<![CDATA[ This study analyzed the use of code-mixing by Generation Z, particularly English Literature students of UNIMA Batch 2021, in their WhatsApp group discussions. Since the data were in the form of written conversations rather than numerical data, this study applied a descriptive qualitative method to answer the research question. The data were collected from WhatsApp class group discussions over one semester, specifically from August 20, 2023, to December 19, 2023. This study used Hoffmann's (2014) theory to identify the types of code-mixing and Myers-Scotton’s (1998) Markedness Model as the framework. The findings revealed 113 instances of code-mixing, which were classified into three types: Insertion (63 cases), Alternation (20 cases), and Congruent Lexicalization (9 cases). The analysis also showed that 84 instances were marked, indicating intentional use for emphasis, social identity, or academic expression, while 29 instances were unmarked, suggesting a natural and habitual use of code-mixing in digital communication. The results indicate that code-mixing among these students is influenced by their academic environment, digital exposure, and peer interactions. This research contributes to the understanding of how Generation Z integrates multiple languages in everyday communication, particularly in academic settings. ]]>
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