The literary canon is often associated with Western literature, including European and American literature. Undergraduate students who declare an English major at California State University, Stanislaus have limited opportunities to take courses based on non-Western literature. There is a gap in the amount of decolonial literature and curricula taught within the major. In addition, students do not usually work alongside faculty in pedagogy and curriculum development. The emphasis of this research is to examine how students and faculty can create collective pedagogies to augment decolonial literature in the English major. The research will be interdisciplinary between the English and Ethnic Studies Departments at CSU Stanislaus. Ethnic Studies will provide a decolonial lens to the qualitative data. I will conduct interviews and focus groups with faculty and students to discuss curricula, decolonization, and collaborative student-faculty pedagogies. I will also conduct participant observation with the CSU Stanislaus Honors Program co-directors to study curriculum development and revision. From my research, I expect that students and faculty can increase decolonial literature in the English major through collective pedagogies that modify curricula and course designs. Future research can lead to an increase in student-faculty collaboration in higher education, as well as an expansion of diverse literature.

This research presentation explores the significant challenges Indigenous communities in Santa Barbara County face in accessing and succeeding in higher education for themselves and their children. Motivated by the persistent need to address educational inequities, this research examines the socio-economic, cultural, and institutional barriers that obstruct educational attainment among Indigenous populations. Through a mixed-methods approach, including qualitative interviews with Indigenous students and their parents, this research identifies key obstacles such as financial constraints, cultural dissonance, and inadequate institutional support. The findings highlight the disparities and the urgent need for targeted interventions to help students achieve higher education goals. Additionally, this research aims to address the stigma that Indigenous people are not interested in education, demonstrating that, in reality, they face numerous obstacles that prevent them from completing an educational career. This research proposes strategies to mitigate these barriers. Santa Barbara County has a considerable Indigenous population, so community organizations and programs in the area should engage in initiatives in the population's native language, advocating for language revitalization. Indigenous communities should refrain from being influenced to learn a new language and forget their native language to succeed but should be encouraged to promote language diversity and inclusion. The proposed solutions address these challenges to enhance educational outcomes for Indigenous communities, fostering more excellent representation and success in higher education—the importance of inclusive policies and practices that honor Indigenous students’ unique cultural identities and needs.

This research investigates the impact of algorithmic colonization on social, political, and economic realms, with a focus on platforms such as META(Facebook), X(Twitter) and Tumblr. Utilizing an intersectional framework and queer methodology, the study analyzes how algorithmic platforms shape access, dissemination, and publication of data. Through a queer and de-colonial lens, it examines the role of neoliberalism in the development of algorithmic models, revealing how systems employed by informational companies are driven by profit and free-market policies, stressing the need for an intersectional perspective to address biases, inequalities, and power dynamics embedded within algorithmic design. Additionally an examination of colonization in the digital age, through traditional forms of data collection and presentation methods—which often seek to simplify and homogenize—reveal how heteronormative and cis identities are positioned as default standards of measurement, collection, and presentation. By employing a queer methodology, this research aims to disrupt and question the heteronormative biases embedded in code used to train algorithmic models, such as META’s ranking system and Twitter’s trending topics. Key arguments focus on algorithm design, personalized content, and regulatory challenges, explored through case studies, and intersections in cultural analytics and digital humanities. This research considers alternative forms of instrumentalization and identification, through forms in which individuals can re-imagining and engage in radical imagination to address and promote more inclusive and ethical algorithmic design.