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Early forecasting of student performance in a course is a critical component of building effective intervention systems. However, when the available student data is limited, accurate early forecasting is challenging. We present a language generation transfer learning approach that leverages the general knowledge of pre-trained language models to address this challenge. We hypothesize that early forecasting can be significantly improved by fine-tuning large language models (LLMs) via personalization and contextualization using data on students' distal factors (academic and socioeconomic) and proximal non-cognitive factors (e.g., motivation and engagement), respectively. Results obtained from extensive experimentation validate this hypothesis and thereby demonstrate the prowess of personalization and contextualization for tapping into the general knowledge of pre-trained LLMs for solving the downstream task of early forecasting.