A race re-imaged, intersectional approach to academic mentoring: Exploring the perspectives and responses of womxn in science and engineering research

Villanueva, Idalis; Stefano, Marialuisa Di; Gelles, Laura A.; Osoria, Paul Vicioso; Benson, Sheree

doi:10.1016/j.cedpsych.2019.101786

Cited by 22 publications

(20 citation statements)

References 64 publications

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“…Data-driven approaches thus utilize bottom-up frameworks centered on relationships instead of top-down frameworks driven by explanations and causality (Qiu et al, 2018). It is not surprising that data-driven approaches have increased in popularity as more and more data is created as part of our daily lives (Gero & Milanovic, 2020;Villanueva, Di Stefano, et al, 2019), which also lessens the need for experiments that control for confounds and the influence of covariates. Instead, data-driven approaches accommodate for the lack of control in data generation and collection through sheer numbers and advanced computational power (Lazer et al, 2009).…”

Section: Person-centered Approachesmentioning

confidence: 99%

New Epistemological Perspectives on Quantitative Methods: An Example Using Topological Data Analysis

Godwin

Benedict

Rohde

et al. 2021

Studies in Engineering Education

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Background: Education researchers use quantitative methodologies to examine generalizable correlational trends or causal mechanisms in phenomena or behaviors. These methodologies stem from (post)positivist epistemologies and often rely on statistical methods that use the means of groups or categories to determine significant results. The results can often essentialize findings to all members of a group as truth knowable within some quantifiable error. Additionally, the attitudes and beliefs of the majority (i.e., in engineering, White cis men) often dominate conclusions drawn and underemphasizes responses from minoritized individuals. In recent years, engineering education research has pursued more epistemologically and methodologically diverse perspectives. However, quantitative methodologies remain relatively fixed in their fundamental epistemological framings, goals, and practices. Purpose: In this theory paper, we discuss the epistemic groundings of traditional quantitative methods and describe an opportunity for new quantitative methods that expand the possible ways of framing and conducting quantitative research-person-centered analyses. This article invites readers to re-examine quantitative research methods. Scope: This article discusses the challenges and opportunities of novel quantitative methods in engineering education, particularly in the limited epistemic framings associated with traditional statistical methods. The affordances of person-centered analyses within different epistemological paradigms and research methods are considered. Then, we provide an example of a person-centered method, topological data analysis (TDA), to illustrate the unique insights that can be gained from person-centered analyses. TDA is a statistical method that maps the underlying structure of highly dimensional data. Discussion/Conclusions: This article advances the discussion of quantitative methodologies and methods in engineering education research to offer new epistemological approaches. Considering the foundational epistemic framings of quantitative research can expand the kinds of questions that can be asked and answered. These new approaches offer ways to conduct more interpretive and inclusive quantitative research.

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Section: Person-centered Approachesmentioning

confidence: 99%

New Epistemological Perspectives on Quantitative Methods: An Example Using Topological Data Analysis

Godwin

Benedict

Rohde

et al. 2021

Studies in Engineering Education

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“…Compassionate mentors recognize how intersectionality affects individuals' experiences and look for ways to reduce pressure on individuals to explain their adversity. Adaptive, compassionate mentorship also requires carefully identifying when to apply pressure and when to give mentees space ( Villanueva et al, 2019 ). These concerns should drive mentors and mentees to co-develop, revisit, and reflect on work plans that reduce stress rather than relying on mentees to express needs during and beyond the pandemic ( Manolis et al, 2009 ).…”

Section: Reframing Mentorship Prioritiesmentioning

confidence: 99%

Mentorship, equity, and research productivity: lessons from a pandemic

Nocco

McGill

MacKenzie

et al. 2021

Biological Conservation

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The coronavirus pandemic is more fully exposing ubiquitous economic and social inequities that pervade conservation science. In this time of prolonged stress on members of the research community, primary investigators or project leaders (PLs) have a unique opportunity to adapt their programs to jointly create more equitable and productive research environments for their teams. Institutional guidance for PLs pursuing field and laboratory work centers on the physical safety of individuals while in the lab or field, but largely ignores the vast differences in how team members may be experiencing the pandemic. Strains on mental, physical, and emotional health; racial trauma; familial responsibilities; and compulsory productivity resources, such as high-speed internet, quiet work spaces, and support are unequally distributed across team members. The goal of this paper is to summarize the shifting dynamics of leadership and mentorship during the coronavirus pandemic and highlight opportunities for increasing equity in conservation research at the scale of the project team. Here, we (1) describe how the pandemic differentially manifests inequity on project teams, particularly for groups that have been structurally excluded from conservation science, (2) consider equitable career advancement during the coronavirus pandemic, and (3) offer suggestions for PLs to provide mentorship that prioritizes equity and wellbeing during and beyond the pandemic. We aim to support PLs who have power and flexibility in how they manage research, teaching, mentoring, consulting, outreach, and extension activities so that individual team members' needs are met with compassion and attention to equity.

