Academic Bias Research Articles

Racial Bias in Academia: An Audit Experiment Revealing Disparities in Faculty Responses to Prospective Students

Building on Milkman, Akinola, and Chugh (2015), this article presents data from an experiment conducted in Australia that included fictional emails from prospective students seeking a meeting with faculty members. The results show significantly different responses from faculty depending on the student’s name and association with a racialized group. While the study reveals evidence of racial bias, there is, contrary to previous studies, little evidence of gender bias. Additionally, the study concludes that gender or racial diversity at the university or discipline level is not associated with lower rates of bias. Additional exploratory analysis further examines the data for evidence of change processes, including the interaction of gender and racial diversity, and lower rates of bias among more junior academics.

Generative or Degenerative?! Implications of AI Tools in Pre-Service Teacher Education and Reflections on Instructors’ Professional Development

Despite existing research on AI applications in education (AIEd), the release of ChatGPT has disrupted the status quo in the educational landscape. Although this technology can personalize learning, decrease teacher workload, and offer access to a wealth of information, concerns around generative AI (GenAI) tools have emerged, including academic integrity, data accuracy, and bias in information. Given research highlights and acknowledging educators’ varied levels of awareness and conflicting views toward AIEd, two teacher educators (also authors of this paper) in the Faculty of Education at Brock University facilitated three workshops among different groups of teacher educators. The workshops focused on the emerging nature of GenAI tools, their affordances, and their implications for educators’ practices. Adopting a narrative inquiry approach, the authors describe the details of these workshops and present their reflections on the process of preparing for and facilitating them. Implications for teacher education research and practice are also presented and discussed.Keywords: artificial intelligence (AI), artificial intelligence in education (AIEd), teacher education, professional development, generative AI (GenAI)

Identification of Students’ Comprehension of Radiation Concept Using Rasch Analysis

This community service was conducted using a survey method. The survey aimed to test the understanding of high school students regarding the dangers of radiation exposure from the surrounding environment. Participants in this activity were 48 students (35 science students and 13 social science students) from a high school in Majalaya, Bandung Regency. The instrument used was a two-tier diagnostic test consisting of 4 essay questions related to physics concepts concerning radiation exposure. The analysis of the test results was divided into two parts: conception analysis and Rasch analysis. The conception analysis was done by interpreting students' answers and categorizing them into five conception categories: Full Understanding (FU), Partial Understanding (PU), Misconception (MC), Not Understanding (NU), and No Coding (NC). This categorization was presented in percentage form. The Rasch analysis was conducted based on the distribution of students' answers and item bias detection to identify the spread of students' answers to each question item and the biases present. First, the conception analysis results showed that 53% of students were included in the FU category, while only 5% were in the Not Understanding (NU) category. This indicates that students' understanding of the dangers of radiation exposure is quite good, as more than half of the participants are in the FU category. Second, the Rasch analysis results based on the distribution of students' answers showed that more students could answer basic comprehension questions compared to classification and analysis-based questions. Additionally, the Rasch analysis results based on item bias detection indicated that there was no gender or academic major bias in the four question items.

ChatGPT in Education: Applications, Concerns and Recommendations

Chat Generative Pretrained Transformer (chatGPT) is a language model that enables users to engage in natural language conversations with the system through text. The model is available through an easy-to-use, free web interface with impressive capabilities, which has contributed to its rapid popularity among students. This significant interest of students in utilizing chatGPT underscores the need to understand its impact on education. This paper presents applications, concerns, and recommendations on the use of chatGPT in education. The reviewed articles highlight several educational applications, including assisting students with writing tasks and helping teachers create learning materials. However, concerns have been raised about its unregulated use among students, particularly issues related to academic integrity, privacy violations, and potential biases. Regardless, many researchers advocate for the integration of chatGPT into the education system. Some do have reservations and recommend measures, including learning goals adjustments and development of new assessment forms to embrace ChatGPT in learning. While there is a great potential of chatGPT use in education, further research is needed to fully understand its implications when used without appropriate guidance and its long-term effects on students.

