Implicit biases operate at a non-conscious level and tend to affect minority and/or marginalized groups more and inhibit their growth in society. Long-standing biases tend to establish prejudices amongst even the most liberal communities and also operate in academic institutions. In particular, biases within academia are likely to negatively impact the career progression of particular groups. For example, discrimination against faculty based on characteristics such as gender, race, and age. This Research Topic invites researchers to investigate the complex dynamics of implicit bias in the educational sector. Aiming to raise awareness, stimulate critical reflection, and offer strategies for mitigating these biases.
Given the rise in popularity of technology-mediated education, we also seek submissions which document biases within the realm of online education. Including but not limited to: video-based classrooms, asynchronous delivery of teaching, and immersive learning environments such as Virtual and Augmented Reality. We encourage research submissions based on underrepresented samples.
Submissions may explore the manifestations of implicit bias, such as its impact on teacher expectations, administrative decisions, and curricular content. Findings may support educational reforms in the hiring and progression of academics in higher education. We are particularly interested in studying how implicit biases affect judgment, learning, and evaluation in an educational environment.
By contributing to this body of knowledge, researchers can help provide evidence-based strategies to identify and address implicit biases, ultimately empowering educational stakeholders to create more inclusive and equitable learning environments for all students.
The research topic will explore themes including but not limited to:
Student evaluations based on perceived learning and personality traits
Studying implicit biases in an online setting - This could be in various settings, such as synchronous online platforms (Zoom, MS Teams), asynchronous forms of self-paced learning (YouTube), or even classes delivered in social VR scenarios (AltSpaceVR, Horizon Worlds, VRChat, or customized environments)
Exploring different biases based on race, age, gender, disability, and class
Evaluations for hiring and progression amongst minority faculty
Reviewing articles which describe recent advances in the field
Research using immersive or innovative technologies such as VR/AR in their research design
Exploring diversity biases in the labor market with respect to hiring faculty
Career opportunities for future generations of minorities and/or marginalized communities in higher education.
Authors may submit original research, experimental studies, systematic reviews, and case studies involving quantitative, qualitative, or mixed-method designs. Additionally, we also welcome studies that assess educational policies and suggest policy interventions to mitigate the impact of bias. Considering the lack of information regarding such topics within understudied samples, we also encourage authors to conduct replications of previous studies in the field.
Implicit biases operate at a non-conscious level and tend to affect minority and/or marginalized groups more and inhibit their growth in society. Long-standing biases tend to establish prejudices amongst even the most liberal communities and also operate in academic institutions. In particular, biases within academia are likely to negatively impact the career progression of particular groups. For example, discrimination against faculty based on characteristics such as gender, race, and age. This Research Topic invites researchers to investigate the complex dynamics of implicit bias in the educational sector. Aiming to raise awareness, stimulate critical reflection, and offer strategies for mitigating these biases.
Given the rise in popularity of technology-mediated education, we also seek submissions which document biases within the realm of online education. Including but not limited to: video-based classrooms, asynchronous delivery of teaching, and immersive learning environments such as Virtual and Augmented Reality. We encourage research submissions based on underrepresented samples.
Submissions may explore the manifestations of implicit bias, such as its impact on teacher expectations, administrative decisions, and curricular content. Findings may support educational reforms in the hiring and progression of academics in higher education. We are particularly interested in studying how implicit biases affect judgment, learning, and evaluation in an educational environment.
By contributing to this body of knowledge, researchers can help provide evidence-based strategies to identify and address implicit biases, ultimately empowering educational stakeholders to create more inclusive and equitable learning environments for all students.
The research topic will explore themes including but not limited to:
Student evaluations based on perceived learning and personality traits
Studying implicit biases in an online setting - This could be in various settings, such as synchronous online platforms (Zoom, MS Teams), asynchronous forms of self-paced learning (YouTube), or even classes delivered in social VR scenarios (AltSpaceVR, Horizon Worlds, VRChat, or customized environments)
Exploring different biases based on race, age, gender, disability, and class
Evaluations for hiring and progression amongst minority faculty
Reviewing articles which describe recent advances in the field
Research using immersive or innovative technologies such as VR/AR in their research design
Exploring diversity biases in the labor market with respect to hiring faculty
Career opportunities for future generations of minorities and/or marginalized communities in higher education.
Authors may submit original research, experimental studies, systematic reviews, and case studies involving quantitative, qualitative, or mixed-method designs. Additionally, we also welcome studies that assess educational policies and suggest policy interventions to mitigate the impact of bias. Considering the lack of information regarding such topics within understudied samples, we also encourage authors to conduct replications of previous studies in the field.
