Reform Exploration and Practice of Process Evaluation for the Machine Vision Course Empowered by Rain Classroom

Traditional process assessment of the Machine Vision course suffers from weak traceability and delayed feedback. To address these issues, a reform was carried out with the support of Rain Classroom, while keeping the syllabus and score weights unchanged. Online learning and homework were migrated to the platform, and course objective achievement was analyzed with real score data. The results indicate that Rain Classroom makes the whole assessment process traceable and effectively motivates students’ autonomous learning; however, superficial learning behaviors still exist among a small number of students, and their homework reveals weaknesses in camera calibration and image algorithm implementation. Overall course achievement is satisfactory, while learning analytics-based early warning and homework feedback remain to be improved.