“I will resume my research work when things settle down”: A narrative inquiry of an EFL academic's emotions and identities in research experiences

Despite a surge of studies on the construction of researcher identities among English as a Foreign Language (EFL) teachers, insufficient attention has been paid to their ongoing identity development after establishing a researcher identity. Using narrative inquiry, this study investigated how an EFL academic transitioned from being a rising star in research to becoming a teaching-focused academic midcareer with emotional flux in situated socio-institutional contexts. Data were collected from semi-structured interviews, narrative frame, institutional documents, and the participant's academic profile. The data analysis revealed that while the participant started her research journey as a confident novice researcher, she faced negative emotions arising from encounters with potential bias in academia, institutional managerial practices, and diminished self-agency with waned research motivation. Such negative emotions gradually escalated, posing severe impediments to her researcher identity. Eventually, these impediments resulted in her research stagnation and subsequent transformation of her identity into that of a teaching-focused EFL academic midcareer. The findings provide a nuanced understanding of the complexities involved in the continuous development of EFL academics' researcher identities in the changing landscape of higher education. The study also provides implications for supporting EFL teachers in constructing and maintaining a robust researcher identity to facilitate their ongoing professional development.

Academic Degree Bias Among Speaking and Leadership Roles at the American Academy of Orthopaedic Surgeons Annual Meetings, 2016-2021.

This study examined the proportion of Doctors of Osteopathic Medicine (DOs) across various speaking and leadership roles at recent American Academy of Orthopaedic Surgeons (AAOS) annual meetings. Design: Meeting programs from the AAOS were publicly accessed and compiled between 2016 and 2021. Two categories of AAOS meeting participants were created. Invited speaker and faculty roles included moderators of symposia and program committee members while authors of presented papers were classified into academic roles. The proportion of DOs in each role type (invited speaker, academic) was recorded for each meeting program. The representation of DOs in these roles was then examined longitudinally across our analysis period using Pearson's Correlation. Results: Overall, 1.1% (119/10,636) of all roles were held by DOs. Across our analysis period, DOs were disproportionately underrepresented as invited faculty or speakers (0.1%, 4/2791) compared with academic roles (0.1% vs 1.5%, p<0.001). Specifically, DOs were underrepresented as program committee members (0.08% vs 1.5%, p<0.001), symposia lecturers (0.1% vs 1.5%, p=0.004), and moderators of paper presentations (0.3% vs 1.5%, p=0.01). Conclusion: Between 2016-2021, DOs were disproportionately represented as invited speakers or faculty at AAOS annual meetings. Our definition of diverse perspectives may need to expand to include osteopathic physicians.

Application of fuzzy-set qualitative comparative analysis (fsQCA) in hospitality and tourism research: a bibliometric study

PurposeFuzzy-set qualitative comparative analysis (fsQCA) is explored as a transformative tool rooted in complexity theory, shedding light on uncertainties shaping real-world decisions in tourism, with a focus on its application in the hospitality domain.Design/methodology/approachThis study systematically evaluates fsQCA’s application in hospitality and tourism research, employing bibliometric analysis to scrutinize the published literature since its induction in 2011. The research seeks to understand the evolving usage by qualitatively reviewing impactful studies based on total citations.FindingsThe study reveals the ascendancy of fsQCA as a predominant approach in hospitality and tourism studies, particularly in illuminating decision-making paradigms in key sectors like destination and hotel selections and entrepreneurial orientations. However, an absence of fsQCA applications in gastronomy and wine tourism is identified, signaling uncharted territories for future inquiry.Research limitations/implicationsTheoretical implications include paradigm shifts to complexity theory, configural analysis and asymmetric algorithms. Practical implications involve improved decision-making and tailored marketing, benefiting industry practitioners. Limitations include potential academic bias, while future research suggests exploring sub-sectors, sustainability and emerging technologies.Originality/valueThis study identifies gaps in the fsQCA application and pioneers its examination within the hospitality domain, offering a unique perspective on understanding intricate relationships and configurations among variables. The study emphasizes the efficacy of asymmetric methodologies in elucidating behavioral nuances in hospitality and tourism, providing a foundation for future inquiries to expand horizons and unravel the nuanced applications of fsQCA in this research domain.

