and data. All students in those courses were recruited to participate in the study. The research team collected student consent and questionnaires during the first or last ten minutes of one of their classes or labs in the 12th or 13th week of a 14-week semester. Regardless of whether they agreed to participate in the study, students who completed the consent form were entered into a raffle to win one of thirty $ 20 gift cards. A QR code was used to direct students to a Qualtrics survey administered in class. The Qualtrics survey contained instruments that assessed students' perceptions of the instructor’s universal beliefs, self-identified demographic factors, and other factors that were part of a more extensive study. Nine of the twenty instructors taught multiple courses, and student surveys were collected from all of their courses. If the same student took the survey for multiple instructors, they were treated as distinct data points for each instructor and retained for analysis, as they were asked to reflect on a learning environment specific to that instructor and course. Response rates from the thirty-four courses across eight departments ranged from 38–90% (n = 5 to n = 36) for 625 student participants. Listwise deletions resulted in 480 student responses with complete cases, as summarized in Table 1 for the final dataset. Students identified as Black, Native American, Native Hawaiian, Alaskan Native, Asian, Pacific Islander, Hispanic/Latino/a/e, other, or multiracial were categorized as BIPOC. Those who chose not to identify demographic characteristics were removed from the analysis. Students who identified as non-binary/third gender constituted less than 2% of the sample size. Thus, they were excluded from the quantitative comparison due to the relatively small sample size, which makes it difficult to draw any inferences from the comparison. Measures Student perceptions of instructor universality beliefs For the scope of this study and to minimize survey fatigue (as this data is a subset of data collected for a more extensive study), we chose to utilize only the universality beliefs (universal and non-universal beliefs) scale from the ULTrA survey and adapt it to assess student perceptions of what they think the instructor believes about student abilities. For example, we modified the original item, “Even if they try, some people could never become as effective at analyzing information as their peers,” to gauge the student perception of instructor beliefs about students: “ The professor in this course seems to believe that even if they try, some students could never become as effective at analyzing information as their peers.” The process was repeated for all ten universality items (see Supporting Information, Table S1 ). Student questionnaires were collected toward the end of the semester in Qualtrics with an option to answer on a scale of 1–6 regarding how much they agreed with those ten statements (1- strongly disagree, 2- disagree, 3- somewhat disagree, 4- somewhat agree, 5- agree, 6- strongly agree, prefer not to answer). A higher score indicated more alignment with universal and non-universal beliefs. Because the original instrument was modified slightly, a confirmatory factor analysis (CFA) was conducted to assess the instrument's validity, and Cronbach Alpha was used to assess the reliability of the survey items. Because of the non-normal distribution for universality beliefs data (through visual QQ plots), an initial CFA was conducted based on the factor structure of the ULTrA survey using a robust maximum likelihood approximation (vs. maximum likelihood approximation for normally distributed data) and full information maximum likelihood (FIML) for missing data. Results revealed acceptable model fit (Robust CFI/TLI = 00.97/00.96, Robust RMSEA = 00.083 (90% confidence interval: 00.067-00.100), SRMR = 00.053) on three of the four parameters (threshold of acceptability: CFI/TLI > 00.95, RSMEA < 00.08, SRMR < 00.06) with one item loading poorly on the non-universal beliefs scale (0.28). Further inspection of the data revealed an inconsistency in wording that may explain the poor loading of the item. The original item read, “Only people with a natural talent can become excellent at analyzing information,” which was modified to “ The professor in this course seems to believe that students with a natural talent can become excellent at analyzing information.” Leaving out “only” changed the statement's meaning (most likely due to a transcription error). There is no reason to believe that talented students will not do well in analyzing information, but believing that only talented students will do well indicates endorsement of non-universal beliefs. This item was removed from further analysis because of the possibility of different meanings from this item (as was apparent in the factor loadings of the CFA). A CFA on the revised survey indicated an excellent model fit, with all model parameters meeting the threshold of acceptability. (Robust CFI/TLI = 0.98/0.97, Robust RMSEA = 0.078 (90% confidence interval: 0.058–0.099), SRMR = 0.018) with item loadings ranging from 0.71–0.94). Universal beliefs (5 items) and non-universal beliefs (4 items) subscales showed excellent internal consistency with Cronbach Alpha of 0.94 and 0.84, respectively. Because this work aimed to explore how student perceptions relate to instructional policies and demographic factors, it is prudent to conduct measurement invariance testing to determine if different groups of students interpret questionnaires differently. If measurement invariance holds, then both groups (e.g., men and women) interpret questions similarly, and any differences found between groups can be attributed to actual differences in the outcome variables. For our study sample, measurement invariance held for all demographic groups tested (race, gender, age group, and generational status), with details outlined in the Supporting Information section. Student-centeredness of instructional policies as identified from the syllabus Twenty-four instructors contributed syllabi from one or more of their courses, resulting in thirty-four STEM syllabi. The scoring rubric developed by Cullen and Harris ( 2009 ) served as the basis for our deductive coding, which utilized the three primary themes they identified: community, power and control, and evaluation and assessment, each comprising 4–5 items (See Supporting Information, Table 3 ). Raters included an undergraduate research assistant (CD) and a Ph.D. candidate (RNK) who is also a college-level chemistry instructor. While coding, the researchers were unaware of students’ responses to the mindset questionnaire to minimize bias. To start, the research team randomly selected three syllabi. Each rater worked independently to assign a score between 0 and 3 for each item through deductive coding based on the original Cullen and Harris ( 2009 ) rubric. The scoring scheme was modified using inductive coding to reflect the nuances discovered during the scoring of the first three syllabi. New items that were not reflected in the original rubric were added as they emerged. Researchers kept notes for each score, along with justification, in a spreadsheet that included evidence from the syllabi to support the reason for that score (see the scoring rubric and sample evidence from the syllabi in the Supporting Information). After rating, researchers met to compare their scores, resolve any discrepancies, and discuss any emerging issues until a consensus was reached. These discussions highlighted the unique perspectives of researchers from different backgrounds, including students and instructors, when coding syllabi. For example, university core objectives may have been identified by the instructor as a mandatory addition that may or may not align with the instructor’s pedagogy. Alternatively, a student may be more apt to interpret the tone of a syllabus than an instructor, having first-hand experience as a student taking similar courses. The variation in the raters' perspectives reduces the potential bias in final coding scores, resulting in more accurate coding. Another round of three syllabi was randomly selected for scoring based on the modified rubric. If new items emerged or scoring criteria were refined, the researchers recoded the previous syllabi with the new refined scoring scheme. After the third round (9 total syllabi), the research team agreed on the scoring scheme and items in the modified rubric. The remaining syllabi were scored with the finalized modified rubric version, with researchers meeting regularly to discuss and resolve any discrepancies in coding until a consensus was reached. Instructors who taught multiple courses generally had the same syllabus structure and thus had similar syllabi scores for separate courses. A summary of the criteria for the three factors is provided below, with additional details included in the Supporting Information section. A student-centered classroom community is one in which students can engage with the instructor and other students. A clear rationale is provided for assignments where students’ presence (or lack thereof) would be noticed and recognized. On the other hand, a teacher-centered classroom community generally lacks opportunities for collaboration, has limited access to the instructor, lacks an explicit rationale for assignments, and lacks a system for monitoring students’ attendance, as indicated by the syllabus. Four items were used to gauge the community aspect of the instructors’ course(s) as inferred from their syllabi. “Accessibility of teacher '' ranged from the instructor not prescribing office hours to providing prescribed office hours and appointment options using multiple modalities (virtual and in-person). Our modified version of the rubric did not include 'fax' and 'home phone' as forms of communication because they were not present in any of the syllabi coded. The “learning rationale” item assessed whether instructors provided a rationale for assignments (linked to learning objectives) rather than simply listing them. Scoring criteria in the “collaboration” item were refined to encompass opportunities and requirements for collaboration, ranging from no opportunities to required group work that encouraged students to learn from one another both inside and outside the classroom. The phrase “discourages interaction except in class or for emergency” was modified to “only one form of communication provided,” as none of the syllabi explicitly discouraged interaction. Lastly, the “attendance policy” item was added under the Community factor because many syllabi specified attendance policies, but it was not included in the original rubric. Mandatory attendance has been shown to have a positive correlation with student performance (Sund & Bignoux, 2018 ), and some instructors have argued that it is essential for promoting active learning (Higbee & Fayon, 2006 ). As attendance policies are often unpopular with students, instructors can incorporate components that help foster student rapport, such as acknowledging students' important role in the classroom through their presence (Sybing, 2019 ). Thus, the criteria ranged from no attendance policy specified to required attendance that makes students feel part of the learning environment, fostering a sense of community. Regarding the power and control factor, a teacher-centered syllabus generally reflects an authoritative and punitive classroom culture, in which the instructor positions themselves as the sole source of knowledge and the student’s role is limited to receiving it. A more student-centered syllabus would reveal elements of shared power between the instructor and student. That would entail a classroom culture where students have more autonomy, are encouraged to bring their knowledge to the class, and reference multiple avenues (besides the instructor) to develop their skills. Five items gauged the power and control factor. The “teacher’s role” determined the extent to which the instructor provided options for students to participate in their own education (e.g., choose a topic for a project rather than assign a topic). The “student role” measured the students’ expected contribution to the class, ranging from being a passive participant to contributing to the learning community. “Outside resources” determined whether instructors provided external homework platforms, notes, video links to content/class recordings, tutoring, and academic support services or positioned themselves as the only resource for students. We added the item “syllabus tone,” adapted from work by Chen et al. ( 2023 ) based on observations from the sample syllabi. The syllabus's tone is used “...to capture the positive, encouraging, and collaborative language employed in the syllabi corpus” (Chen et al., 2023 ). Under this item, criteria ranged from an entirely punitive syllabus tone to a positive and encouraging one that fosters student teamwork. Finally, “syllabus focus” assessed if the syllabi were primarily written to communicate policies and procedures (like a legal document) or were centered around student learning. The evaluation and assessment factor identified a teacher-centered syllabus as one with limited opportunities for feedback and revision, where the instructor relies heavily on student summative assessments as the primary mode of evaluation. A student-centered syllabus generally reflected an instructor who evaluated students based on a broad range of assignments with regular feedback and opportunities for revision. Five items determined evaluation and assessment. Criteria under “grades” were expanded from the original rubric to account for the weight of assignments (e.g., the relative weight of summative and formative assessments) and how frequently formative assessments were administered. An instructor may use various feedback and evaluation techniques, but still primarily assigns grades based on summative exams or uses assignments to accumulate points (such as extra credit and participation points) rather than as learning opportunities. The “feedback mechanisms'' item was refined slightly to include feedback mechanisms via homework and student response systems, such as iClicker, since they were more prevalent in the syllabi coded than written work. Criteria ranged from one form of feedback mechanism (exams) to a more scaffolded feedback mechanism (homework and/or quizzes) and real-time feedback in class. This item aimed to determine the extent to which instructors utilized feedback mechanisms, not how they were accounted for in student grades. The “evaluation” item focused on how students' learning was assessed, with criteria ranging from using only summative