Optimized matching between students of the automation major and their specialized project design assignment

Abstract

Specialized project design (SPD) is a compulsory course for most of the engineering students in China. A SPD course requires teamwork of students. It is therefore an important issue about how to group students in a more reasonable form to increase students’ interest in the course and improve their performance. However, at present, it remains a challenge to quantitatively assess the effectiveness of group teaching and optimize group teaching through state-of-the-art methods such as artificial intelligence. In this study, we propose a qualitative analysis method to study the group teaching of students through teacher’s subjective evaluation of different aspects of students’ abilities. In particular, the proposed method is a background-based grouping method which takes into account the students’ scores attained in prerequisite courses of a SPD course and realized by a genetic algorithm. Results of our experiment demonstrate that, compared with the conventional random grouping method, the proposed method improves the students’ average scores in all eight SPD topics by 2.5 in the grade 2016 and 1.875 in grade 2017, respectively (the total score is 100). Therefore, our findings strongly support that grouping students based on their strengths (for example, the courses they are good at) can enhance students’ interest in the SPD course and thereby improve students’ SPD performance.