Gender patterns in engineering PhD teaching assistant evaluations corroborate role congruity theory

BackgroundThe body of work regarding gender bias in academia shows that female instructors are often rated lower by students than their male counterparts. Mechanisms are complex and intersectional and often associated with role congruity theory. Little research has examined parallel patterns in graduate teaching assistant (TA) evaluations. In research institutions, TAs make up a large portion of teaching teams. Identifying bias and working to remove it is critical to shifting the already-well-documented gender imbalance in higher education. To evaluate gender-associated perceptions of graduate TAs’ teaching skills, we analyzed Likert-scale, mid-semester survey data using ordinal logistic regression models for PhD TAs in five (pre-COVID) semesters in the College of Engineering at Cornell University, a large R1 institution in the United States. We also regressed scores for each survey question against the overall TA quality rating for male- and female-identifying TAs to compare the strength of those relationships and explore potential differences in student expectations associated with gender roles. A subset of narrative comment data were coded into themes, analyzed, and triangulated with other observed patterns.ResultsMale TAs had a higher likelihood of receiving a better rating than female TAs for all survey questions in which students rated performance. Statistical evidence of different slopes of relationships between particular questions and overall TA quality rating suggested that female and male TAs were “valued” more for behaviors/attributes congruent with roles ascribed to that gender in broader society. Female TAs received a higher proportion of positive comments for communication skills and more comments regarding supportiveness than male TAs. Males received more comments about their overall value as TAs, however all comments regarding overall quality as TAs were positive regardless of gender. The amount and proportion of comments that were positive or negative for knowledge, enthusiasm, preparedness or fairness were the same for male and female TAs.ConclusionsGender-based disparity is occurring in TA evaluations and aligns with patterns observed in research on teaching evaluations for faculty. Correlation between overall TA ratings and scores for specific survey questions and narrative responses indicate that role congruity influences traits that students perceive as important and positive in TAs of different genders.

Critical analysis of the technological affordances, challenges and future directions of Generative AI in education: a systematic review

ABSTRACT Generative artificial intelligence has been regarded as a transformative tool. While responsible and ethical applications could bring opportunities to education, their misuse could pose demanding challenges. It is necessary to clarify the technological affordances and challenges in a normative way to lay the foundation for future development. This study addressed the dearth of literature by performing a systematic review, aiming to (i) explore the utility and availability from the technological affordances perspective; (ii) summarize the current challenges in risks prevention; and (iii) propose possible directions for future research and practice. A total of 27 academic articles published in core journals between 2020 and 2023 were analyzed, and the inductive grounded approach was used to categorize the coding schemes. The findings revealed four technological affordances: accessibility, personalization, automation, and interactivity; and five challenges: academic integrity risk, response errors and bias, over-dependence risk, the widening digital divide, and privacy and security. We propose future directions, encourage educational organizations to formulate guidelines for the ethical use of AI in education, call on educators to embrace future trends in AI education instead of shunning its use, and guide students to treat it as a thought aid and reference, rather than relying on it entirely.

The philosophical debate on linguistic bias: A critical perspective

ABSTRACT Drawing on empirical findings, a number of philosophers have recently argued that people who use English as a foreign language may face a linguistic bias in academia in that they or their contributions may be perceived more negatively than warranted because of their English. I take a critical look at this argument. I first distinguish different phenomena that may be conceptualized as linguistic bias but that should be kept separate to avoid overgeneralizations. I then examine a range of empirical studies that philosophers have cited to argue that people who use English as a foreign language are subject to linguistic bias in academia. I contend that many of these studies do not sufficiently support key claims that philosophers have made about linguistic bias, are challenged by counterevidence, and lack generalizability. I end by introducing methodological recommendations that may help philosophers develop more convincing empirically informed arguments regarding linguistic bias.

AI literacy in geographic education and research: Capabilities, caveats, and criticality

AbstractConcerns about runaway artificial intelligence (AI) – including large language models (LLMs) like ChatGPT – are at the forefront of contemporary political, social, and scientific discourse. This commentary provides a first look at ChatGPT's capabilities and limitations in supporting geographic research, critical thinking, learning, and curriculum development. We assessed ChatGPT's geographic knowledge, synthesising abilities, and potential for extrapolation. ChatGPT was employed for writing assistance, research evaluation, curriculum material creation, and content generation. Despite achieving scores of 47% to 55% on an actual exam paper, ChatGPT exhibited shortcomings including the generation of false references. Ethical concerns regarding academic misconduct, model bias, robustness, and toxic output were also identified. We assert that AI and LLMs like ChatGPT have transformative potential in Geography education and knowledge production but demand critical usage. Accordingly, we urge geographers to enhance AI literacy to enable responsible and effective use of these assistive technologies in our academic practice.

Self-perception of academic ability and ADHD symptoms in college students in China and the United States: A preliminary study.

