JOURNAL OF MICROBIOLOGY & BIOLOGY EDUCATION DOI: https://doi.org/10.1128/jmbe.v18i1.1235
Supplemental Materials for Student Buy-In Toward Formative Assessments: The Influence of Student Factors and Importance for Course Success Kathleen R. Brazeal and Brian A. Couch* School of Biological Sciences, University of Nebraska, Lincoln, NE 68588
Table of Contents (Total pages 5)
Appendix 1: FA survey items used in the present study Appendix 2: Numbers of students included in the study and total enrollment for each course Appendix 3: Additional information regarding definitions for predictor and outcome variables Appendix 4: Procedure for addressing potential collinearity between predictor variables
*Corresponding author. Mailing address: School of Biological Sciences, University of Nebraska, 204 Manter, Lincoln, NE 68588-0118. Phone: 402-472-8130. Fax: 402-472-2083. E-mail:
[email protected]. Received: 5 September 2016, Accepted: 25 January 2017, Published: 21 April 2017. ©2017 Author(s). Published by the American Society for Microbiology. This is an Open Access article distributed under the terms of the Creative Commons AttributionNoncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/ and https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.
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Appendix 1: FA survey items used in the present study. For all items, the parenthetical “[these FAs]” was substituted with the name of the FA. The survey directions also included a descriptive definition of the FA type customized for each course. To what extent do you agree or disagree with the following statements? (Likert choices: strongly disagree, disagree, neither agree nor disagree, agree, strongly agree) Overall, [these FAs] are beneficial to me. Overall, [these FAs] are beneficial to the instructor. [These FAs] help me identify what material I am expected to learn in this course. [These FAs] help me understand what it takes to be successful in this course. [These FAs] give me feedback on what I still need to learn in this course. [These FAs] help me take control of my own learning in this course. [These FAs] give the instructor feedback on how well students understand course material. How often do you discuss [these FA] questions with other students? (Likert choices: never, rarely, sometimes, often, always) What percentage of [these FA] questions: -are relevant to course content? (Scale of 0-100, in increments of 10) -challenge you to think more deeply about the course content? (Scale of 0-100, in increments of 10) How many of your previous high school and college courses have used [insert description of FA and/or similar FAs]? (Likert choices: 0 courses, 1-2 courses, 3-5 courses, 6-8 courses, more than 8 courses) Ideally, who do you think should be responsible for whether students learn in this course? (Scale from 0-100, in increments of 10, with “0 = The instructor is solely responsible for student learning in this course”, “50 = The instructor and the student are equally responsible for student learning”, and “100 = Students are solely responsible for their own learning in this course”.)
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Appendix 2: Numbers of students included in the study and total enrollment for each course Number in the Total Courses study1 enrollment Introductory courses 1 145 249 2 131 261 3 162 249 4 157 235 5 64 113 6 181 228 7 98 139 Non-introductory courses 8 156 231 9 35 103 10 12 36 11 9 26 12 32 57 1 Represents the number of students who completed the FA survey, consented to have their data released, and who had complete demographic data.
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Appendix 3: Additional information regarding definitions for predictor and outcome variables. Race/ethnicity: Under-represented minority (URM) students (i.e., Black, Hispanic, Hawaiian/Pacific Islander, American Indian/Alaska Native) were compared with a non-URM reference group, including White, Asian, and International students. Generation status: Students were considered first-generation if neither of their parents received a bachelor’s degree, while continuing-generation students had one or both parents with bachelor’s degrees. High school location: We used a lookup tool (www.usac.org) to designate each high school as rural or urban based on Federal Communication Commission definitions. A rural high school was defined as being located in or near a town of less than 25,000. The comparison group consisted of students attending urban high schools along with a small number with other high school backgrounds (i.e., home school, online high school, GED, or international high school). Major: The reference group included all students majoring in subjects related to the life sciences, which was compared to all other students (i.e., non-life science majors) who came from a range of other majors, including other STEM disciplines, non-STEM disciplines, and undeclared students. Class rank: We included class rank as a covariate using the numerical values 1-4 to indicate first-year, sophomore, junior, and senior students. Previous academic performance: We calculated a z-score of a student’s GPA upon entrance to the course. For firstyear students, we calculated separate z-scores using high school GPAs to account for differences in grade distributions from high school to college. Z-scores represent the number of standard deviations a value is above or below the mean. Course grades: We represented course grades with a numerical scale in which A+ was equivalent to 4.33, A to 4.00, A- to 3.66, B+ to 3.33, B to 3.00, and so on. Students who received an F or dropped the course were assigned the same score on the scale (0.33).
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Appendix 4: Procedure for addressing potential collinearity between predictor variables. Pairwise Pearson correlations were calculated for all possible predictor variable combinations. Variables with pairwise correlations above 0.3 were subjected to additional scrutiny within the general linear models. If both variables were statistically significant, we left them in the model since collinearity was not preventing either variable from being significant. If one or both variables were not significant, we ran separate models to determine whether each of the correlated variables was significant when the other was excluded from the model. In following these guidelines, we determined that collinearity between our variables was not preventing detection of significant effects.
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