The honeymoon ends. Within days or weeks of landing, most international students hit a sharp dip in confidence—the “arrival cliff”—when novelty stops compensating for cumulative friction: a missed bureaucratic deadline, a familiar gesture suddenly read as inappropriate, a class discussion that moves three steps ahead of what can be processed. This chapter maps the dip with a modified U-curve model that accounts for the daily and weekly zigzags the classic curve hides, and shows that the trajectory is not linear, not personal, and not pathological. It identifies what makes the first weeks so costly—the gap between test-ready English and real-time speech, listening fatigue, communication burnout, foreign-language anxiety, and the unwritten conversational norms that grammar instruction never covers—and then supplies what helps: repair strategies and freeze-recovery moves, perceptual adaptation to unfamiliar accents and speech rates, phatic communication and the mechanics of small talk, and the four capabilities of cultural intelligence (motivation, knowledge, strategy, and behavioral flexibility). It also covers the practical systems that consume disproportionate energy in the first months: health care and insurance, emergency numbers and interpreter requests, transportation, banking, groceries, and tipping. Nonverbal norms—such as personal space, eye contact, and greeting styles—get the same treatment. Student narratives from Kenya, Malaysia, Colombia, Sweden, Singapore, and Korea run throughout, alongside an instructor-perspective section on what faculty actually notice during the arrival cliff and a case study of two Korean students at a U.K. university reclaiming voice without abandoning cultural identity. The chapter argues that recovery is faster for students who treat the cliff as expected weather rather than a verdict on whether they belong.
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
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