assessments to a more diverse portfolio of student work that also included formative, collaborative, and generative assessments, common assessment types in STEM that may indicate a deeper level of engagement with the material (Fiorella & Mayer, 2016 ). The “learning outcomes” item determined how assignments were tied to assessing learning outcomes. Lastly, “revision/redoing” assessed the extent to which instructors gave opportunities for learning and growth without penalizing students. The range of scores was 0–3 for each item, with a score of 0 indicating teacher-centered policies and a score of 3 indicating student-centered policies. Regarding the community factor, the research team considered four items, each with a maximum score of three, for a total of twelve points. If the researchers scored the rubric on the community factor as 9/12 (75%), then the instructor was identified as leaning more towards student-centered policies when establishing a community in their course. Descriptive statistics of the scoring rubric for the three factors of syllabi coding are presented in Table 2 . Table 2 Descriptive statistics of instructional policies coded from the instructor syllabi by the research team Factor Mean SD Skew Kurtosis Shapiro-Wilks sig Community 50.41 20.85 -0.53 -0.62 0.31 Power & Control 43.67 19.16 -0.40 -0.62 0.27 Evaluation and Assessment 46.67 25.13 -0.11 -1.00 0.86 The Shapiro-Wilks Normality Test (SWN) was insignificant (p > 0.05), and skew and kurtosis are within the + 2 range, suggesting normality in the data distribution. The average factor scores ranged from ~ 43% to ~ 50%, with instructors scoring the lowest in power and control and the highest in establishing community. Although the averages were generally in the middle of the spectrum between teacher-centered and student-centered, scores among instructors varied considerably (SD ranged from ~ 18 to 25). The Shapiro-Wilk normality (SWN) test (used to assess normality for n 0.05, and skew and kurtosis were within the ± 2 range). While building community and sharing power and control with students in the course can influence student perceptions of the instructor’s universal beliefs, we hypothesize that evaluation and assessment may be more linked to the outcome variable. How faculty evaluate students may be more directly related to their universality beliefs about student abilities that students may pick up on. Thus, we assess how each syllabus factor influences student perceptions to uncover a more nuanced understanding of which policies influence student perceptions. The Variance Inflation Factor (VIF) values for the community (1.18), power and control (1.93), and evaluation and assessment (1.88) indicate no significant multicollinearity (all values are well below the threshold of 5), justifying our decision to examine each factor individually. Course grades Grades were retrieved from instructors at the end of the semester for consenting students only. Course grades were used as a control variable to isolate the impact of instructional policies on student perceptions, particularly at the end of the semester when students are most likely to be aware of their standing in the course. Method of Analysis Our sample set included twenty instructors, some of whom taught multiple courses, each with a corresponding syllabus score. On the other hand, there were multiple student responses per instructor and course. Thus, we must account for the data's hierarchical (nested) structure to assess how the instructor's policies, as reflected in the syllabus score, shape student perceptions. That is, student responses may not be entirely independent, but influenced by shared factors related to their instructor or the course. To account for this, we considered multilevel modeling (hierarchical linear modeling), in which we used random intercepts for instructors and courses to account for variability between instructors and courses using the lme4 in Rstudio. The model's random-effects analysis showed negligible variance attributed to differences between instructors (non-universal beliefs variance = 0.000, SD = 0.00; universal beliefs variance = 0.0058, SD = 0.076), suggesting that instructor-level factors did not substantially contribute to variability in student perceptions. In contrast, the residual variance was substantial at the student level (non-universal beliefs variance = 1.47, SD = 1.21; universal beliefs variance = 0.68, SD = 0.82), indicating that most variability in student perceptions occurred at the individual student level, rather than the instructor level. The random-effects analysis also revealed that the variance attributed to differences between courses was relatively low (non-universal variance = 0.030, SD = 0.174; universal variance = 0.0061, SD = 0.08), suggesting minimal variability in students’ perceptions of instructors’ non-universal beliefs across courses. In contrast, the residual variance was substantial with non-universal beliefs variance (SD = 1.19) and universal beliefs variance (SD = 0.82), indicating that most variability in student perceptions is attributed to individual student differences rather than course-level factors. Thus, it was determined that fixed effects multiple linear regression modeling should be used for all analyses and interactions. Visualization methods, such as scatterplots, histograms, and QQ plots, were used to examine the linearity and normality of the data. There were general linear trends between the dependent variables and the outcome variable. However, QQ plots indicated a non-normal distribution of the outcome variables (students' perceptions of the instructors' universality beliefs). Thus, we used bootstrapping to generate empirical confidence intervals and p-values, without relying on the data's normality, using the boot package in RStudio for the fixed-effects multiple regression models. Results and Discussion RQ1) Instructional Policies → Perceptions of the Instructors’ Universality Beliefs A fixed-effects model was employed to investigate the relationship between instructional policies (coded from the syllabus) and students' perceptions of the instructors' universal beliefs while controlling for students' course grades. A nonparametric bootstrapping technique with 1,000 iterations was utilized to address the non-normal distribution of residuals. Results are summarized in Table 3, along with respective estimates (effect sizes), standard errors (SEs), and lower and upper confidence intervals (LCIs and UCIs) for each variable. LCI and UCI form a 95% confidence interval (in this case, bias-corrected and accelerated to account for non-normal distribution), indicating the range within which the true value is expected to fall with high probability. We examined both universal beliefs and non-universal beliefs because students’ endorsement of more universal beliefs may not necessarily indicate that they endorse fewer non-universal beliefs, as these are two separate constructs and not necessarily inversely related. Table 3 Model 1: The impact of instructional policies (coded from the instructors’ syllabi) on student perceptions of the instructors’ universality beliefs Syllabus Factor Community Power and control Evaluation and assessment Variable Est. SE LCI UCI Est. SE LCI UCI Est. SE LCI UCI Non-univ. belief Intercept 3.73 .467 2.92 4.78 3.88 .435 3.05 4.83 3.88 .444 3.07 4.90 Instruct. policy − .003 .003 − .008 .002 − .009 .004 − .017 − .002 − .007 .003 − .012 − .001 Course grade − .018 .005 − .029 − .010 − .016 .005 − .027 − .008 − .017 .005 − .028 − .009 Univer. belief Intercept 3.81 0.364 3.14 4.56 3.64 .335 2.99 4.31 3.60 .342 2.92 4.28 Instruct. policy -2.20 0.002 − .004 .003 .004 .003 − .001 .009 .004 .002 .004 .008 Course grade 1.76 0.004 .010 .025 .017 .004 .009 .024 .018 .004 .009 .024 95% bias-corrected and accelerated (BCa) lower and upper confidence intervals (LCI & UCI). Significant CIs (do not include zero) are bolded. As hypothesized, more student-centered evaluation and assessment policies are associated with more positive perceptions of the instructors’ universality beliefs, characterized by lower non-universal and greater universal beliefs (although the effect size is small, Table 3). These evaluative techniques include more collaborative group work, multiple opportunities for feedback through frequent, lower-stakes homework assignments, quizzes, and live polling, as well as opportunities to revise work that focuses on achieving learning outcomes (see SI for the syllabus and rubric). These findings are consistent with previous literature that has shown students infer their instructor’s mindset more positively from teaching behaviors that involve more opportunities for practice and feedback(Kroeper, Fried, et al., 2022; Kroeper, Muenks, et al., 2022) and active/collaborative learning (Muenks, Yan, Woodward, et al., 2021). Syllabi that communicated more shared power and control with students revealed mixed findings. They were associated with lower student perceptions that their instructor endorsed non-universal beliefs (although the effect size is small, Table 3). However, they do not seem to impact student perceptions of their instructor’s universal beliefs (Table 3). Such policies include increasing student autonomy in course assignments and grading and focusing less on procedural and contractual policies and more on student learning. Thus, when the instructor places more effort into developing a syllabus that empowers student voices, this could indicate that the instructor is more mindful of student learning, resulting in students perceiving their instructors as less likely to endorse the belief that STEM is exclusive to the few. As expected, syllabus policies that foster community were not significantly related to students' perceptions of the instructor's universal beliefs (Table 3). Providing multiple modalities for assisting students, creating a space where attendance is encouraged, and offering opportunities for collaboration may be more closely related to students' sense of belonging within a community (Rattan et al., 2018), rather than how they perceive their instructor’s beliefs about student ability. Higher course grades are consistently linked to more positive perceptions of instructor beliefs about student abilities, as indicated by lower perceptions of non-universal beliefs and greater perceptions of universal beliefs (Table 3). In this work, we use course grades as control variables to isolate the impact of instructional policies on student perceptions, primarily because student surveys were collected at the end of the semester when students were most likely to be aware of their course outcomes. However, it could very well be that student perceptions of the instructor’s universality beliefs are impacting their course grades (rather than their course grades impacting their perceptions), as prior work in controlled lab settings has shown(Canning et al., 2019, 2021; Muenks et al., 2020). In a native learning environment and within a demographically diverse institution, reciprocity may exist between the learning environment and student performance, making the directionality of the relationship more challenging to determine. RQ2) Instructional Policies + Students Demographics → Student Perceptions Next, we investigated whether student demographic factors, in addition to instructional policies and student grades, influenced their perceptions of the instructor's non-universal and universal beliefs through a bootstrap analysis. The results are presented in Table 4. Table 4 Model 2: The impact of instructional policies (coded from the instructors’ syllabi) on student perceptions of the instructor's universality beliefs, accounting for student demographic factors Syllabus Factor Community Power and control Evaluation and assessment Variable Est. SE LCI UCI Est. SE LCI UCI Est. SE LCI UCI Non-univ. belief Intercept 3.87 .501 2.91 5.01 3.99 .461 3.14 5.01 4.03 .479 3.17 5.12 Instruct. policy − .002 .003 − .008 .002 − .009 .004 − .016 − .001 − .007 .003 − .013 − .001 Course grade − .018 .005 − .029 − .008 − .016 .005 − .027 − .007 − .017 .005 − .028 − .008 Race (White) − .258 .117 − .497 − .037 − .246 .116 − .489 − .028 − .269 .117 − .501 − .043 Gender (Woman) − .011 .113 − .200 .238 .030 .113 − .182 .262 .013 .112 − .193 .254 Age group (Trad) − .087 .120 − .345 .122 − .071 .120 − .342 .141 − .057 .119 − .327 .150 Gen. status (FG) − .043 .111 − .249 .190 − .039 .110 − .242 .193 − .056 .110 − .254 .182 Univer. belief Intercept 3.80 .388 3.10 4.66 3.64 .350 2.99 4.37 3.58 .353 2.88 4.33 Instruct. policy .001 .002 − .004 .003 .004 .003 − .001 .010 .004 .002 − .001 .008 Course grade .018 .004 .010 .024 .017 .004 .010 .024 .018 .004 .010 .024 Race (White) − .044 .077 − .183 .115 − .050 .076 − .190 .102 − .039 .076 − .183 .116 Gender (Woman) − .011 .077 − .157 .139 − .015 .077 − .159 .136 − .006 .076 − .149 .144 Age group (Trad) .006 .080 − .146 .166 − .006 .081 − .163 .154 − .015 .081 − .169 .142 Gen. status (FG) .035 .081 − .137 .179 − .037 .079 − .128 .176 .047 .079 − .114 .191 95% bias-corrected and accelerated (BCa) lower and upper confidence intervals. Significant CIs (do not include zero) are in bold. When introducing student demographics into the model, we observed similar trends to those in our initial model, regarding the impact of student grades and instructional policies on student outcomes. The more student-centered evaluation and assessment, the lower the perceptions of the instructor’s non-universal beliefs (more positive). However, evaluation and assessment did not impact student perceptions of the instructor’s universal beliefs about student abilities (as we found in Model 1). Additionally, policies emphasizing shared power and control were associated with lower non-universal belief (although with a small effect size), which was not the case for our first model. However, the power and control factor was not associated with student perceptions of the instructor’s universal beliefs (consistent with Model 1). This suggests that the faculty level of shared power with students may influence student perceptions and warrants further exploration with multiple instructors and institutions. The community factor did not predict student perception of the instructor’s universal or non-universal beliefs, while course grades consistently did so (consistent with Model 1). We did not detect differences in student perceptions of the instructor’s universality beliefs based on gender, age, and generational status. Interestingly, White students had significantly more positive perceptions of instructors’ beliefs (lower non-universal belief scores) than BIPOC students. This was consistent in all three syllabi factors (Table 