SCENARIO-BASED TEACHING REFORM AND PRACTICE OF PROBABILITY THEORY AND MATHEMATICAL STATISTICS FOR COMPUTER MAJORS IN THE CONTEXT OF EMERGING ENGINEERING EDUCATION

Probability Theory and Mathematical Statistics is an important basic public mathematics course for computer-related majors such as Computer Science and Technology, Information and Computing Science. It also serves as a prerequisite supporting course for core specialized courses including machine learning, big data analysis, artificial intelligence and network security. Aiming at the common problems existing in the public mathematics teaching of colleges and universities, such as overly generalized teaching content, disconnection between teaching cases and professional practice, separation of theoretical teaching from engineering application, and insufficient internal motivation of students, guided by the major-oriented education concept and combined with the talent training objectives of computer-related majors, this paper carries out research on the scenario-based teaching reform of the course. The barriers between mathematical theories and professional engineering applications are broken down by reconstructing the teaching content system adapted to computer majors, building a library of professional scenario-based teaching cases, innovating classroom teaching modes, and improving the process assessment and evaluation mechanism. Teaching practice shows that the scenario-based teaching reform can effectively improve students’ ability to apply mathematics and their professional critical thinking ability. It meets the training requirements of emerging engineering education for applied and innovative computer talents, and provides practical references for the specialized teaching reform of similar public mathematics courses.