Personalized programming education: Using machine learning to boost learning performance based on students’ personality traits

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