127 Matching Annotations
  1. Last 7 days
    1. 80.5619.44 75.00 25.0041.67JSTE1.46539.47 60.5382.4617.54 66.67 33.3326.32RISE2.24852.94 47.0611.7688.24 80.39 19.6119.61Abbreviations: CSSE,Cultural Studies for Science Education; IJSE,International Journal of Science Education; JRST,Journal ofResearch in Science Teaching; JSTE,Journal of Science Teacher Education; RISE,Research in Science Education.BANCROFTET AL.|1241 1098237x, 2022, 5, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/sce.21733 by Univ of Colorado Health Science Center, Wiley Online Library on [03/04/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

      Interesting numbers, a little skewed, wondering about the impact.

    2. double‐blind peer‐review process. Each of these approaches to peer‐review and their advantages and disadvantages are presented later in the paper.

      Looking forward to reading more about this and how it is done

    3. ANALYSIS

      Pretty straightforward data collection, simply using the information provided on the website of the journals, then synthesizing results.

    4. The research questions (RQs) guiding this paper are:1. How does the described peer‐review process compare across well‐established science education journals?2. What is the extent of the diversity of the editors and editorial boards across well‐established science educationjournals using peer review? To what extent are scholars who pursue equity‐focused research represented?3. How do the included journals support reviewers' ability to provide a fair review?

      I'm interested in their methodology and data collection to answer their RQ

    5. Homophily is a tendency of reviewers to positively review scholarship of authors who are in their same socialgroup

      definition. Both of these terms are interrated and make sense in the context of this research

    6. likely thriving compared to their male counterparts indisseminating their research in venues where peer review acts as a gatekeeper.

      A word of warning

    7. women represent 75.5% of 2020 doctoral recipients in the fieldof science education

      That is a very high percentage of women, followed by the stat that men faculty outnumber women two to one.

    8. Unbiased evaluation of the manuscript can be promoted in the peer‐review process when editors assignreviewers who use similar epistemologies

      How do they make the researchers or authors unidentifiable?

  2. Mar 2026
    1. Although research suggests that social-emotional qualities have a positive influence on academic achievement, most related studies examine these qualities in relation to outcome measurement and prediction, and more work is needed to develop interventions based on this research (Levin 2013)

      I wonder what these look like...how can classroom teachers, who already do it all, do even more.

    2. two research goals are priorities for studies of social and emotional learning: 1. Developing assessment techniques, 2. Providing intervention approaches.

      Research goals stated here

    3. The movement involves some complex issues ranging from the establishment of social and emotional learning standards to the development of social and emotional learning programs for students, and to the offering of professional development programs for teachers, and to the carrying out of social and emotional learning assessments (Kamenetz 2015).

      We have a big push as our school for SEL. Wondering big this movement will go and how effective it will be on students.

    4. The field of statistics is the core building block of DS theory and practice, and many of the techniques for extracting knowledge from data have their roots in this.

      Definitions and situating data science

    5. The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education.

      Interesting model and call to action to be using it within education

    1. implicit biases re-lated to the values stemming from which systems of ethics we prioritize

      This is something every reearcher needs to be aware of!

    2. KST: Shí éí Kinłichii’nii nishłi ̧′dóó Naakai diné’e bashishchiin. Tòdichii’niidashicheii dóó Tłizi’łaní dashinalí. ̇Akót’éego diné asdzáán nishłi ̧′. (Englishtranslation: I am Red House clan, born for the Mexican Peoples clan. Mymaternal grandfather’s clan is Bitterwater and my paternal grandfather’sclan is Many Goats. In this way, I am a Navajo woman.

      Beautiful.

    3. hí éí Ilocano nishłi ̧′dóó Ta’chii nii bashishchiin. Ilocano dashicheiidóó Tsi’naajinii dashinalí. ̇Akót’éego diné nishłi ̧′. (English translation: I amIlocano/Filipinx, born for the Red Running into the Water clan. My mater-nal grandfather is Ilocano and my paternal grandfather’s clan is Black Streakin the Forest.)

      Love this intro

    4. he intersections sections at the end of each dialog are intendedto highlight the themes that we collectively recognized in our own answers.

      Wondering if this is going to drive coding.

    5. researchers towards considerations of a data science education that is equity-minded both in concept and practice

      I like how they clearly state what to expect

    6. uoethnography — a research method in which prac-titioners discursively interrogate the relationships between culture, context, andthe mechanisms which shape individual autobiographical experience

      I have never heard of this methodology

    1. Black x Male to identify a Black male, instead of looking at that person’s identities in isolation

      There must be more math here than what I am used to, to use this method for intersectionality.

