Recognition Of Learners’ Personality Traits For Software Engineering Education

It is vital for educators to teach learners in accordance with their aptitude, which can be useful to help learners reach their full potential. Educators have been taking the Myers-Briggs Type Indicator (MBTI) as a powerful tool to understand the differences in students’ learning styles, adopting appropriate teaching strategies to accommodate the learning styles of different types of students can effectively prevent students from being tired of studying. It is a problem worthy of research to recognize the students’ personality traits with technological means. Therefore, we propose a method to recognize learners’ MBTI from videos, which can be applied in the course learning and practice stages of software engineering education. We propose a novel approach to recognize the MBTI personality traits of learners from videos. Personality and emotion unconsciously affect facial expression, the speaking style in social contexts. However, in the current literature, there is no publicly available source of images dataset labeled with the MBTI personality scale; nearly all the available data are text. In this paper, we use two datasets: images extracted from ChaLearn First Impressions dataset and the Myer-Briggs Personality Type Dataset from Kaggle for our training tasks. Furthermore, we take plentiful text data labeled with the MBTI personality scale as the source domain and image data as the target domain for borrowing knowledge from the source domain to facilitate the learning task in a target domain. By adopting feature transfer, a bridge is built between the source domain and the target domain. We perform experiments on the transfer task and evaluate the effectiveness of this approach, the results of this study can assist educators in regards to the identification of learners’ MBTI personality types in a new way.