4), although no differences were detected for universal beliefs. To explore the reason for this trend, we conducted a series of interaction effects to assess whether student grades moderate this difference. We found no evidence from this dataset that suggests White students perceived the instructor more positively than BIPOC students due to higher grades. Thus, other factors may shape the perceptions of White and BIPOC students differently in the classroom, such as faculty-student interactions, which should be explored further through classroom observations. Additionally, students' beliefs about the universality of ability should be measured, as it could confound with their views of their instructor's beliefs. Perhaps there is a cultural difference in how White students and BIPOC students view the universality of ability that may be projected on the instructor. RQ3) Instructional Policies + Instructor Demographics → Student Perceptions Because student evaluations have shown biases toward faculty from marginalized groups, we assess whether instructor demographics (race and gender) affect students' perceptions of their instructors' universal beliefs. A fixed-effects linear model examined the effects of syllabus score, course grade, and instructor race/gender on student perceptions of their instructors’ non-universal and universal beliefs. The results for each variable are presented in Table 5. Table 5 The impact of instructional policies (coded from the instructors’ syllabi) on student perceptions of the instructor's universality beliefs accounting for instructor demographic factors Syllabus Factor Community Power and control Evaluation and assessment Variable Est. SE LCI UCI Est. SE LCI UCI Est. SE LCI UCI Non-univ. belief Intercept 3.76 .475 2.93 4.81 3.80 .452 2.94 4.80 4.03 .479 2.98 4.87 Instruct. policy − .001 .003 − .007 .004 − .004 .005 − .014 .005 − .007 .003 − .011 .003 Course grade − .016 .005 − .027 − .006 − .015 .005 − .026 − .006 − .017 .005 − .027 − .006 Race (White) − .252 .136 − .512 .018 − .194 .142 − .488 .087 − .269 .117 − .477 .115 Gender (Woman) − .325 .119 − .568 − .084 − .276 .142 − .552 .016 .013 .112 − .521 .012 Univer. belief Intercept 3.83 .369 3.15 4.59 3.83 .339 3.19 4.51 3.75 .343 3.10 4.45 Instruct. policy .001 .001 − .004 .003 .001 .003 − .006 .006 .002 .002 − .002 .006 Course grade .014 .004 .006 .022 .014 .004 .007 .022 .015 .004 .007 .022 Race (White) .289 .086 .123 .464 .283 .093 .106 .464 .255 .084 .097 .413 Gender (Woman) .121 .081 − .042 .277 .116 .096 − .061 .333 .076 .086 − .072 .262 95% bias-corrected and accelerated (BCa) lower and upper confidence intervals. Significant CIs (do not include zero) are in bold. We consistently observe that course grades were positively associated with students' perceptions of the instructors' universal beliefs. The higher the students' grades, the more likely they are to perceive the instructor as endorsing universal and less non-universal beliefs. However, when we accounted for instructor race and gender, instructional policies did not hold up in predicting students' perceptions of the instructor’s universal beliefs. White instructors were consistently perceived to hold more universal beliefs, although no difference was detected between White and BIPOC instructors in terms of their non-universal beliefs (Table 5). Women instructors were perceived to hold fewer non-universal beliefs, but this perception was inconsistent across instructional policy factors (Table 5). Nonetheless, these results align with the literature, which suggests that women and faculty of color may face significant biases in student teaching evaluations (Kreitzer et al., 2021), which in this case are regarding students' evaluations of instructors’ beliefs about student ability. We assessed whether there were biases in student perceptions that may be a result of the possible “othering” effect, where students are likely to rate the universality beliefs of the in-group (same race) more positively than those of the out-group (different race). However, no significant interactions were detected in our dataset, indicating that all students, regardless of race, perceived White instructors as holding more universal beliefs about student ability than BIPOC instructors. That said, we cannot completely rule out the possibility that there may have been a genuine difference in this particular sample of White and BIPOC instructors, given the small sample size of instructors. Additionally, it is not entirely clear from our study if BIPOC instructors are perceived differently than White instructors solely based on race (outward physical appearance), ethnicity (a group that shares a common culture), or “foreign” status in the US. For example, students may perceive a BIPOC instructor who was born and primarily educated in the US differently from a BIPOC instructor who was born and educated outside the US. Perhaps different cultures have different implicit theories of human intelligence that are projected in the classroom. While our sample size was too small to determine this, further studies on the intersectionality of instructor identity may help pinpoint where the disparities of student perceptions lie. Lastly, we found no interaction between instructional policies and instructor race/gender in predicting students' perceptions of their instructor’s universal beliefs. Thus, adopting more student-centered policies may not necessarily mitigate biases in student perceptions of their instructors. However, this should be explored further with multiple instructors and different institutions to make any broader generalizations beyond our study sample at one institution. Overall, we observed that student perceptions of their instructor’s beliefs about their ability may stem from their performance in the course (although there is a possibility of reverse causality), but are also likely influenced by the instructor’s racial and gender identity, regardless of their instructional practices. Thus, future work investigating student perceptions (universality beliefs or generally speaking) should carefully account for instructor demographics (and other relevant factors) before drawing any conclusions. Additionally, observations of classroom practices and instructor/student interactions may provide a more nuanced understanding of how instructional factors shape student perception, considering that faculty self-reported beliefs about student ability may also be biased. Conclusions In this work, we explored the relationship between instructional policies and demographic factors (student and instructor) and student perceptions of their instructors’ universality (universal and non-universal) beliefs in the STEM classroom, while controlling for student grades. While previous work has explored how teaching behaviors influence student perceptions of instructor mindset, these studies were conducted at predominantly white institutions and were largely based on student reports of instructional practices, which may be biased (Kroeper, Muenks, et al., 2022 ; Muenks, Yan, Woodward, et al., 2021). Our work focused on collecting evidence of teaching policies from instructor syllabi as artifacts of what occurs in the classroom at a moderately selective institution serving a diverse student population. In our simplest model, our data indicated that students generally perceived their instructors’ universality beliefs differently depending on the instructional policies, as determined by the syllabus. Notably, the difference arose in evaluations and assessment policies but not in power and control or community factors. That is, when instructors utilized more student-centered policies in the evaluation of students, they were perceived more positively (less aligned with non-universal beliefs and more aligned with universal beliefs). Thus, instructors who are undergoing pedagogical reform to include more student-centered policies in their classroom (i.e., student response systems, formative homework assignments with feedback) in STEM are indeed increasing the association of their policies with more universal beliefs and less non-universal beliefs, which can lead to downstream effects in improving student engagement, retention, and performance in STEM. We found no differences in student perceptions of the instructor’s universality beliefs based on the student's gender, age, or generational status. However, White students had significantly more positive perceptions of instructors’ beliefs (lower non-universal belief scores) than BIPOC students across all three syllabi factors (Table 4 ), with no differences in universal beliefs. Interaction analyses showed that grades did not moderate this difference, suggesting other factors may influence how White and BIPOC students perceive instructors. Future research should explore these differences through classroom observations and measure students' universality beliefs, which may confound their perceptions of instructors. Our work also highlights the challenges instructors may face when creating an environment that communicates more universal and less non-universal beliefs, which falls outside of their adoption of student-centered policies and is instead related to their demographic identities and broader systemic inequities in representation in STEM fields. In our study, White instructors are generally perceived to endorse more universal beliefs as compared to their BIPOC peers, regardless of instructional policies and irrespective of student race. While this could be due to actual differences between White and BIPOC cultures in the classroom in our study sample, this observation may be a result of systemic inequities that perpetuate White superiority in positions of power that even BIPOC students are susceptible to believing. However, White and BIPOC instructors may exhibit behavioral differences (verbal and nonverbal cues) that cannot be detected in syllabus coding, which contribute to the difference in student perceptions and should be examined in future studies. While this is the first study of its kind at a mid-sized metropolitan university with highly diverse students (in terms of race, gender, age group, and generational status), this work is based on a small sample size of instructors from one institution and, therefore, generalizations to other settings may be limited. The demographic binning of students and instructors in BIPOC and White categories may be limiting, as it risks losing nuances of identity. It is not entirely clear from our study if BIPOC instructors are perceived differently from White instructors solely based on race (outward physical appearance), ethnicity (a group that shares a common culture), or “foreign” status in the United States. Perhaps different cultures have different implicit theories of human intelligence that are projected in the classroom. While our sample size was too small to determine this, further studies on the intersectionality of instructor identity may help pinpoint where the disparities of student perceptions lie. Additionally, depending on how instructors view the role of the syllabi and how much they deviate from it, the syllabi coding may not accurately reflect what is occurring in the classroom. Thus, future work should employ cross-validation studies with direct observations to assess the validity of the information contained in the syllabi. Although attempts were made to minimize bias by using two raters of different backgrounds in this study, biases may still exist in the scoring rubric, which are subject to the coders' interpretation. Hence, other researchers are encouraged to utilize the rubric and refine its criteria and scoring scheme. Lastly, there may be other factors that are shaping student perceptions of instructor mindsets, such as contextual factors (subject-specific, verbal, and nonverbal cues) or the students’ own views about human abilities. For example, an instructor might utilize a student-centered approach, such as a student response system, to gather input from all students but use it in a teacher-centered manner, valuing only the correct answer rather than using it to help students understand the underlying process. Thus, future work should seek to assess the qualitative aspect of how instructional policies are implemented in the classroom. Abbreviations BCa: 95% bias-corrected and accelerated BIPOC: Black Indigenous People of Color LCI: Lower Confidence Interval SD: Standard Deviation SE: Standard Error STEM: Science, Technology, Engineering, and Mathematics SWN: Shapiro-Wilks Normality Test UCI: Upper Confidence Interval ULTrA: Undergraduate Lay Theories of Ability Declarations Ethics Approval and consent to participate All protocols in this study were reviewed and approved by the Institutional Review Board (IRB). Participants were invited to participate and given the option to opt out at any time after providing their consent, with no consequences. Consent for publication All participants were notified that consenting to participate includes consent for the authors to publish aggregated results without disclosing individual identities or personal information. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This work is supported by the Upholding Active Learning Reform in STEM (UALRS) initiative (NSF, #2142611). References Baecker, D. L. (1998). Uncovering the Rhetoric of the Syllabus: The Case of the Missing I. College Teaching , 46 (2), 58–62. https://doi.org/10.1080/87567559809596237 Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit Theories of Intelligence Predict Achievement Across an Adolescent Transition: A Longitudinal Study and an Intervention. Child Development , 78 (1), 246–263. https://doi.org/10.1111/j.1467-8624.2007.00995.x Canning, E. A., Muenks, K., Green, D. J., & Murphy, M. C. (2019). 