This study explores the use of machine learning and physiological signals to enhance learning performance based on students' personality traits. Traditional personality assessment methods often yield unreliable responses, prompting the need for a novel approach utilizing objective data collection through physiological signals. Participants from a Taiwanese university's Department of Electrical Engineering engaged in a programming video task while wearable sensors captured their physiological signals. A Big Five-factor theory questionnaire was administered to assess their personality traits, and a personality prediction model was developed using the collected data. Results indicated that galvanic skin response and heart rate variance significantly predicted extroversion, while heart rate variance also predicted agreeableness and conscientiousness. These findings hold implications for personalized programming education, enabling educators to tailor pedagogical methods based on students' personality traits, thereby improving learning outcomes. A case study in a game development elective course demonstrated significantly better performance with personalized materials. By leveraging machine learning and physiological signals, this research presents new opportunities for personalized education, fostering engaging and effective learning environments. Future research can explore its application in other educational domains and assess its long-term impact on learning outcomes.