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As emerging scholars of color with transnational backgrounds, we collectively recount our socialization experiences in US higher education institutes. We explore moments of betweenness as catalysts for envisioning a more inclusive academia that operates beyond the tokenism of diversity.

Employing betweener autoethnography (Diversi and Moreira, 2018), we inquire into the sense of impasse encountered by South Korean female emerging scholars in the field of education in becoming an outsider within the academic system.

Chronicling our shifts in perspectives of our positionality, we interweave inquiries motivating us to challenge normative pressures and map our betweener experiences onto the Wiedman and DeAngelo’s (2020) socialization model. Through this process, we wedge open in-between spaces in the socialization process that accommodate the nuanced positionality of transnational scholars.

Integrating postcolonial critiques on the Western-centric meritocratic academia, this piece sheds light on the complexity and fluidity of emerging transnational scholars’ socialization processes. The thick, nuanced description deepens the understanding of the complexity of their identity negotiation within the dominant logics of academia. Our inquiries interwoven through betweener autoethnography serve as guidance for mentoring international graduate students and transnational scholars.

Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.