DOI: 10.5061/dryad.mgqnk996c
title: Data from: More than a token photo: humanising scientists enhances student engagement

description: Despite broad consensus that highlighting counter-stereotypical scientist
 role models in educational materials promotes equity and success, the
 specific elements that make these materials effective remain untested. Are
 pictures of counter-stereotypical scientists enough to communicate to
 students that scientists come from a variety of backgrounds, or is
 additional information required? To parse the effects of including visual
 depictions and humanising information about scientists featured in biology
 course materials, we distributed three randomised versions of assignments
 over several academic terms across 36 undergraduate institutions (N
 &gt; 3,700 students). We found that the inclusion of humanising
 information about scientists was key to increasing student engagement with
 the biology course materials. Structural equation modelling revealed that
 the extent to which students related to scientists mediated the positive
 effect of humanising descriptions on student engagement. Further, our
 results were strongest among students who shared one or more excluded
 identity(s) with the featured scientists. Our findings underscore the
 importance of providing students with examples of humanised and relatable
 scientists in classrooms. Rather than simply adding a photo to increase
 representation, showcasing scientists as actual people (enjoying hobbies,
 experiencing setbacks, etc.) is a promising intervention to engage
 students with identities systemically excluded from biology. 
description: These data describe survey responses from students immediately
 after working through a quantitative biology activity that featured a
 counter-stereoyptical scientist in one of three ways: 1) provided no
 information about the scientist (the control treatment), 2) included
 photos of the scientist (the visual treatment), or 3) included both photos
 and interview questions answered by the scientist about their experiences
 as a counter-stereotypical scientist (the humanizing treatment). Students
 in biology courses taught by 43 different instructors at 36 different US
 undergraduate universities and colleges participated in the study. Over
 the duration of the course, students engaged with three different
 quantitative biology activities from a single treatment and were surveyed
 after each activity. Our study included a total of 12 different
 quantitative biology activities, and the specific activities implemented
 in each course were chosen by the instructor to best match the course
 content. We collected demographic information from students and created
 student-scientist demographic matching categories based on their
 self-reported gender and race/ethnicity identities. The survey data are
 organized into two different datasets: the quantitative and qualitative
 datasets. The quantitative dataset includes student responses to survey
 items on a 7-point scale. The qualitative dataset includes codes for how
 students responded to the open-ended prompt, "Describe how you
 related to the featured scientist in the activity, if at all." The
 datasets were error-checked to remove all surveys that could not be
 assigned to a student. All students that did not consent to participate in
 the study were removed from the datasets.
description: # More than a token photo: humanising scientists enhances student
 engagement
 [https://doi.org/10.5061/dryad.mgqnk996c](https://doi.org/10.5061/dryad.mgqnk996c) These data describe survey responses from students immediately after working through a quantitative biology activity that featured a counter-stereotypical scientist in one of three ways: 1) provided no information about the scientist (the control treatment), 2) included photos of the scientist (the visual treatment), or 3) included both photos and interview questions answered by the scientist about their experiences as a counter-stereotypical scientist (the humanizing treatment). # Description of the data and file structure The survey data are organized into two different datasets: the quantitative dataset (TokenPhotoQuantitative_20240404.csv) and the qualitative dataset (TokenPhotoQualitative_20241010.csv). The quantitative dataset includes student responses to survey items on a 7-point scale, whereas the qualitative dataset includes codes for how students responded to the open-ended prompt, "Describe how you related to the featured scientist in the activity, if at all." Below is a description of the structure of these two datasets. ### *Quantitative Dataset* StudentID: A unique ID was assigned to each student to protect student identity. A student may be represented in the dataset up to three times, as students engaged with a maximum of three different quantitative biology activities during their course. All students who did not consent to participate in the study were removed from the dataset. StudentID_DN: A composite variable, combining StudentID (described above) and DN (described below). InstructorID: A unique ID was assigned to each instructor to protect instructor identity. DN: The name of the quantitative biology activity that the student engaged with. The quantitative biology activities used in this study were Data Nugget (DN) activities. The twelve different Data Nugget activities used in this study can be freely downloaded at [https://datanuggets.org/dataversify/](https://datanuggets.org/dataversify/) Treatment: "Control", "Visual", "Humanizing", representing the three treatments used in this study. Every student experienced one of these treatments. When working through a quantitative biology activity, students were either not provided with information about the featured scientist ("Control"), provided with photos of the featured scientist ("Visual"), or provided with photos and interview questions answered by the featured scientist about their experiences as a counter-stereotypical scientist ("Humanizing"). Matching: "Non-Excluded", "Excluded, Double Match", "Excluded, Single Match", or "Excluded, No Match", representing the four student-scientist demographic matching categories. We created these categories using self-reported gender and race/ethnicity identities. Our categories focus on excluded identities, which we define as non-cis-man genders and non-white race. If the student did not posses an excluded identity (i.e., was a white cis-gender man), that student was included in the "Non-Excluded" category. If the student held excluded gender and/or race/ethnicity identity(s), that student either shared both identities ("Excluded, Double Match"), shared only one identity ("Excluded, Single Match"), or did not share those identities ("Excluded, No Match") with the featured counter-stereotypical scientist. More information about how these categories were assigned is included in the Supplementary Text of the manuscript. Missing data indicates the student did not fill out their demographic information. Relatability: Student response on a 7-point sliding scale, ranging from 1 (not at all) to 7 (very) to the survey item, "If a scientist was mentioned, to what extent did you relate to the featured scientist in the activity?" Missing data indicates the student did respond to the