Data from: More than a token photo: humanising scientists enhances student engagement
Data files
Nov 20, 2024 version files 1.70 MB
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README.md
11.30 KB
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TokenPhotoQualitative_20241010.csv
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TokenPhotoQuantitative_20240404.csv
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Abstract
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 > 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.
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/
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/
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).
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.