    2. Giving communities a voice in creating and shaping that data is essential for using data with a social justice orientation

      This is something I didn't think of when centering on QuantCrit.

    3. Repositioning black girls in mathematics disposition research: New perspectives from QuantCritRACE ETHNICITY AND EDUCATION5

      I want to see this one as well.

    4. A QuantCrit analysis of the Black teacher to principal pipeline

      I want to look up this one. I was just talking to a teacher today about the importance of students being able to see themselves in their teachers.

    1. extensive gap between Black students and either White or Asian and International students in their ability to persist.

      Why? Reminds me of the other reading about using Ladson Billings concept of education debt and the clear racism that has historically created these systems of oppression. It's sad

    2. Black, first-generation males have the lowest probability of persisting in any major is supported by research on Black male retention in higher education literature

      Heartbreaking- "any major"

    3. The students in the sample have a slightly higher SAT critical reading score than mathematics score, have a mean cumulative grade point average (GPA) of almost 3, and a mean total number of semestersof about seven.

      Interesting numbers here! I would not have guessed that SAT reading scores were higher than math.

    4. high school grade point average

      I understand the rationale for choosing this stat for comparison, but being the one who is in charge of GPA, grades are so arbitrary and can vary greatly.

    5. 16% of undergraduates in the USchose STEM as a career major, compared to higher percentagesfor other countries(up to 47% in China), which can not only be detrimental to the USeconomy, but to the global market (National Science Board, 2010

      I have not heard this statistic before. I would be interested in a more recent percentage of students choosing STEM. Also, questions about what qualifies as STEM career

    6. QuantCrit theory, we use multilevel modelsto determine factors that predicted persistence in any major and factors that predicted persistence in STEM. We also use marginal effects to explore the intersection ofethnicity, gender, and first-generation status

      Again, interesting to see how these are applied in the analysis and findings sections.

    1. Society’s educational debts before instruction were largeenough that women and Black men’s average posttest scoresdid notreach the White men’s average pretest scores.

      Interesting finding, but it makes sense given the education debts of the others.

    2. ismiss meaningfulinequities due to lack of representation in minoritizedgroups.28Instead, we used the standard errors of the estimatedscores to inform our confidence in the results

      I like how the researchers are explicitly addressing and applying the tenets to the analysis.

    3. dson-Billings’11,12concept of“educational debts”to interpret the extent towhich college chemistry courses mitigate, perpetuate, orexacerbate the educational debts society owes to studentsfrom groups minoritized13by racism and sexism.

      Looking forward to seeing how this is used in the analysis and finding sections.

    4. QuantitativeCritical (QuantCrit) perspective that framed inequities as educa-tional debts that society owed students due to racism, sexism, orboth. Results showed that society owed women and Black menlarge educational debts before and after instruction. Society’seducational debts before instruction were large enough that womenand Black men’s average scores were lower than White men’s average pretest scores even after instruction. Society would have toprovide opportunities equivalent to taking the course up to two and a half times to repay the largest educational debts. Thesefindings show the scale of the inequities in the science education systems and highlight the need for reallocating resources andopportunities throughout the K−16 education system to mitigate, prevent, and repay society’s educational debts from sexism andracism

      This is the first research I have read that explicitly names QuantCrit as a framework for analyzing the data. I'm interested to see how the tenets show up in the analysis/findings sections

    1. I cannot annotate and add a comment. Here are my notes: -Crit Quant Literacy is a means to apply QuantCrit. These work hand in hand. Critical Quant Literacy – paradigm for teaching, learning, understanding, and applying. CQL is a “critically informed understanding of the scope of quantitative methodology including statistical research design, definitions, variables, methods and findings. “

      QuantCritRT- “specifically a quantitative instantiation of CRT.” 5 tenets (centrality of racism, non-neutrality of numbers, non-natural categories, number do not and can not speak for themselves, using number for social justice. Quant findings are objective and can reinforce systems of privilege and oppression.

      Interesting notes about mathematics vs philosophical assumptions. Unpacking the design of the study, language, CQL is a precursor-a way of thinking about the reading, understanding, and contextualizing quant methods.

      Questions to guide CQL: design, methodology, measurement

    1. QuantCrit principles, and the examples we have set out above, will go some way tosupporting greater critical scrutiny of quantitative data and the potential to harness its status in the cause of social justice

      These principles should be a part of every college experience. Americans would benefit from asking critical questions about the data being presented and questioning who it benefits.