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L. (2019). The mindset disconnect in mathematics teaching: A qualitative analysis of classroom instruction. The Journal of Mathematical Behavior , 56 , 100706. https://doi.org/10.1016/j.jmathb.2019.04.005 Sund, K. J., & Bignoux, S. (2018). Can the performance effect be ignored in the attendance policy discussion? Higher Education Quarterly , 72 (4), 360–374. Education Research Complete. https://doi.org/10.1111/hequ.12172 Sybing, R. (2019). Making Connections: Student-Teacher Rapport in Higher Education Classrooms: Student-teacher rapport in higher education classrooms. Journal of the Scholarship of Teaching and Learning , 19 (5), Article 5. https://doi.org/10.14434/josotl.v19i5.26578 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7190396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498967908,"identity":"bab859e0-470f-47ce-821e-c3a9366d2446","order_by":0,"name":"Ronia Kattoum","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACHijNDxPgQxbFoYWx4QCQlmyACrCxEavF4ACxWsx7Dj9//KHicLTx7cMHH/6oOCzHJt/A+OBtG24tMmfbDBsOnDmcu+1cWrIxz5nDxkBbmA3n4tEiwc9g2HCwDajlDI+ZNGNbWmIbGwObNC9eLewfwVo29/CYSf78l1YP1ML+G68W3h6ILRt4eMwkeBtsEoAOY2PGq4XnTOGMM2fSc2ecYQP65ZiNYRtbYrPknHP4tKRv+FBRYZ3b38MMDLEaCXl+5sMHP7wpw60FG2BsIE39KBgFo2AUjAIMAAAuN04Av1EL/gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Arkansas at Little Rock","correspondingAuthor":true,"prefix":"","firstName":"Ronia","middleName":"","lastName":"Kattoum","suffix":""},{"id":498967909,"identity":"b5ea66fd-cf72-4119-be67-386b14c2bf7d","order_by":1,"name":"Cole Dwyer","email":"","orcid":"","institution":"University of Arkansas at Little Rock","correspondingAuthor":false,"prefix":"","firstName":"Cole","middleName":"","lastName":"Dwyer","suffix":""},{"id":498967910,"identity":"5453e253-0dad-4cca-82ed-484476264539","order_by":2,"name":"Mark Baillie","email":"","orcid":"","institution":"University of Arkansas at Little Rock","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Baillie","suffix":""}],"badges":[],"createdAt":"2025-07-22 21:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7190396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7190396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94045180,"identity":"b20808c0-6de8-4ffe-9b9f-135cc2f85a08","added_by":"auto","created_at":"2025-10-21 21:16:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7190396/v1/911e24b9-3032-48ed-85c8-c3c14c182086.pdf"},{"id":88931333,"identity":"b6315b0b-3a4a-4bfc-b5ea-38c42d1e6005","added_by":"auto","created_at":"2025-08-12 21:51:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":59782,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7190396/v1/59bce07504904a3c9d360470.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Student-centered instructional policies are associated with more positive student perceptions of their instructor's universality beliefs, but these perceptions favor White instructors in STEM","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStudents' lay theories of ability, beliefs about whether intelligence is innate (fixed mindset) or malleable (growth mindset), have been associated with student outcomes (Dweck \u0026amp; Leggett, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). When students believe that their intelligence is fixed, this correlates with increased helplessness and lower grades (De Castella \u0026amp; Byrne, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Students who believe their intelligence is malleable show an upward trajectory in their math grades over a two-year period in junior high school, whereas those who believe their intelligence is fixed show no such growth in their grades (Blackwell et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Perhaps the mechanism by which student mindset beliefs influence outcomes can be explained by recent work that shows that when students believe their intelligence can grow, they choose more effective study strategies that may be more error-prone because they are more comfortable with seeing error as part of the learning process (Choubalova et al., 2024). Women developed a disparity in how they viewed their “Ability” over time in a physics course as compared to men, which negatively impacted their course grades (Malespina et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While students' theory about their intelligence is an important factor in their motivation and performance, an essential component of the mindset context is students’ perception of the instructor’s mindset (Muenks, Yan, \u0026amp; Telang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Even after controlling for students’ mindsets, if students perceive the instructor to endorse a more fixed mindset, this is associated with lower motivation and a sense of belonging in engineering courses (Muenks, Yan, \u0026amp; Telang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, those who perceive the instructor as endorsing a more fixed mindset experience greater academic misfit, leading to lower chemistry grades (Kattoum et al., 2024) and a lower sense of belonging, which in turn leads to underperformance among women in STEM (Canning et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, when students perceive that the instructor believes only some students can succeed in STEM, this leads to downstream effects on their own growth beliefs, resulting in less classroom comfort in contributing to discussions and lower grades, especially for traditional-age students (18–24 years old) (Kattoum and Baillie, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings align with Social Cognitive Theory (SCT), which posits that learning occurs within a social system in which the environment reciprocally influences both the student's affective states (attitudes and feelings) and behaviors (engagement and performance). Thus, students are influenced by what the instructor models. This raises the question of how instructional policies affect student perceptions of their instructors’ lay theories of ability, particularly if these perceptions may perpetuate disparities in student representation of historically marginalized groups in STEM compared to the majority group (Canning et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrior work has started investigating how the learning environment may impact student perceptions. For example, instructors who provide opportunities for practice and feedback, explicitly emphasize student progress (rather than performance), value student learning and development, and provide constructive feedback in response to poor performance in STEM classrooms are perceived to endorse more of a growth mindset by their students (Kroeper, Fried, et al., 2022; Kroeper, Muenks, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another study found that students perceived instructors as having a more growth mindset when they employed “elaborative learning practices” such as interactive group work and project-based instruction in science classrooms (Muenks, Yan, Woodward, et al., 2021). Additionally, practices that communicate a growth mindset include focusing on processes, normalizing mistakes, encouraging students to contribute knowledge, and providing opportunities for feedback in the assessment process (Sun, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While insightful, these studies have been primarily based on student perceptions of the instructor's teaching practices for the general science faculty, not necessarily on what policies are implemented in a college STEM classroom for specific instructors (Kroeper, Muenks, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muenks, Yan, Woodward, et al., 2021). Although others have conducted teaching observations and interviews to determine the teaching practices that convey mindset messages in mathematics education, these studies have primarily been derived from the K-12 setting (Sun, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Lastly, the impact of instructor demographics has not been examined in conjunction with instructional policies to determine how they influence student perceptions. Our work builds upon previous studies by utilizing syllabi as artifacts of instructional policies (a proxy for instructional practices), which we hypothesize may influence students' perceptions of their instructor’s lay theories of abilities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHuman Lay Theories of Abilities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Undergraduate Lay Theories of Abilities (ULTrA) survey offers nuanced insights into lay theories of human ability specific to STEM, providing greater depth in understanding students' perceptions of the learning environment (Limeri et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The ULTrA characterized human lay theories about ability as separate but related constructs of mindset, universality, and brilliance beliefs. Mindset beliefs reflect whether students believe they can grow their intelligence through effort or perceive their intelligence as a fixed trait that cannot be developed, a concept similar to Dweck’s (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) mindset theory. For example, a student with a growth belief might agree, \"I could improve my intellectual abilities to the same level as successful STEM professionals.\"\u003c/p\u003e\u003cp\u003eUniversality beliefs (universal and non-universal beliefs) refer to an individual’s beliefs about who has the potential to reach excellence in a given field (Limeri et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A student who endorses universal beliefs may align with the thought that all students can reach maximum potential: “Anyone could become effective at learning as highly successful STEM students.” On the other hand, students who endorse non-universal beliefs may perceive that only certain students can reach the highest level of potential: “Even if they try, some people could never become as effective at analyzing information as their peers.” A student can have a growth mindset (believing they can improve their STEM abilities) but hold non-universal beliefs (believing they can never master a concept in science class, no matter how much they study).\u003c/p\u003e\u003cp\u003eLastly, brilliance's beliefs gauge whether students perceive that specific fields require raw talent: “Becoming a top student in STEM requires an innate talent that just cannot be taught.” This distinction is separate from students' beliefs about whether they can develop their abilities (growth beliefs) or whether there is a distribution of abilities among students (universalism beliefs). For example, a student may believe they can improve their math intelligence (growth beliefs) and master any concept in math class if they try (universal beliefs), but believe that only those who are born brilliant can become top mathematicians (brilliance beliefs). All three constructs of lay theories of human ability, relating to how students perceive their own abilities, have been linked to student success.\u003c/p\u003e\u003cp\u003eThis work focuses on student perceptions of their instructors’ beliefs about \u003cem\u003ewho\u003c/em\u003e can succeed in STEM (i.e., universality beliefs). This is particularly interesting to the study because of the reputation of STEM disciplines as exclusive to certain people. If an instructor believes that all students can achieve excellence in STEM, this may be reflected in their course policies, such as providing students with multiple opportunities to succeed in an assignment. If they believe only some will be able to “make it” through the STEM pipeline, they may structure their course to emphasize performance over process and effort (i.e., only a midterm and final exam count toward course grades) to “weed out” students who are not successful. While we acknowledge that students’ perceptions of their instructors’ mindsets and brilliance beliefs may also be related to their instructional policies, we hypothesize that students may pick up on cues from the instructors’ policies that signal they believe STEM disciplines are exclusive to certain people. This would be particularly important for students from marginalized communities who may be more vulnerable to stereotype threats based on contextual cues from the classroom environment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstructional policies: inferred from syllabi\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile syllabi traditionally serve as policy-based contracts, permanent records reflecting course content, and guides to learning strategies (Parkes \u0026amp; Harris, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), they also reflect the teaching approaches for instructors. Prior work has shown that students from all backgrounds form their initial impressions of an instructor’s competency and approachability based on their syllabi, as well as their decision to take another course with the instructor (Jenkins et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Merchán Tamayo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saville et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Relatively small changes to course syllabi, such as the use of ‘we’ over ‘I’ (Baecker, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and statements of mental health support (Gurung \u0026amp; Galardi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), can have a significant improvement on student perceptions (Perrine et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Student perceptions of their instructors’ universality beliefs approach may be related to the policies they adopt and describe in their syllabi. We hypothesize that instructors who utilize more student-centered policies in their syllabi may be more likely to be perceived by students as endorsing universal beliefs and fewer non-universal beliefs about their abilities.\u003c/p\u003e\u003cp\u003ePrevious researchers have developed syllabus scoring rubrics to assess the student-centeredness of instructor syllabi (Cullen \u0026amp; Harris, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Richmond, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Richmond et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and as a guide for instructors seeking to effectively improve their course design through their syllabi (Merrill, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Slattery \u0026amp; Carlson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Various disciplines, including business (Rich, 2023) and psychology (Richmond et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), have applied these rubrics to evaluate teaching policies.\u003c/p\u003e\u003cp\u003eCullen and Harris identified three primary factors in assessing the student-centeredness of syllabi: community, power and control, and evaluation and assessment. The community factor gauges the instructors’ accessibility and the opportunities they provide for collaboration within their course(s). The power and control factor builds upon the idea that students have more positive course outcomes when they feel autonomous (Merchán Tamayo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, this factor examines evidence of shared power between the teacher and students through the manner and tone of the syllabus, as well as the granting of students some choice in policy and assessment. The final factors, evaluation and assessment, uncover the instructor's techniques for evaluating student performance. Does the instructor employ iterative assessment techniques to monitor student progress with constructive feedback opportunities or rely heavily on formal summative assessment as the primary mode of student evaluation?