Disagreeable, Slovenly, Honest and Un-named Women? Investigating Gender Bias in E nglish Educational Resources by Extending Existing Gender Bias Taxonomies (2024)

Abstract: Gender bias has been extensively studied in both the educational field and the Natural Language Processing (NLP) field, the former using human coding to identify patterns associated with and causes of gender bias in text and the latter to detect, measure and mitigate gender bias in NLP output and models. This work aims to use NLP to facilitate automatic, quantitative analysis of educational text within the framework of a gender bias taxonomy. Analyses of both educational texts and a lexical resource (WordNet) reveal patterns of bias that can inform and aid educators in updating textbooks and lexical resources and in designing assessment items.

## introduction educational materials for children such as reading comprehension articles or test assessments often protagonize real or fictional characters with gender information, rendering the materials more engaging @xcite . they, however, could carry implicit gender bias and thus potentially reinforce gender stereotypes via children's learning process @xcite . one example of such gender bias in educational materials lies in the asymmetrical distribution of males and females in human-generated text such as textbooks, where male and female characters tend to take on different social roles @xcite . additionally, such gender bias surfaces in the lexical entries and definitions in dictionaries. an open letter @xcite calls on oxford university press to change its "sexist" definitions of the word "woman." most research on gender bias in the educational field relies on qualitative methodologies suitable for small-scale analyses (e.g., @xcite ; @xcite ). in contrast, gender bias studies in the field of nlp mostly attempt to identify, quantify and mitigate gender bias in nlp applications @xcite @xcite , with few looking at educational texts @xcite . towards the aim to identify and analyze gender bias in educational data using nlp methods, in this paper, we first review recently developed gender bias taxonomies ( §3) with an extension to incorporate new types of bias in text. using nlp techniques, we extract gendered mentions ## bias statement in this work, we attempt to examine gender bias in human-generated text and specialize it to educational resources such as textbooks, test assessment items and lexicons. we adopt the definition of gender bias as given in @xcite : "an exclusionary, implicitly prejudicial, or generalized representation of a specific gender as a function of various societal stereotypes." here we employ and extend existing gender bias taxonomies @xcite and examine different types of gender bias in educational resources. people implicitly associate certain behaviors or traits to a specific gender, creating gender stereotypes. such bias in educational resources can be learned by children through the early process of learning @xcite and further perpetuates gender stereotypes. for example, it has been shown that women are generally less represented in textbooks and often associated with family-related roles and traits, whereas men are over-represented and often associated with work-related roles. such differentiated representation of male and female genders in textbooks, which often serve an instructional purpose, creates a false imagery for children with respect to what roles men and women are expected to undertake, producing unnecessary and harmful gender stereotypes. furthermore, lexical resources such as wordnet are often used to train nlp systems or as external knowledge bases. the implicit bias within these resources can be passed on to produce biased system outputs that can potentially cause representational harms @xcite . here, we investigate gender bias in educational resources only for male and female genders for the following reasons: (1) the datasets used for analysis are not recent and up-to-date (all educational datasets are published before 2018). therefore, the number of people mentioned in those datasets whose gender is non-binary gender is limited; (2) the nlp systems such as coreference resolution in the current pipeline to extract person mentions cannot reliably detect and extract people of non-binary gender. in future work, once trustworthy nlp systems that can reliably detect and extract people of non-binary gender become accessible, the analyses can be extended to incorporate the comparison between binary and non-binary genders by using the same overall pipeline and analysis methods (e.g. odds ratio analysis). ## related work in this study, we focus on gender bias in educational data. we first discuss a taxonomy of gender bias in human-generated text and then review previous research on gender bias in the educational field and in nlp research. ## methodology in this work, we adopt and expand the existing taxonomies for gender bias in human-generated text and attempt to identify different types of gender bias in our datasets. we look at two types of data foot_2 : ## discussion our investigation has revealed the pervasive existence of various types of gender bias within both educational corpora and wordnet. specifically, we have noted the prevalence of distributional bias evidenced by the uneven distributions of males and females across both datasets, alongside explicit marking of sex and the generic use of male pronouns within wordnet. additionally, a diverse array of syntactic patterns within the corpora has been identified as displaying gender bias. in this work, we only explore gender bias in english educational materials. the extraction pipeline and gender labeling procedure proposed contain language-dependent components that are unique to english (e.g. using a coreference resolution system to determine gender of a common noun based on gendered pronouns). for languages such as man-darin chinese where the gender of the pronouns is indistinguishable without orthographic information, the pipeline may integrate language-specific nlp systems to resolve the gender of person mentions. moreover, the way that gender bias manifests in text can differ from language to language (and culture to culture). thus, the bias patterns used to detect gender bias in this work will be different. the presence of gender bias in educational resources carries significant implications. exposure to those materials can potentially shape children's perceptions through implicit gender bias, fostering the development of gender stereotypes. this perpetuation of biased narratives has far-reaching consequences for societal attitudes and inequality. moreover, nlp models reliant on lexical resources such as wordnet, wherein gender bias is discernible in multiple forms, may inadvertently perpetuate said biases in downstream tasks. however, our work offers actionable insights for educational resource developers, offering guidance on elements to consider during the creation process to mitigate bias. moreover, our study on wordnet pinpoints the bias issues that warrant monitoring and maintenance by developers. ## conclusion in this study, based on the existing taxonomy of gender bias in text, we have examined 7 types of gender bias in educational corpora and wordnet. the analysis has shown that many types of gender bias exist in both types of data, emphasizing the necessity for meticulous examination of such biases in associated resources. our future work aims to identify additional linguistic features correlated with gender. furthermore, deeper exploration is warranted into corpora from other domains and lexical resources beyond wordnet.