    2. Critical race theorists work simultaneously withand againstrace, (i.e.,we know that race only exists as a social construct, but we recognize the,sometimes murderous power of the fiction and seek to engage, resist and ultimately destroy race/racism). Similarly, QuantCrit should work withand againstnumbers by engaging with statistics as a fully social aspect of how race/racism is constantly made and legitimated in society.

      This is an interesting way of thinking about it, working with and against race.

    3. Social relationships are hugely complex and fluid; they do not easily translate into simple categories and effects that are easily quantified.

      Connects to data collection and analysis. Also, it connects to the other tenets of QuantCrit when thinking about the narratives, counternarratives, intersectionality, etc.

    4. peopleare used to judging the trustworthiness of qualitative data

      This is so true, people just believe what they hear, they don't ask questions or try to find sources.

    5. ‘Crime Statistics Bureau’ doesn’t exist.

      The problem is, Trump makes up shit and people believe it! News agencies need to fact-check and accurately report data.

    1. We also know that researchers are encouraged to search for “silver bullets” or universal approaches in their work. In fact, we still fail at upholding all of the recommendations we have offered. However, understanding the value that statistical practices have in equity policy initiatives, we are committed to working through present-day limitations that come with the quantification of human experiences. By being upfront where our work falls short, we get closer to discovering new analytical approaches that can be used for liberatory purposes.

      Great point, there may not be a true silver bullet, but we can uphold recommendations and highlight the work that needs to be heard.

    2. The foundational elements of QuantCrit are tied to critical race theory but are also aligned with other perspectives of the critical canon. Critical race theory explores where and how racism prevents people of color from accessing social and economic opportunities (Bell, 1995; Ladson-Billings, 2009). Critical race theorists are also interested in subverting defi-cit-framing projections by documenting the ways that people of color actively resist and cultivate joy despite racist structures

      Again, these theories are so important when researching education because it is a product of society and has been created to serve a purpose. Sadly, little has changed in the last 100 years.

    3. In contrast to postpositivism, a critical lens assumes that what can be known about the world is socially constructed. Critical theorists separate themselves from traditional theorists across fields (Bohman, 2005). Critical theories explore how his-torical events and society have shaped present-day experiences and understandings of how the world functions

      Critical theory is incredibly interesting and always relevant within education systems.

    4. these devastating and brutal events reflect under-lying systemic inequities that exist across the globe

      I see someone also commented on this two years ago, it's sad that now, there is more brutality than ever targeting marginalized groups.

    1. socially constructed and fluid.

      These demographics are just a way to distinguish between different groups of people in hopes of continuing to keep some in power. They are social constructs.

    2. ensure diversity in the narratives promoted.

      This is something I think about in my research. I want to be sure to capture the individual and story.

    3. intersection of these axes shapes an individual’s experience of the world.

      Very true and hard to grapple with and to take into consideration when determining "groups" to study and how to represent the data as people and human not just these social label imposed onto them.

    4. s that all data and analysis methods introduce biases and strives to minimize and explicitly discuss these biases.

      Being aware of your biases or any biases that could occur during the data analysis part is important to acknolwedge, discouse and explicitly discuss.

    5. QuantCrit researchers commit to disrupting narratives that frame minoritized students as deficient and disrupting oppressive systems through anti-racist and anti-sexist work.

      This reminds me of the study we read in another class about dis/ability students and their mathematical thinking. It is up to us to draw attention and disrupt the oppressive systems.

  3. Feb 2026
    1. I was unable to highlight sections and annotate so I'm just going to write a note.

      It will be good practice to write a research question, find datasets, create a plan and name potential impact. I nervous about the "comprehensive" part because we have not done this before and looking forward to feedack.

      In the rubric, I love the explicit tie to equity and public benefit. Narrowing in on the target audience could be a little difficult when you would think it would benefit all but zooming into who needs to hear it more.

      I'm a little nervous about the analytical methodologies because I don't know of a variety of methodologies let alone how to determine which would be best. Maybe we will find out more information about methodology strategies in upcoming weeks.

    1. Who benefits from data science and who is overlooked.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1A they/them Pollicino?

      Even in the first hospital example in the intro, this was also the case. Who benefits from overlooking groups?