\u003c/p\u003e\u003cp\u003ePrevious studies have found that professors typically score higher in the community-based factor but lower in evaluation and assessment, such as providing opportunities to revise assignments and offering learning rationales tied to outcomes (Cullen \u0026amp; Harris, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Richmond et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Students have been found to prefer longer, more detailed syllabi (Harrington \u0026amp; Gabert-Quillen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, students held more positive perceptions of hypothetical instructors whose syllabi were rated as more learner/student-centered (Richmond et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For our study, we modified the Cullen and Harris (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) rubric to assess the student-centeredness of faculty syllabi within STEM disciplines. We hypothesize that instructional policies, inferred by the research team from the syllabi, may be correlated with student perceptions of the instructor's universal and non-universal beliefs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudent and Instructor Demographic Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe recognize existing systemic inequities that contribute to disparities in the representation of people from historically marginalized groups such as Black Indigenous People of Color (BIPOC: Black, Native American, Native Hawaiian, Alaskan Native, Asian, Pacific Islander, Hispanic/Latino/a/e, other, multiracial), women, first-generation college students, and nontraditional-age student (\u0026gt; 22 years old) in US higher education, particularly in STEM. Thus, to examine and highlight the narrative of those marginalized groups, we assess if student demographic characteristics moderate the relationship between instructional policies and student perceptions within the undergraduate STEM context at a metropolitan research institution with a diverse student population. We hypothesize that more student-centered instructional policies focusing on growth and development may benefit these student populations and increase their participation in STEM fields.\u003c/p\u003e\u003cp\u003eAdditionally, we explore whether faculty demographic characteristics (race and gender) influence student perceptions and how these factors interact with their instructional practices. Previous work has shown that faculty with marginalized identities (BIPOC and women) are subject to more biased student teaching evaluations (Kreitzer \u0026amp; Sweet-Cushman, 2022). However, no work has explored how students' perceptions of their instructor's mindset are influenced by faculty demographic factors, particularly in tandem with their instructional policies. Awareness of such biases (if they exist) can lead to more robust conclusions and discoveries in mindset research by accounting for the instructors’ demographics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCurrent Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study builds on the current literature by addressing the following research questions:\u003c/p\u003e\u003cp\u003eRQ1) How do student perceptions of their instructors’ universality beliefs vary based on instructional policies (coded from instructor syllabi) while controlling for their course grades?\u003c/p\u003e\u003cp\u003eRQ2) How do student demographic factors (race, gender, age group, generational status) play a role in determining student perceptions of the instructors’ universality beliefs, and how does that interact with their instructional policies and student grades?\u003c/p\u003e\u003cp\u003eRQ3) How do the instructors’ demographic characteristics (race and gender) play a role in student perceptions of the instructor’s universality beliefs, and how does that interact with their instructional policies?\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers recruited full-time faculty members in STEM disciplines who taught undergraduate courses in a southern United States metropolitan institution by visiting department meetings. Twenty-four faculty members (whom we will refer to as instructors, although they may have various ranks) consented to participate in the research project during the 2022\u0026ndash;2023 academic year, with IRB approval. Four of the twenty-four faculty participants had fewer than five student responses and were excluded from the analysis to protect student identities, resulting in twenty instructor participants. Instructors did not have an incentive for participation.\u003c/p\u003e\n\u003cp\u003eAcademic ranks included two full professors, two associate professors, seven assistant professors, eight full-time lecturers/instructors, and one visiting instructor. Four instructors were tenured, seven were untenured but on the tenure track, and nine were untenured and not on the tenure track. Instructors came from eight departments: Anthropology, Biology, Chemistry, Information Science, Mathematics and Statistics, Mechanical Engineering, Physics and Astronomy, and Public Affairs. Thirteen instructors identified as White, and seven identified as BIPOC. Thirteen instructors identified as men and seven as women. Teaching experience ranged from 1 to 35 years, with an average of 12.6 years (SD\u0026thinsp;=\u0026thinsp;7.7). Teaching responsibilities ranged from 10\u0026ndash;85%, averaging 59.5% (SD\u0026thinsp;=\u0026thinsp;19.8).\u003c/p\u003e\n\u003cp\u003eInstructors were asked to identify courses from which the research team could collect syllabi, student consent forms, and data. All students in those courses were recruited to participate in the study. The research team collected student consent and questionnaires during the first or last ten minutes of one of their classes or labs in the 12th or 13th week of a 14-week semester. Regardless of whether they agreed to participate in the study, students who completed the consent form were entered into a raffle to win one of thirty \u003cspan\u003e$\u003c/span\u003e20 gift cards. A QR code was used to direct students to a Qualtrics survey administered in class. The Qualtrics survey contained instruments that assessed students\u0026apos; perceptions of the instructor\u0026rsquo;s universal beliefs, self-identified demographic factors, and other factors that were part of a more extensive study.\u003c/p\u003e\n\u003cp\u003eNine of the twenty instructors taught multiple courses, and student surveys were collected from all of their courses. If the same student took the survey for multiple instructors, they were treated as distinct data points for each instructor and retained for analysis, as they were asked to reflect on a learning environment specific to that instructor and course. Response rates from the thirty-four courses across eight departments ranged from 38\u0026ndash;90% (n\u0026thinsp;=\u0026thinsp;5 to n\u0026thinsp;=\u0026thinsp;36) for 625 student participants. Listwise deletions resulted in 480 student responses with complete cases, as summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for the final dataset.\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\n\u003cp\u003eStudents identified as Black, Native American, Native Hawaiian, Alaskan Native, Asian, Pacific Islander, Hispanic/Latino/a/e, other, or multiracial were categorized as BIPOC. Those who chose not to identify demographic characteristics were removed from the analysis. Students who identified as non-binary/third gender constituted less than 2% of the sample size. Thus, they were excluded from the quantitative comparison due to the relatively small sample size, which makes it difficult to draw any inferences from the comparison.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudent perceptions of instructor universality beliefs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the scope of this study and to minimize survey fatigue (as this data is a subset of data collected for a more extensive study), we chose to utilize only the universality beliefs (universal and non-universal beliefs) scale from the ULTrA survey and adapt it to assess student perceptions of what they think the instructor believes about student abilities. For example, we modified the original item, \u0026ldquo;Even if they try, some \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epeople\u003c/span\u003e could never become as effective at analyzing information as their peers,\u0026rdquo; to gauge the student perception of instructor beliefs about students: \u0026ldquo;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe professor in this course seems to believe that\u003c/span\u003e even if they try, some \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003estudents\u003c/span\u003e could never become as effective at analyzing information as their peers.\u0026rdquo; The process was repeated for all ten universality items (see Supporting Information, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Student questionnaires were collected toward the end of the semester in Qualtrics with an option to answer on a scale of 1\u0026ndash;6 regarding how much they agreed with those ten statements (1- strongly disagree, 2- disagree, 3- somewhat disagree, 4- somewhat agree, 5- agree, 6- strongly agree, prefer not to answer). A higher score indicated more alignment with universal and non-universal beliefs.\u003c/p\u003e\n\u003cp\u003eBecause the original instrument was modified slightly, a confirmatory factor analysis (CFA) was conducted to assess the instrument\u0026apos;s validity, and Cronbach Alpha was used to assess the reliability of the survey items. Because of the non-normal distribution for universality beliefs data (through visual QQ plots), an initial CFA was conducted based on the factor structure of the ULTrA survey using a robust maximum likelihood approximation (vs. maximum likelihood approximation for normally distributed data) and full information maximum likelihood (FIML) for missing data. Results revealed acceptable model fit (Robust CFI/TLI\u0026thinsp;=\u0026thinsp;00.97/00.96, Robust RMSEA\u0026thinsp;=\u0026thinsp;00.083 (90% confidence interval: 00.067-00.100), SRMR\u0026thinsp;=\u0026thinsp;00.053) on three of the four parameters (threshold of acceptability: CFI/TLI\u0026thinsp;\u0026gt;\u0026thinsp;00.95, RSMEA\u0026thinsp;\u0026lt;\u0026thinsp;00.08, SRMR\u0026thinsp;\u0026lt;\u0026thinsp;00.06) with one item loading poorly on the non-universal beliefs scale (0.28). Further inspection of the data revealed an inconsistency in wording that may explain the poor loading of the item. The original item read, \u0026ldquo;Only people with a natural talent can become excellent at analyzing information,\u0026rdquo; which was modified to \u0026ldquo;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe professor in this course seems to believe that students\u003c/span\u003e with a natural talent can become excellent at analyzing information.\u0026rdquo; Leaving out \u0026ldquo;only\u0026rdquo; changed the statement\u0026apos;s meaning (most likely due to a transcription error). There is no reason to believe that talented students will not do well in analyzing information, but believing that \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eonly\u003c/span\u003e talented students will do well indicates endorsement of non-universal beliefs. This item was removed from further analysis because of the possibility of different meanings from this item (as was apparent in the factor loadings of the CFA). A CFA on the revised survey indicated an excellent model fit, with all model parameters meeting the threshold of acceptability. (Robust CFI/TLI\u0026thinsp;=\u0026thinsp;0.98/0.97, Robust RMSEA\u0026thinsp;=\u0026thinsp;0.078 (90% confidence interval: 0.058\u0026ndash;0.099), SRMR\u0026thinsp;=\u0026thinsp;0.018) with item loadings ranging from 0.71\u0026ndash;0.94). Universal beliefs (5 items) and non-universal beliefs (4 items) subscales showed excellent internal consistency with Cronbach Alpha of 0.94 and 0.84, respectively.\u003c/p\u003e\n\u003cp\u003eBecause this work aimed to explore how student perceptions relate to instructional policies and demographic factors, it is prudent to conduct measurement invariance testing to determine if different groups of students interpret questionnaires differently. If measurement invariance holds, then both groups (e.g., men and women) interpret questions similarly, and any differences found between groups can be attributed to actual differences in the outcome variables. For our study sample, measurement invariance held for all demographic groups tested (race, gender, age group, and generational status), with details outlined in the Supporting Information section.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudent-centeredness of instructional policies as identified from the syllabus\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTwenty-four instructors contributed syllabi from one or more of their courses, resulting in thirty-four STEM syllabi. The scoring rubric developed by Cullen and Harris (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) served as the basis for our deductive coding, which utilized the three primary themes they identified: community, power and control, and evaluation and assessment, each comprising 4\u0026ndash;5 items (See Supporting Information, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eRaters included an undergraduate research assistant (CD) and a Ph.D. candidate (RNK) who is also a college-level chemistry instructor. While coding, the researchers were unaware of students\u0026rsquo; responses to the mindset questionnaire to minimize bias. To start, the research team randomly selected three syllabi. Each rater worked independently to assign a score between 0 and 3 for each item through deductive coding based on the original Cullen and Harris (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) rubric. The scoring scheme was modified using inductive coding to reflect the nuances discovered during the scoring of the first three syllabi. New items that were not reflected in the original rubric were added as they emerged. Researchers kept notes for each score, along with justification, in a spreadsheet that included evidence from the syllabi to support the reason for that score (see the scoring rubric and sample evidence from the syllabi in the Supporting Information).