Abstract
This study proposes a replicable pipeline for transforming students' open-ended motivation inputs into structured data using large language models (LLMs) and expectancy–value theory. Analysis of a synthetic dataset suggests that distinct motivational profiles may correspond with varying levels of engagement and performance. Motivational responses can also be analyzed to identify patterns across course subjects, levels, and outcomes. These insights have practical implications for curriculum design, academic advising, and early intervention. Although motivational features may not significantly enhance predictive accuracy or recall, they contribute to interpretation by offering insights into the internal factors that drive student engagement and disengagement. This work showcases a use case of LLMs to power scalable motivation analytics to inform student success efforts.
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