    2. Seals Allers—and her fifteen-year-old son, Michael—are working on their own data-driven contribution to the maternal and infant health conversation: a platform and app called Irth—from birth, but with the b for bias removed (figure 1.8). One of the major contributing factors to poor birth outcomes, as well as maternal and infant mortality, is biased care. Hospitals, clinics, and caregivers routinely disregard Black women’s expressions of pain and wishes for treatment.81 As we saw, Serena Williams’s own story almost ended in this way, despite the fact that she is an international tennis star. To combat this, Irth operates like an intersectional Yelp for birth experiences. Users post ratings and reviews of their prenatal, postpartum, and birth experiences at specific hospitals and in the hands of specific caregivers. Their reviews include important details like their race, religion, sexuality, and gender identity, as well as whether they felt that those identities were respected in the care that they received. The app also has a taxonomy of bias and asks users to tick boxes to indicate whether and how they may have experienced different types of bias. Irth allows parents who are seeking care to search for a review from someone like them—from a racial, ethnic, socioeconomic, and/or gender perspective—to see how they experienced a certain doctor or hospital.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Amanda Christopher.

      "taxonomy of bias" love this term and didn't think about it as biased originally.

    3. This consists of asking who questions about data science: Who does the work (and who is pushed out)? Who benefits (and who is neglected or harmed)? Whose priorities get turned into products (and whose are overlooked)? .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Seyoon Ahn

      These are the key, love these questions and all data should be examined using these

    4. We use the term power to describe the current configuration of structural privilege and structural oppression.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }2A they/them Pollicino, Jiaqi Qin, in which some groups experience unearned advantages.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1111—because various systems have been designed by people like them and work for people them—and other groups experience systematic disadvantages—because those same systems were not designed by them or with people like them in mind. .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }211

      I like this definition

    5. examine power is the first principle of data feminism, and the focus of this chapter..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Khushi Loomba

      This is where my mind went when reading the intro, I'm interested to see how this impacted the data.

    6. On the contrary, the data showed that Black women with college degrees suffered more severe complications of pregnancy and childbirth than white women without high school diplomas.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }11..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }61212

      Why? I thought it would be systemic issues in access to healthcare and such, and this still could be the truth, but what else is at play?

    7. Black women are over 3 times more likely than white women.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }2Eshan Velidandla, Nishaan Chavla to die from pregnancy- or childbirth-related causes.”2.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1111

      I have heard this statistic before and it is mind boggling that this is real in 2026. Also, my brain now goes to systemic and oppressive systems that could contribute to this as well.

    1. et the field’s understanding of racial and ethnic dispari-ties in advanced STEM achievement during elementary school is currently limited. Relatively few studies of advanced STEM achievement have been conducted, particu-larly those using elementary school samples and longitudinal designs (Clotfelter et al., 2009; Davis-Kean & Jager, 2014; Gandara, 2005; Rambo-Hernandez et al., 2019). Of these, only two studies have examined racial and ethnic disparities in advanced STEM achievement as early as kindergarten in analyses of nationally representative data (Davis-Kean & Jager, 2014; Gandara, 2005). Neither study reported on explanatory factors for these disparities in adjusted analyses. Existing studies examining advanced STEM achievement have analyzed samples of middle or high school students (e.g., Kotok, 2017; Lubinski et al., 2014; McCoach & Siegle, 2003) or examined gender disparities (e.g., Penner & Paret, 2008; Robinson & Lubienski, 2011).

      They situate themselves within the existing research by stating that there have been few studies about STEM achievement have been longitudinal. Also, even fewer with a direct relationship were examined between access and other disparities in different forms.

    2. An antecedent-opportunity-propensity framework is a well-validated theory of achievement growth (Byrnes, 2020) hypothesizing that a relatively small set of student, family, and school factors explain racial and ethnic disparities in STEM achievement

      Theoretical framework used-haven't seen this before, interested in learning more about it.

    3. An antecedent-opportunity-propensity framework is a well-validated theory of achievement growth (Byrnes, 2020) hypothesizing that a relatively small set of student, family, and school factors explain racial and ethnic disparities in STEM achievement

      theoretical framework used.