\u003c/p\u003e\n\u003cp\u003eAfter rating, researchers met to compare their scores, resolve any discrepancies, and discuss any emerging issues until a consensus was reached. These discussions highlighted the unique perspectives of researchers from different backgrounds, including students and instructors, when coding syllabi. For example, university core objectives may have been identified by the instructor as a mandatory addition that may or may not align with the instructor\u0026rsquo;s pedagogy. Alternatively, a student may be more apt to interpret the tone of a syllabus than an instructor, having first-hand experience as a student taking similar courses. The variation in the raters\u0026apos; perspectives reduces the potential bias in final coding scores, resulting in more accurate coding.\u003c/p\u003e\n\u003cp\u003eAnother round of three syllabi was randomly selected for scoring based on the modified rubric. If new items emerged or scoring criteria were refined, the researchers recoded the previous syllabi with the new refined scoring scheme. After the third round (9 total syllabi), the research team agreed on the scoring scheme and items in the modified rubric. The remaining syllabi were scored with the finalized modified rubric version, with researchers meeting regularly to discuss and resolve any discrepancies in coding until a consensus was reached. Instructors who taught multiple courses generally had the same syllabus structure and thus had similar syllabi scores for separate courses. A summary of the criteria for the three factors is provided below, with additional details included in the Supporting Information section.\u003c/p\u003e\n\u003cp\u003eA student-centered classroom \u003cem\u003ecommunity\u003c/em\u003e is one in which students can engage with the instructor and other students. A clear rationale is provided for assignments where students\u0026rsquo; presence (or lack thereof) would be noticed and recognized. On the other hand, a teacher-centered classroom community generally lacks opportunities for collaboration, has limited access to the instructor, lacks an explicit rationale for assignments, and lacks a system for monitoring students\u0026rsquo; attendance, as indicated by the syllabus. Four items were used to gauge the community aspect of the instructors\u0026rsquo; course(s) as inferred from their syllabi.\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;Accessibility of teacher \u0026apos;\u0026apos; ranged from the instructor not prescribing office hours to providing prescribed office hours and appointment options using multiple modalities (virtual and in-person). Our modified version of the rubric did not include \u0026apos;fax\u0026apos; and \u0026apos;home phone\u0026apos; as forms of communication because they were not present in any of the syllabi coded. The \u0026ldquo;learning rationale\u0026rdquo; item assessed whether instructors provided a rationale for assignments (linked to learning objectives) rather than simply listing them. Scoring criteria in the \u0026ldquo;collaboration\u0026rdquo; item were refined to encompass opportunities and requirements for collaboration, ranging from no opportunities to required group work that encouraged students to learn from one another both inside and outside the classroom. The phrase \u0026ldquo;discourages interaction except in class or for emergency\u0026rdquo; was modified to \u0026ldquo;only one form of communication provided,\u0026rdquo; as none of the syllabi explicitly discouraged interaction. Lastly, the \u0026ldquo;attendance policy\u0026rdquo; item was added under the Community factor because many syllabi specified attendance policies, but it was not included in the original rubric. Mandatory attendance has been shown to have a positive correlation with student performance (Sund \u0026amp; Bignoux, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), and some instructors have argued that it is essential for promoting active learning (Higbee \u0026amp; Fayon, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). As attendance policies are often unpopular with students, instructors can incorporate components that help foster student rapport, such as acknowledging students\u0026apos; important role in the classroom through their presence (Sybing, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, the criteria ranged from no attendance policy specified to required attendance that makes students feel part of the learning environment, fostering a sense of community.\u003c/p\u003e\n\u003cp\u003eRegarding the \u003cem\u003epower and control\u003c/em\u003e factor, a teacher-centered syllabus generally reflects an authoritative and punitive classroom culture, in which the instructor positions themselves as the sole source of knowledge and the student\u0026rsquo;s role is limited to receiving it. A more student-centered syllabus would reveal elements of shared power between the instructor and student. That would entail a classroom culture where students have more autonomy, are encouraged to bring their knowledge to the class, and reference multiple avenues (besides the instructor) to develop their skills. Five items gauged the power and control factor. The \u0026ldquo;teacher\u0026rsquo;s role\u0026rdquo; determined the extent to which the instructor provided options for students to participate in their own education (e.g., choose a topic for a project rather than assign a topic). The \u0026ldquo;student role\u0026rdquo; measured the students\u0026rsquo; expected contribution to the class, ranging from being a passive participant to contributing to the learning community. \u0026ldquo;Outside resources\u0026rdquo; determined whether instructors provided external homework platforms, notes, video links to content/class recordings, tutoring, and academic support services or positioned themselves as the only resource for students. We added the item \u0026ldquo;syllabus tone,\u0026rdquo; adapted from work by Chen et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) based on observations from the sample syllabi. The syllabus\u0026apos;s tone is used \u0026ldquo;...to capture the positive, encouraging, and collaborative language employed in the syllabi corpus\u0026rdquo; (Chen et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Under this item, criteria ranged from an entirely punitive syllabus tone to a positive and encouraging one that fosters student teamwork. Finally, \u0026ldquo;syllabus focus\u0026rdquo; assessed if the syllabi were primarily written to communicate policies and procedures (like a legal document) or were centered around student learning.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eevaluation and assessment\u003c/em\u003e factor identified a teacher-centered syllabus as one with limited opportunities for feedback and revision, where the instructor relies heavily on student summative assessments as the primary mode of evaluation. A student-centered syllabus generally reflected an instructor who evaluated students based on a broad range of assignments with regular feedback and opportunities for revision. Five items determined evaluation and assessment. Criteria under \u0026ldquo;grades\u0026rdquo; were expanded from the original rubric to account for the weight of assignments (e.g., the relative weight of summative and formative assessments) and how frequently formative assessments were administered. An instructor may use various feedback and evaluation techniques, but still primarily assigns grades based on summative exams or uses assignments to accumulate points (such as extra credit and participation points) rather than as learning opportunities. The \u0026ldquo;feedback mechanisms\u0026apos;\u0026apos; item was refined slightly to include feedback mechanisms via homework and student response systems, such as iClicker, since they were more prevalent in the syllabi coded than written work. Criteria ranged from one form of feedback mechanism (exams) to a more scaffolded feedback mechanism (homework and/or quizzes) and real-time feedback in class. This item aimed to determine the extent to which instructors utilized feedback mechanisms, not how they were accounted for in student grades. The \u0026ldquo;evaluation\u0026rdquo; item focused on how students\u0026apos; learning was assessed, with criteria ranging from using only summative assessments to a more diverse portfolio of student work that also included formative, collaborative, and generative assessments, common assessment types in STEM that may indicate a deeper level of engagement with the material (Fiorella \u0026amp; Mayer, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The \u0026ldquo;learning outcomes\u0026rdquo; item determined how assignments were tied to assessing learning outcomes. Lastly, \u0026ldquo;revision/redoing\u0026rdquo; assessed the extent to which instructors gave opportunities for learning and growth without penalizing students.\u003c/p\u003e\n\u003cp\u003eThe range of scores was 0\u0026ndash;3 for each item, with a score of 0 indicating teacher-centered policies and a score of 3 indicating student-centered policies. Regarding the community factor, the research team considered four items, each with a maximum score of three, for a total of twelve points. If the researchers scored the rubric on the community factor as 9/12 (75%), then the instructor was identified as leaning more towards student-centered policies when establishing a community in their course. Descriptive statistics of the scoring rubric for the three factors of syllabi coding are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of instructional policies coded from the instructor syllabi by the research team\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkew\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShapiro-Wilks sig\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePower \u0026amp; Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvaluation and Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eThe Shapiro-Wilks Normality Test (SWN) was insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and skew and kurtosis are within the +\u0026thinsp;2 range, suggesting normality in the data distribution.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe average factor scores ranged from ~\u0026thinsp;43% to ~\u0026thinsp;50%, with instructors scoring the lowest in power and control and the highest in establishing community. Although the averages were generally in the middle of the spectrum between teacher-centered and student-centered, scores among instructors varied considerably (SD ranged from ~\u0026thinsp;18 to 25). The Shapiro-Wilk normality (SWN) test (used to assess normality for n\u0026thinsp;\u0026lt;\u0026thinsp;50), skew, and kurtosis indicated that the data are normally distributed for the syllabi factors (SWN was insignificant: p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, and skew and kurtosis were within the \u0026plusmn;\u0026thinsp;2 range).\u003c/p\u003e\n\u003cp\u003eWhile building community and sharing power and control with students in the course can influence student perceptions of the instructor\u0026rsquo;s universal beliefs, we hypothesize that evaluation and assessment may be more linked to the outcome variable. How faculty evaluate students may be more directly related to their universality beliefs about student abilities that students may pick up on. Thus, we assess how each syllabus factor influences student perceptions to uncover a more nuanced understanding of which policies influence student perceptions. The Variance Inflation Factor (VIF) values for the community (1.18), power and control (1.93), and evaluation and assessment (1.88) indicate no significant multicollinearity (all values are well below the threshold of 5), justifying our decision to examine each factor individually.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCourse grades\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGrades were retrieved from instructors at the end of the semester for consenting students only. Course grades were used as a control variable to isolate the impact of instructional policies on student perceptions, particularly at the end of the semester when students are most likely to be aware of their standing in the course.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod of Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur sample set included twenty instructors, some of whom taught multiple courses, each with a corresponding syllabus score. On the other hand, there were multiple student responses per instructor and course. Thus, we must account for the data\u0026apos;s hierarchical (nested) structure to assess how the instructor\u0026apos;s policies, as reflected in the syllabus score, shape student perceptions. That is, student responses may not be entirely independent, but influenced by shared factors related to their instructor or the course. To account for this, we considered multilevel modeling (hierarchical linear modeling), in which we used random intercepts for instructors and courses to account for variability between instructors and courses using the \u003cem\u003elme4\u003c/em\u003e in Rstudio.\u003c/p\u003e\n\u003cp\u003eThe model\u0026apos;s random-effects analysis showed negligible variance attributed to differences between instructors (non-universal beliefs variance\u0026thinsp;=\u0026thinsp;0.000, SD\u0026thinsp;=\u0026thinsp;0.00; universal beliefs variance\u0026thinsp;=\u0026thinsp;0.0058, SD\u0026thinsp;=\u0026thinsp;0.076), suggesting that instructor-level factors did not substantially contribute to variability in student perceptions. In contrast, the residual variance was substantial at the student level (non-universal beliefs variance\u0026thinsp;=\u0026thinsp;1.47, SD\u0026thinsp;=\u0026thinsp;1.21; universal beliefs variance\u0026thinsp;=\u0026thinsp;0.68, SD\u0026thinsp;=\u0026thinsp;0.82), indicating that most variability in student perceptions occurred at the individual student level, rather than the instructor level.\u003c/p\u003e\n\u003cp\u003eThe random-effects analysis also revealed that the variance attributed to differences between courses was relatively low (non-universal variance\u0026thinsp;=\u0026thinsp;0.030, SD\u0026thinsp;=\u0026thinsp;0.174; universal variance\u0026thinsp;=\u0026thinsp;0.0061, SD\u0026thinsp;=\u0026thinsp;0.08), suggesting minimal variability in students\u0026rsquo; perceptions of instructors\u0026rsquo; non-universal beliefs across courses. In contrast, the residual variance was substantial with non-universal beliefs variance (SD\u0026thinsp;=\u0026thinsp;1.19) and universal beliefs variance (SD\u0026thinsp;=\u0026thinsp;0.82), indicating that most variability in student perceptions is attributed to individual student differences rather than course-level factors. Thus, it was determined that fixed effects multiple linear regression modeling should be used for all analyses and interactions.