Teaching Reform and Practice of the Machine Learning Course in the Context of Professional Certification

Abstract

The engineering education accreditation framework, recognized as a pivotal quality assurance mechanism under the Washington Accord, has achieved worldwide adoption since its establishment. Chinese higher education institutions have strategically aligned their curricula with professional accreditation standards in recent years, systematically implementing outcome-based evaluation to cultivate globally competent engineering professionals. Using the machine learning course as a case study, this paper investigates teaching reform and practice under the framework of professional certification. The outlined student-centered pedagogical innovation process encompasses four constitutive phases: precise definition of outcome-based teaching objectives, backward-designed modularized teaching content, implementation of problem-oriented didactic methodologies integrated with case-practice immersion modules, and deployment of an evaluation enabling real-time learning analytics and formative feedback loops across instructional cycles. These strategies serve as a reference for advancing course-level reforms within the engineering education professional certification framework, offering actionable insights for related disciplines.