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    LANGUAGE IN CONTEXT: EXPLORING THE LEARNING EXPERIENCES OF CULTURALLY AND LINGUISTICALLY DIVERSE STUDENTS IN STEM AFTER-SCHOOL PROGRAM

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    This study examines how language shapes interactions and learning outcomes for culturally and linguistically diverse middle school girls and their facilitators in an after-school STEM program. Grounded in the multiple identities approach, it explores how participants navigate hierarchies, build relationships, and engage through their multiple identities. Using Systemic Functional Linguistics, it analyzes linguistic strategies to reveal how language empowers or marginalizes. Focusing on interpersonal meanings of tenor, it examines expressions of authority and autonomy. The findings emphasize how participants’ multiple identities—such as gender, race, language, and cultural background—shape their STEM learning experiences. By exploring the intersection of linguistic and social elements, the study provides critical insights for educators and policymakers to create more equitable and inclusive STEM learning environments

    A Comparison of Museums From the U.S. and Mexico Within National History

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    With the creation of nation-states, the implementation of museums within a society is used to propagate a national agenda. A Comparison of Museums From the U.S. and Mexico Within National History will discuss the social and historical contexts of public museums in the modern nations of Mexico and the United States of America. Using Benedict Anderson’s definition of a nation, this thesis will conduct a comparison between the two countries in how their museums uphold their chosen national identities. The topics of religion, politics, colonization, race relations, and more will be explored in a discussion of how these phenomena play a part in creating a nation’s identity and the narratives upheld by public museums

    Hybrid Neutrosophic Multi-Criteria Decision Model for Guest Selection in Collaborative Tourism Platforms

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    This study presents a hybrid multi-criteria decision-making (MCDM) model applied to the guest selection process in Airbnb-style vacation rentals. The model integrates the PAPRIKA method, Analytic Hierarchy Process (AHP), and Neutrosophic TOPSIS to evaluate and rank potential guests based on multiple criteria prioritized by experienced property owners. Through the 1000minds software, pairwise comparisons were conducted to elicit preferences and derive normalized weights for six key criteria, with “review history” emerging as the most influential. These weights were then incorporated into classical and neutrosophic TOPSIS evaluations to account for uncertainty, indeterminacy, and subjectivity in guest profiles. Results highlight consistent rankings across both approaches, with the neutrosophic model providing deeper insight into decision-maker hesitation and risk perception. The proposed model demonstrates robustness, transparency, and adaptability for complex service environments and can be extended to broader collaborative economy contexts

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