\u003c/p\u003e\n\u003cp\u003eVisualization methods, such as scatterplots, histograms, and QQ plots, were used to examine the linearity and normality of the data. There were general linear trends between the dependent variables and the outcome variable. However, QQ plots indicated a non-normal distribution of the outcome variables (students\u0026apos; perceptions of the instructors\u0026apos; universality beliefs). Thus, we used bootstrapping to generate empirical confidence intervals and p-values, without relying on the data\u0026apos;s normality, using the \u003cem\u003eboot\u003c/em\u003e package in RStudio for the fixed-effects multiple regression models.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eRQ1) Instructional Policies → Perceptions of the Instructors’ Universality Beliefs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA fixed-effects model was employed to investigate the relationship between instructional policies (coded from the syllabus) and students' perceptions of the instructors' universal beliefs while controlling for students' course grades. A nonparametric bootstrapping technique with 1,000 iterations was utilized to address the non-normal distribution of residuals. Results are summarized in Table 3, along with respective estimates (effect sizes), standard errors (SEs), and lower and upper confidence intervals (LCIs and UCIs) for each variable. LCI and UCI form a 95% confidence interval (in this case, bias-corrected and accelerated to account for non-normal distribution), indicating the range within which the true value is expected to fall with high probability. We examined both universal beliefs and non-universal beliefs because students’ endorsement of more universal beliefs may not necessarily indicate that they endorse fewer non-universal beliefs, as these are two separate constructs and not necessarily inversely related.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eModel 1: The impact of instructional policies (coded from the instructors’ syllabi) on student perceptions of the instructors’ universality beliefs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSyllabus Factor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePower and control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eEvaluation and assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-univ. belief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruct. policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCourse grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniver. belief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruct. policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCourse grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"13\"\u003e\n \u003cp\u003e95% bias-corrected and accelerated (BCa) lower and upper confidence intervals (LCI \u0026amp; UCI). Significant CIs (do not include zero) are bolded.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs hypothesized, more student-centered evaluation and assessment policies are associated with more positive perceptions of the instructors’ universality beliefs, characterized by lower non-universal and greater universal beliefs (although the effect size is small, Table 3). These evaluative techniques include more collaborative group work, multiple opportunities for feedback through frequent, lower-stakes homework assignments, quizzes, and live polling, as well as opportunities to revise work that focuses on achieving learning outcomes (see SI for the syllabus and rubric). These findings are consistent with previous literature that has shown students infer their instructor’s mindset more positively from teaching behaviors that involve more opportunities for practice and feedback(Kroeper, Fried, et al., 2022; Kroeper, Muenks, et al., 2022) and active/collaborative learning (Muenks, Yan, Woodward, et al., 2021).\u003c/p\u003e\n\u003cp\u003eSyllabi that communicated more shared power and control with students revealed mixed findings. They were associated with lower student perceptions that their instructor endorsed non-universal beliefs (although the effect size is small, Table 3). However, they do not seem to impact student perceptions of their instructor’s universal beliefs (Table 3). Such policies include increasing student autonomy in course assignments and grading and focusing less on procedural and contractual policies and more on student learning. Thus, when the instructor places more effort into developing a syllabus that empowers student voices, this could indicate that the instructor is more mindful of student learning, resulting in students perceiving their instructors as less likely to endorse the belief that STEM is exclusive to the few.\u003c/p\u003e\n\u003cp\u003eAs expected, syllabus policies that foster community were not significantly related to students' perceptions of the instructor's universal beliefs (Table 3). Providing multiple modalities for assisting students, creating a space where attendance is encouraged, and offering opportunities for collaboration may be more closely related to students' sense of belonging within a community (Rattan et al., 2018), rather than how they perceive their instructor’s beliefs about student ability.\u003c/p\u003e\n\u003cp\u003eHigher course grades are consistently linked to more positive perceptions of instructor beliefs about student abilities, as indicated by lower perceptions of non-universal beliefs and greater perceptions of universal beliefs (Table 3). In this work, we use course grades as control variables to isolate the impact of instructional policies on student perceptions, primarily because student surveys were collected at the end of the semester when students were most likely to be aware of their course outcomes. However, it could very well be that student perceptions of the instructor’s universality beliefs are impacting their course grades (rather than their course grades impacting their perceptions), as prior work in controlled lab settings has shown(Canning et al., 2019, 2021; Muenks et al., 2020). In a native learning environment and within a demographically diverse institution, reciprocity may exist between the learning environment and student performance, making the directionality of the relationship more challenging to determine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ2) Instructional Policies + Students Demographics → Student Perceptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we investigated whether student demographic factors, in addition to instructional policies and student grades, influenced their perceptions of the instructor's non-universal and universal beliefs through a bootstrap analysis. The results are presented in Table 4.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eModel 2: The impact of instructional policies (coded from the instructors’ syllabi) on student perceptions of the instructor's universality beliefs, accounting for student demographic factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSyllabus Factor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePower and control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eEvaluation and assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-univ. belief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruct. policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCourse grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (White)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .258\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.117\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .497\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .246\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.116\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .489\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .269\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.117\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .501\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Woman)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group (Trad)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGen. status (FG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniver. belief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruct. policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCourse grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (White)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Woman)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group (Trad)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGen. status (FG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"13\"\u003e\n \u003cp\u003e95% bias-corrected and accelerated (BCa) lower and upper confidence intervals. Significant CIs (do not include zero) are in bold.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWhen introducing student demographics into the model, we observed similar trends to those in our initial model, regarding the impact of student grades and instructional policies on student outcomes. The more student-centered evaluation and assessment, the lower the perceptions of the instructor’s non-universal beliefs (more positive). However, evaluation and assessment did not impact student perceptions of the instructor’s universal beliefs about student abilities (as we found in Model 1). Additionally, policies emphasizing shared power and control were associated with lower non-universal belief (although with a small effect size), which was not the case for our first model. However, the power and control factor was not associated with student perceptions of the instructor’s universal beliefs (consistent with Model 1). This suggests that the faculty level of shared power with students may influence student perceptions and warrants further exploration with multiple instructors and institutions. The community factor did not predict student perception of the instructor’s universal or non-universal beliefs, while course grades consistently did so (consistent with Model 1).\u003c/p\u003e\n\u003cp\u003eWe did not detect differences in student perceptions of the instructor’s universality beliefs based on gender, age, and generational status. Interestingly, White students had significantly more positive perceptions of instructors’ beliefs (lower non-universal belief scores) than BIPOC students. This was consistent in all three syllabi factors (Table 4), although no differences were detected for universal beliefs. To explore the reason for this trend, we conducted a series of interaction effects to assess whether student grades moderate this difference. We found no evidence from this dataset that suggests White students perceived the instructor more positively than BIPOC students due to higher grades. Thus, other factors may shape the perceptions of White and BIPOC students differently in the classroom, such as faculty-student interactions, which should be explored further through classroom observations. Additionally, students' beliefs about the universality of ability should be measured, as it could confound with their views of their instructor's beliefs. Perhaps there is a cultural difference in how White students and BIPOC students view the universality of ability that may be projected on the instructor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ3) Instructional Policies + Instructor Demographics → Student Perceptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause student evaluations have shown biases toward faculty from marginalized groups, we assess whether instructor demographics (race and gender) affect students' perceptions of their instructors' universal beliefs. A fixed-effects linear model examined the effects of syllabus score, course grade, and instructor race/gender on student perceptions of their instructors’ non-universal and universal beliefs. The results for each variable are presented in Table 5.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe impact of instructional policies (coded from the instructors’ syllabi) on student perceptions of the instructor's universality beliefs accounting for instructor demographic factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSyllabus Factor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePower and control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eEvaluation and assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEst.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-univ. belief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruct. policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCourse grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (White)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Woman)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .568\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e− .084\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniver. belief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstruct. policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCourse grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (White)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.123\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.464\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.106\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.464\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.097\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.413\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Woman)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e− .072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"13\"\u003e\n \u003cp\u003e95% bias-corrected and accelerated (BCa) lower and upper confidence intervals. Significant CIs (do not include zero) are in bold.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWe consistently observe that course grades were positively associated with students' perceptions of the instructors' universal beliefs. The higher the students' grades, the more likely they are to perceive the instructor as endorsing universal and less non-universal beliefs. However, when we accounted for instructor race and gender, instructional policies did not hold up in predicting students' perceptions of the instructor’s universal beliefs. White instructors were consistently perceived to hold more universal beliefs, although no difference was detected between White and BIPOC instructors in terms of their non-universal beliefs (Table 5). Women instructors were perceived to hold fewer non-universal beliefs, but this perception was inconsistent across instructional policy factors (Table 5). Nonetheless, these results align with the literature, which suggests that women and faculty of color may face significant biases in student teaching evaluations (Kreitzer et al., 2021), which in this case are regarding students' evaluations of instructors’ beliefs about student ability.