Children with ADHD are frequently reported to demonstrate a positive illusory bias in multiple domains. Less is known about such a bias in college students. This study examined academic positive illusory bias in college students and whether cultural factors play a role in its expression. A total of 633 college students from China and the United States completed measures designed to assess biased self-perception of academics. Among other measures, the nonclinical sample completed a math task and then estimated their own achievement and completed measures of intellectual and scholastic self-competence. Symptoms of ADHD were unrelated to overconfidence on the math task and were negatively related to reports of self-competence. However, individualism and collectivism were related to overconfidence and self-competence. In contrast to results from research in other domains, academic positive illusory bias among those with more symptoms of ADHD does not appear to persist into college.

Indexing Deficiency: Connecting Language Learning and Teaching to Evaluations of US Spanish

The examination of language attitudes towards US Spanish variables unearths indexical meanings rooted in deficit perspectives, particularly in educational contexts. Standard language ideologies undergird pedagogical practice and learning experiences in second language (L2) and heritage language (HL) Spanish classes. The present study utilizes dual research paradigms of social cognition (matched guise technique (MGT); implicit association test (IAT)) to determine if varying experiences with (Spanish) standard language ideologies in academic settings condition bias towards standardized Spanish (SS) and US Spanish (USS) repertoires. L2 and HL students as well as teachers of Spanish (n = 81) have more positive associations of SS in both the MGT and IAT, demonstrating that standard language ideologies influence perceptions of language acquisition and academic language learning. No correlations between the bias measures were reported yet attitudes did not differ, suggesting that attitudes are stable and reflected in both early learnings of social information and lived experiences throughout formative education. These results contribute to a growing body of research that examines how monoglossic ideologies reinforce and reproduce the stigma associated with features of US Spanish(es).

Current Employment Situation and Countermeasures of Students in Chinese Higher Colleges and Universities

The purpose of this paper is to discuss the current situation of student employment and countermeasures in Chinese higher vocational colleges and universities. By analyzing the current situation, it is found that students of higher vocational colleges and universities are facing many challenges in the process of employment, including the severe form of employment rate, academic bias, unbalanced quality of education, lack of career planning and employment skills, and changes in the structure of talent demand. The specific reasons are as follows: imbalance of educational resources, unreasonable employment concepts, industry restructuring, skill matching problems, and lack of practical experience. Aiming at these status quo problems and reasons, this paper puts forward some countermeasures, including designing a good cultivation system at the school level, strengthening employment guidance services, carrying out entrepreneurship services, carrying out the matching of the talent market, deepening the co-operation between schools and enterprises, and strengthening the support of the government, and so on. By implementing these countermeasures, it is hoped that the employment competitiveness of students in higher vocational colleges and universities can be improved and their successful employment promoted.

Editorial: Breaking the Gender Bias in Academia and Academic Practice

Editorial: Breaking the Gender Bias in Academia and Academic Practice

Gender bias in special issues: evidence from a bibliometric analysis

Even though the majority of psychologists are women, they are outnumbered by men in senior academic ranks. One reason for this representation bias in academia is that men favor other men in decision-making, especially when the stakes are high. We tested the possibility of such bias in a bibliometric analysis, in which we coded editors' and authors' gender in regular and special issues, the latter considered of higher scientific prominence. We examined all special issues from five prominent scientific outlets in the fields of personality and social psychology published in the twenty-first century. Altogether, we analyzed 1911 articles nested in 93 sets comprising a special issue and a neighboring regular issue treated as a control condition. For articles published in special (but not regular) issues, when there were more men editors, more men first-authored and co-authored the work. This pattern suggests how gender bias can be perpetuated within academia and calls for revising the editorial policies of leading psychology journals.

Where are nurse-scientists? Academic nursing research at critical crossroads

BackgroundAcademic nursing research is at a critical impasse after the great retirement and resignation during COVID-19. Sustaining and replenishing senior nurse-scientist faculty that are clinical experts with real-world clinical practice is critical. Leveraging the mission of nursing scholarship within the business of building and sustaining externally funded research enterprises in schools of nursing presents conundrums, especially with persistent nursing faculty vacancies. Purpose and MethodsThrough a lens of intersectionality within the context of academic bias and nursing education regulation, we address challenges in NIH funding for nurse-scientist faculty. Publicly available data reveal equity, inclusion, and advancement issues that make it an unequal playing field for nurse-scientist faculty if expected to achieve similar NIH funding as faculty in schools of public health and medicine. DiscussionUnderstanding research enterprises requires appreciation of the complex interplay between academic nursing units, university infrastructures, and academic budgetary models. Creative support for both nursing deans and their faculty is needed.