survey item. Engagement_1: Student response on a 7-point sliding scale, ranging from 1 (not at all) to 7 (very) to the survey item, "How hard were you working on the activity?" For more information on this survey construct to measure student engagement, please reference doi:10.1002/tea.21409. Missing data indicates the student did respond to the survey item. Engagement_2: Student response on a 7-point sliding scale, ranging from 1 (not at all) to 7 (very) to the survey item, "How well were you concentrating the activity?" For more information on this survey construct to measure student engagement, please reference doi:10.1002/tea.21409. Missing data indicates the student did respond to the survey item. Engagement_3: Student response on a 7-point sliding scale, ranging from 1 (not at all) to 7 (very) to the survey item, "How important was the activity to you?" For more information on this survey construct to measure student engagement, please reference doi:10.1002/tea.21409. Missing data indicates the student did respond to the survey item. Engagement_4: Student response on a 7-point sliding scale, ranging from 1 (not at all) to 7 (very) to the survey item, "How important was the activity to your future?" For more information on this survey construct to measure student engagement, please reference doi:10.1002/tea.21409. Missing data indicates the student did respond to the survey item. Engagement_5: Student response on a 7-point sliding scale, ranging from 1 (not at all) to 7 (very) to the survey item, "Was the activity interesting?" For more information on this survey construct to measure student engagement, please reference doi:10.1002/tea.21409. Missing data indicates the student did respond to the survey item. Engagement_6: Student response on a 7-point sliding scale, ranging from 1 (not at all) to 7 (very) to the survey item, "Did you enjoy the activity?" For more information on this survey construct to measure student engagement, please reference doi:10.1002/tea.21409. Missing data indicates the student did respond to the survey item. Engagement_7: Student response on a 7-point sliding scale, ranging from 1 (not at all) to 7 (very) to the survey item, "How engaging did you find the activity?" For more information on this survey construct to measure student engagement, please reference doi:10.1002/tea.21409. Missing data indicates the student did respond to the survey item. ### *Qualitative Dataset* StudentID: A unique ID was assigned to each student to protect student identity. A student may be represented in the dataset up to three times, as students engaged with a maximum of three different quantitative biology activities during their course. All students who did not consent to participate in the study were removed from the dataset. StudentID_DN: A composite variable, combining StudentID (described above) and DN (described below). InstructorID: A unique ID was assigned to each instructor to protect instructor identity. DN: The name of the quantitative biology activity that the student engaged with. The quantitative biology activities used in this study were Data Nugget (DN) activities. The twelve different Data Nugget activities used in this study can be freely downloaded at [https://datanuggets.org/dataversify/](https://datanuggets.org/dataversify/) Treatment: "Control", "Visual", "Humanizing", representing the three treatments used in this study. Every student experienced one of these treatments. When working through a quantitative biology activity, students were either not provided with information about the featured scientist ("Control"), provided with photos of the featured scientist ("Visual"), or provided with photos and interview questions answered by the featured scientist about their experiences as a counter-stereotypical scientist ("Humanizing"). Matching: "Non-Excluded", "Excluded, Double Match", "Excluded, Single Match", or "Excluded, No Match", representing the four student-scientist demographic matching categories. We created these categories using self-reported gender and race/ethnicity identities. Our categories focus on excluded identities, which we define as non-cis-man genders and non-white race. If the student did not posses an excluded identity (i.e., was a white cis-gender man), that student was included in the "Non-Excluded" category. If the student held excluded gender and/or race/ethnicity identity(s), that student either shared both identities ("Excluded, Double Match"), shared only one identity ("Excluded, Single Match"), or did not share those identities ("Excluded, No Match") with the featured counter-stereotypical scientist. More information about how these categories were assigned is included in the Supplementary Text of the manuscript. Missing data indicates the student did not fill out their demographic information. StudentResponse: Open-ended student responses to the prompt, ""Describe how you related to the featured scientist in the activity, if at all." Sensitive information that was included in the student responses has been redacted, and some answers have been modified slightly to remove identifying information. ExcludedIdentities: One of the codes used to describe student responses to the open-ended prompt, "Describe how you related to the featured scientist in the activity, if at all." A "1" indicates that the student response mentioned relating to the excluded identity(s) held by the featured scientist. A "0" indicates that the student response did not mention relating to the excluded identity(s) held by the featured scientist. For the full codebook, see the Supplementary Text of the manuscript. HumanizingInfo: One of the codes used to describe student responses to the open-ended prompt, "Describe how you related to the featured scientist in the activity, if at all." A "1" indicates that the student response mentioned relating to the humanizing information provided about the featured scientist. A "0" indicates that the student response did not mention relating to the humanizing information provided about the featured scientist. For the full codebook, see the Supplementary Text of the manuscript. ResearchInterests: One of the codes used to describe student responses to the open-ended prompt, "Describe how you related to the featured scientist in the activity, if at all." A "1" indicates that the student response mentioned relating to the research interests of the featured scientist. A "0" indicates that the student response did not mention relating to the research interests of the featured scientist. For the full codebook, see the Supplementary Text of the manuscript. DidNotRelate: One of the codes used to describe student responses to the open-ended prompt, "Describe how you related to the featured scientist in the activity, if at all." A "1" indicates that the student response mentioned not relating to the featured scientist. A "0" indicates that the student response did not mention not relating to the featured scientist. For the full codebook, see the Supplementary Text of the manuscript. ## Code/Software The R code used to analyze these two datasets is provided. The packages required to run this script are tidyverse (version 2.0.0), lme4 ( version1.1-34), car (version 3.1-2), lavaan (version 0.6-16), piecewiseSEM (version 2.3.0), and emmeans (version 1.8.9).