Feature-Based Misconception Detection is a methodological approach within misconception detection that identifies and analyzes conceptual misunderstandings by extracting and evaluating domain-specific features from learner-generated responses, distinguishing itself through its reliance on explicitly defined diagnostic attributes as indicators of erroneous reasoning.