Inequality at the Starting Line: Underrepresentation in Gifted Identification and Disparities in Early Achievement

Is underrepresentation of Black students, Latinx students, English learners (EL), and students from economically challenging communities in gifted programs due to inequality in early academic achievement or bias in the gifted identification process? Using three-level multilevel logistic models, we examine the degree to which the disparities in gifted identification are due to disparities in early achievement. Our datasets include 3rd-, 4th-, and 5th-grade achievement scores and demographic variables from all students in a 3rd-grade, 2011–2012 cohort across three states. Students who qualified for free/reduced-price lunch (FRPL), EL, and Black or Latinx students were between two to eight times less likely to be identified as gifted compared to non–free/reduced-price lunch, non-EL, and White or Asian students. However, between 50% and 100% of gifted identification disparities could be explained by student-level differences in early academic achievement, which is consistent with an opportunity gap explanation of underrepresentation.

Wondering If I'd Get There Quicker If I Was a Man: Factors Contributing to Delayed Academic Advancement of Women in Infectious Diseases.

Gender inequities in academic advancement persist in many specialties, including Infectious Diseases (ID). Prior studies of advancement disparities have been predominantly quantitative, utilizing large physician databases or surveys. We used qualitative methods to explore ID physicians' experiences and beliefs about causes and ways to mitigate gender inequities in advancement. We conducted semistructured focus group discussions with academic ID physicians in the United States at IDWeek 2019 to explore perceived barriers and facilitators to academic advancement. Participants were assigned to focus groups based on their academic rank and gender. We analyzed focus group transcripts using content analysis to summarize emergent themes. We convened 3 women-only focus groups (1 for instructors/assistant professors, 1 for associate professors, and 1 for full professors) and 1 men-only focus group of full professors (total N = 50). Our analyses identified several major themes on barriers to equitable academic advancement, including (1) interpersonal and institutional gender bias, (2) difficulty balancing the demands of family life with work life, and (3) gender differences in negotiation strategies. Barriers to gender equity in academic advancement are myriad and enduring and span the professional and personal lives of ID physicians. In addition to swift enactment of policy changes directed at critical issues such as ending workplace harassment and ensuring adequate parental leaves for birth and nonbirth parents, leaders in academic medicine must shine a bright light on biases within the system at large and within themselves to correct these disparities with the urgency required.

Identification of Pivotal Factors Influencing the Establishment of Green Port Governance Models: A Bibliometric Analysis, Content Analysis, and DPSIR Framework

Green port governance is an emerging and recent port restructuring process influencing port authorities regarding adopting recurring sustainable governance practices within their business models. However, contemporary academic bias towards a particular aspect of port sustainable governance practice leads to publication results fragmentation and difficulty in a collective assessment of the conclusions. The lack of holistic research endeavors to port sustainable governance practices hinders the identification of organized, specific, and goal-oriented factors indispensable for coherently directing ports to sustainable and green transitions. In order to bridge the identified gap, this paper conducts a bibliometric analysis of 278 scientific articles on the emerging green port governance concept in the maritime industry, published in 113 academic outlets, authored by 695 scholars, and obtained from the world’s most reputable scientific database—ISI Web of Science. The conducted meta-analysis via HistCite software revealed the most prestigious academic institutions, the most prominent scientific journals, the most impactful scientific articles, and the most influential scholars. The bibliographic coupling methodology via Bibliometrix tool in R software indicated five concurrent research streams on the basis of the top 10% of the scientific articles: (1) Adoption of contextualized models of port governance reform as a consequence of port multi-scalar embeddedness forces and institutional stretching; (2) Fostering port sustainability competitiveness via extended resource pools in terms of dry ports coupling; (3) The digitalization role of information systems regarding the interaction between transport, infrastructure, and institutional management in ports; (4) Assessment and innovation in seaport competition policies for the integration of green and sustainable operations; and (5) Conceptual development and awareness raising of port management practices on account of CO2 evidence-based policies regarding port terminals. The application of co-citation analysis methodology revealed future research directions within the five concurrent research streams on the basis of the top 5% trending scientific articles. The detailed content analysis assesses the scientific mapping of future research directions via the structural associations of the Driver-Pressure-State-Impact-Response (DPSIR) framework. The cause-and-effect relationships of the structural associations of the DPSIR framework possess the possibility of providing substantial insights into the most influential factor identification regarding the development of green port governance models and self-assessment toolkits.