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“…Multiple layers of the phenomena include both the context and nature of discursive practices while also accounting for the convergence and divergence of its sensing modalities (Archer & Newfield, 2014). Together, the layers and immediacies of multi‐modal approaches allow scholars to further nuances in meaning‐making as it relates to complex realities such as access, social justice, and equity (Djonov & Zhao, 2013), race (e.g., Mills & Unsworth, 2018), ethnicity (e.g., Lewis Ellison & Esposito, 2021), gender and sexuality (e.g., Leppänen & Tapionkaski, 2021), and intersectionality (e.g., Bagga‐Gupta, 2012; Mejia et al, 2018; Villanueva, Di Stefano, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Situating multi‐modal approaches in engineering education research

Villanueva

Anwar

2022

J of Engineering Edu

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BACKGROUNDTraditionally, engineering education researchers rely on quantitative, qualitative, and mixed-or multi-method approaches for their research designs, each with its nuances, set of rules, and worldviews. Here we present a new approach that is yet to be widely accepted in engineering education research (EER): namely the multi-modal approach. This guest editorial is particularly timely because, in 2020, the EER taxonomy underwent a revision (Version 1.2), where the term "multi-modal approaches" was added to Section 12.d.iv (Finelli, 2020). With this addition to the taxonomy, it is important for EER scholars to understand what multi-modal approaches entail and how they are different from other approaches present in mixed-methods or multi-methods. To clarify this new approach further, we give examples of how multi-modal research is used in EER and other related studies. | INTRODUCTIONRooted in sociolinguistic approaches, multi-modality centers on multiple modes of communication and representation (e.g., reading, writing, and speech) to study meaning-making. In multi-modality, meaning-making includes multiple dynamics or representations of phenomena (or layers) and sensing modes by which an individual recognizes or becomes aware of the complexity of experienced phenomena (e.g., Ledin & Machin, 2017). Multiple sensing modalities are what give an individual "a wealth of information to support interaction with the world and with one another" (Turk, 2014, p. 189), and these can be interpreted through senses such as, but not limited to, hearing, seeing, touching, and feeling (e.g., Ledin & Machin, 2017). Phenomena, in the multi-modal sense, include the "binding of inputs from multiple sensory modalities and the effects of this binding" (Lachs, 2017) on individuals and accounts for the influence that one sensory modality has on another (Spence et al., 2009). Taken together, multi-modal approaches aim to capture the multi-layered and near-real-time ensemble of the intersections and dynamics of meaning-making (e.g., Archer & Newfield, 2014).Take, for example, the experience of listening to a song. When a person listens to a song, multiple events happen almost simultaneously: a person hears the song, reads the song lyrics, and thinks about how the song applies to their life. If the song connects with a person on a deeper level, their emotions manifest as excitement or joy, their heart palpitates, their mood changes, and they almost immediately begin to memorize the lyrics and tunes. Analogously, multimodal approaches try to collect many simultaneous events in order to represent the messiness and immediacy of life.Multiple layers of the phenomena include both the context and nature of discursive practices while also accounting for the convergence and divergence of its sensing modalities (Archer & Newfield, 2014). Together, the layers and immediacies of multi-modal approaches allow scholars to further nuances in meaning-making as it relates to complex realities such as access, social justice, and equity (

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A race re-imaged, intersectional approach to academic mentoring: Exploring the perspectives and responses of womxn in science and engineering research

Cited by 22 publications

References 64 publications

New Epistemological Perspectives on Quantitative Methods: An Example Using Topological Data Analysis

New Epistemological Perspectives on Quantitative Methods: An Example Using Topological Data Analysis

Mentorship, equity, and research productivity: lessons from a pandemic

Situating multi‐modal approaches in engineering education research

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