    4. Addressing racial and ethnic underrepresentation in the sci-ence, technology, engineering, and mathematics (STEM) workforce is a national priority (American Society of Mechanical Engineers, 2021; National Academies of Sciences, Engineering, and Medicine [NASEM], 2011; National Science Foundation [NSF], 2021). Less than 10% of the U.S. STEM workforce is Black or Hispanic1 (Funk & Parker, 2018; National Science Foundation [NSF], 2019). White or Asian students are more likely to complete STEM college degrees (Steenbergen-Hu & Olszewki-Kubilius, 2017). Less than 1% of those with a bachelor’s degree in sci-ence or engineering are American Indian, Native American, or Pacific Islanders (AINAPI). The contrasting percentages for those who are White are 57% and 64% (NSF, 2021). The nation’s economic competitiveness and scientific innovation is constrained by racial and ethnic underrepresentation in the STEM workforce (Bell et al., 2019; NASEM, 2011). The earning potential of high-achieving adults of color is also constrained. High-achieving college students of color major-ing in STEM report early career earnings that are 26% to 40% higher than closely matched counterparts majoring in other fields (Melguizo & Wolniak, 2012).

      Bigger issue the research is trying to address.

    5. Research Question 1: Are Black, Hispanic, or AINAPI students less likely than White students to display advanced science or mathematics achievement during elementary school? If so, how large are the observed gaps?Research Question 2: Do antecedent, opportunity, and propensity factors explain the lower likelihoods that Black, Hispanic, or AINAPI students display advanced science or mathematics achievement during elementary school?

      Research questions. Both questions are related to showing achievement and looking for an explanation as to why they are not.

    1. Greater use of special education data may help prevent future systematic failures to identify and serve eligible students with disabilities

      Love this idea and it's important that is it still executed and reflected properly.

    2. percent of students in special education is negative and significant

      System oppression and racism at play? Historically underrepresented communities?

    3. special education accountability system to reduce the number of students identified as children with disabilities.

      Another example of misuse of data. Who would this under-identification benefit?

  4. Jan 2026
    1. So theprisoners are kept in the dark as much as possible and do not learn theirrisk scores

      This is sad, no transparency in the data and the use of it

    2. When we ask Google Mapsfor directions, it models the world as a series of roads, tunnels, andbridges. It ignores the buildings, because they aren’t relevant to the task.When avionics software guides an airplane, it models the wind, the speedof the plane, and the landing strip below, but not the streets, tunnels,buildings, and people.

      Great analogy, wondering where it's going

    3. A model, after all, is nothing more than an abstractrepresentation of some process

      Definition of a model- they tell us what to expect and they guide decisions

    1. 17.7% of patients that the algorithm assigned to receive extra care were black. The researchers calculate that the proportion would have been 46.5% if t

      huge difference, almost 3x as many should have been referred

    2. verage black person was also substantially sicker than the average white person

      Again, interesting on how they were able to pull this data to determine the average black person was sicker

    3. urces and closer medical super-vision for people with mu

      Interesting initial study as well. Clearly they were looking for other trends in the data and keeping an open mind to discover this trend too

    4. less likely to refer black people than white people who were equally sick to programmes that aim to improve care for patients with complex medical needs

      Interested in learning how this was determined and studied

    1. "Feminist analysis these power differentials so that they can change them." really addressing the power and oppression that comes with it for all oppressed/marginalized groups. Privilege and oppression are intersectional (Crenshaw). Definition of oppression-the systematic mistreatment of certain groups of people by other groups. "the work of data feminism is first to tune into how standard practices in data science serve to reinforce these existing inequalities and second to use data science to challenge and change the distribution of power." co-liberation-oppressive systems of power harm all of us. Data feminism- must answer these questions: what info needs to because data before it can be trusted? whose info needs to become data before it can be considered as fact and acted upon? The book by Cathy O'Neil sounds interesting. Love this quote "It takes more than one gender to have gender inequality and more than one gender to work toward justice." data feminism is about power-about who has it and who doesn't. Review the seven principles. Book provides concrete steps

    2. we employ the term feminism as a shorthand for the diverse and wide-ranging projects that name and challenge sexism and other forces of oppression, as well as those which seek to create more just, equitable, and livable futures.

      Definition used for feminism

    3. wealth inequalities and the role that well-educated, well-off people play in maintaining those..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; } Or to believe in the logic of co-liberation. Or to advocate for justice through equity. .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1nyah beanIndeed, a central aim of this book is to describe a form of intersectional feminism that takes the inequities of the present moment as its starting point

      almost like their declaration of biases or disclaimer.

    4. ngineering positions, where they could be promoted through the ranks of the civil service, while women with the same degrees were sent to the computing pools, where they languished until they retired or quit.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }211..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Joe Masnyy

      Men-engineering past more opportunities. Women-human computers. Glass ceiling

    5. Not only was her contribution vital to the success of the Apollo II mission, her very presence was challenging the sexism and racism within the US.