\u003c/p\u003e\n\u003cp\u003eWe assessed whether there were biases in student perceptions that may be a result of the possible “othering” effect, where students are likely to rate the universality beliefs of the in-group (same race) more positively than those of the out-group (different race). However, no significant interactions were detected in our dataset, indicating that all students, regardless of race, perceived White instructors as holding more universal beliefs about student ability than BIPOC instructors.\u003c/p\u003e\n\u003cp\u003eThat said, we cannot completely rule out the possibility that there may have been a genuine difference in this particular sample of White and BIPOC instructors, given the small sample size of instructors. Additionally, it is not entirely clear from our study if BIPOC instructors are perceived differently than White instructors solely based on race (outward physical appearance), ethnicity (a group that shares a common culture), or “foreign” status in the US. For example, students may perceive a BIPOC instructor who was born and primarily educated in the US differently from a BIPOC instructor who was born and educated outside the US. Perhaps different cultures have different implicit theories of human intelligence that are projected in the classroom. While our sample size was too small to determine this, further studies on the intersectionality of instructor identity may help pinpoint where the disparities of student perceptions lie.\u003c/p\u003e\n\u003cp\u003eLastly, we found no interaction between instructional policies and instructor race/gender in predicting students' perceptions of their instructor’s universal beliefs. Thus, adopting more student-centered policies may not necessarily mitigate biases in student perceptions of their instructors. However, this should be explored further with multiple instructors and different institutions to make any broader generalizations beyond our study sample at one institution.\u003c/p\u003e\n\u003cp\u003eOverall, we observed that student perceptions of their instructor’s beliefs about their ability may stem from their performance in the course (although there is a possibility of reverse causality), but are also likely influenced by the instructor’s racial and gender identity, regardless of their instructional practices. Thus, future work investigating student perceptions (universality beliefs or generally speaking) should carefully account for instructor demographics (and other relevant factors) before drawing any conclusions. Additionally, observations of classroom practices and instructor/student interactions may provide a more nuanced understanding of how instructional factors shape student perception, considering that faculty self-reported beliefs about student ability may also be biased.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this work, we explored the relationship between instructional policies and demographic factors (student and instructor) and student perceptions of their instructors\u0026rsquo; universality (universal and non-universal) beliefs in the STEM classroom, while controlling for student grades. While previous work has explored how teaching behaviors influence student perceptions of instructor mindset, these studies were conducted at predominantly white institutions and were largely based on student reports of instructional practices, which may be biased (Kroeper, Muenks, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muenks, Yan, Woodward, et al., 2021). Our work focused on collecting evidence of teaching policies from instructor syllabi as artifacts of what occurs in the classroom at a moderately selective institution serving a diverse student population.\u003c/p\u003e\u003cp\u003eIn our simplest model, our data indicated that students generally perceived their instructors\u0026rsquo; universality beliefs differently depending on the instructional policies, as determined by the syllabus. Notably, the difference arose in evaluations and assessment policies but not in power and control or community factors. That is, when instructors utilized more student-centered policies in the evaluation of students, they were perceived more positively (less aligned with non-universal beliefs and more aligned with universal beliefs). Thus, instructors who are undergoing pedagogical reform to include more student-centered policies in their classroom (i.e., student response systems, formative homework assignments with feedback) in STEM are indeed increasing the association of their policies with more universal beliefs and less non-universal beliefs, which can lead to downstream effects in improving student engagement, retention, and performance in STEM.\u003c/p\u003e\u003cp\u003eWe found no differences in student perceptions of the instructor\u0026rsquo;s universality beliefs based on the student's gender, age, or generational status. However, White students had significantly more positive perceptions of instructors\u0026rsquo; beliefs (lower non-universal belief scores) than BIPOC students across all three syllabi factors (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with no differences in universal beliefs. Interaction analyses showed that grades did not moderate this difference, suggesting other factors may influence how White and BIPOC students perceive instructors. Future research should explore these differences through classroom observations and measure students' universality beliefs, which may confound their perceptions of instructors.\u003c/p\u003e\u003cp\u003eOur work also highlights the challenges instructors may face when creating an environment that communicates more universal and less non-universal beliefs, which falls outside of their adoption of student-centered policies and is instead related to their demographic identities and broader systemic inequities in representation in STEM fields. In our study, White instructors are generally perceived to endorse more universal beliefs as compared to their BIPOC peers, regardless of instructional policies and irrespective of student race. While this could be due to actual differences between White and BIPOC cultures in the classroom in our study sample, this observation may be a result of systemic inequities that perpetuate White superiority in positions of power that even BIPOC students are susceptible to believing. However, White and BIPOC instructors may exhibit behavioral differences (verbal and nonverbal cues) that cannot be detected in syllabus coding, which contribute to the difference in student perceptions and should be examined in future studies.\u003c/p\u003e\u003cp\u003eWhile this is the first study of its kind at a mid-sized metropolitan university with highly diverse students (in terms of race, gender, age group, and generational status), this work is based on a small sample size of instructors from one institution and, therefore, generalizations to other settings may be limited. The demographic binning of students and instructors in BIPOC and White categories may be limiting, as it risks losing nuances of identity. It is not entirely clear from our study if BIPOC instructors are perceived differently from White instructors solely based on race (outward physical appearance), ethnicity (a group that shares a common culture), or \u0026ldquo;foreign\u0026rdquo; status in the United States. Perhaps different cultures have different implicit theories of human intelligence that are projected in the classroom. While our sample size was too small to determine this, further studies on the intersectionality of instructor identity may help pinpoint where the disparities of student perceptions lie.\u003c/p\u003e\u003cp\u003eAdditionally, depending on how instructors view the role of the syllabi and how much they deviate from it, the syllabi coding may not accurately reflect what is occurring in the classroom. Thus, future work should employ cross-validation studies with direct observations to assess the validity of the information contained in the syllabi. Although attempts were made to minimize bias by using two raters of different backgrounds in this study, biases may still exist in the scoring rubric, which are subject to the coders' interpretation. Hence, other researchers are encouraged to utilize the rubric and refine its criteria and scoring scheme.\u003c/p\u003e\u003cp\u003eLastly, there may be other factors that are shaping student perceptions of instructor mindsets, such as contextual factors (subject-specific, verbal, and nonverbal cues) or the students\u0026rsquo; own views about human abilities. For example, an instructor might utilize a student-centered approach, such as a student response system, to gather input from all students but use it in a teacher-centered manner, valuing only the correct answer rather than using it to help students understand the underlying process. Thus, future work should seek to assess the qualitative aspect of how instructional policies are implemented in the classroom.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBCa: 95% bias-corrected and accelerated\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBIPOC: Black Indigenous People of Color\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLCI: Lower Confidence Interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD: Standard Deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSE: Standard Error\u003c/p\u003e\n\u003cp\u003eSTEM: Science, Technology, Engineering, and Mathematics\u003c/p\u003e\n\u003cp\u003eSWN: Shapiro-Wilks Normality Test\u003c/p\u003e\n\u003cp\u003eUCI: Upper Confidence Interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eULTrA: Undergraduate Lay Theories of Ability\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll protocols in this study were reviewed and approved by the Institutional Review Board (IRB). Participants were invited to participate and given the option to opt out at any time after providing their consent, with no consequences. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were notified that consenting to participate includes consent for the authors to publish aggregated results without disclosing individual identities or personal information. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by the Upholding Active Learning Reform in STEM (UALRS) initiative (NSF, #2142611).\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaecker, D. 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Making Connections: Student-Teacher Rapport in Higher Education Classrooms: Student-teacher rapport in higher education classrooms. \u003cem\u003eJournal of the Scholarship of Teaching and Learning\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(5), Article 5. https://doi.org/10.14434/josotl.v19i5.26578\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Students’ perceptions of instructor mindset, lay theories, universal and non-universal beliefs, ULTrA survey, undergraduate STEM, instructional policies, student-centered syllabi, syllabus rubrics","lastPublishedDoi":"10.21203/rs.3.rs-7190396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7190396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe way students perceive their instructor's mindset has been linked to student outcomes, such as engagement and performance, in STEM courses. However, the factors that influence such perceptions are not yet understood, particularly in relation to the instructor\u0026rsquo;s teaching policies and demographic characteristics. To address this gap, we investigate how student perceptions of their instructors\u0026rsquo; universality beliefs (the belief that all students or only some students can reach their full potential in STEM) about student abilities vary based on instructional policies while considering student and instructor demographic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eStudent perceptions of the instructor's universal and non-universal beliefs about student abilities were collected using a portion of the Undergraduate Lay Theories of Abilities (ULTrA) survey (n\u0026thinsp;=\u0026thinsp;625). Teaching policies were characterized by adapting a rubric to assess the student-centeredness of instructors\u0026rsquo; syllabi of 24 STEM instructors (34 courses) in a demographically diverse research institution in the Southern United States. Our findings indicate that using more student-centered instructional policies in evaluating and assessing students is associated with a more positive perception of the instructor\u0026rsquo;s universality beliefs. However, when instructor demographics are introduced in the model, that association between instructors\u0026rsquo; policies and student perceptions is lost, with White instructors being perceived more positively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings indicate that student-centered instructional policies are associated with more positive perceptions of instructors' universal beliefs. Thus, adopting student-centered policies, particularly in evaluation and assessment, is a potential mechanism to enhance student perceptions of the learning environment, thereby increasing retention in STEM courses. In addition, our work identifies possible biases in student perceptions of the learning environment that extend beyond the instructors\u0026rsquo; adoption of student-centered policies, specifically in relation to instructor demographic identity. Instructional policies are no longer significant when the instructor's race is introduced in our model. White instructors are perceived more positively than their Black Indigenous People of Color (BIPOC) peers. Thus, other contextual factors, such as verbal and non-verbal cues, cultural differences among faculty, or built-in systemic inequities, may contribute to shaping student perceptions and should be explored further.\u003c/p\u003e","manuscriptTitle":"Student-centered instructional policies are associated with more positive student perceptions of their instructor's universality beliefs, but these perceptions favor White instructors in STEM","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 21:51:05","doi":"10.21203/rs.3.rs-7190396/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"06684453-6d12-4b9a-9a5d-1e3175261287","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T21:08:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 21:51:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7190396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7190396","identity":"rs-7190396","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}