A New Perspective on Higher Education Quality Assessment: The Application of Machine Learning in Data Driven Decision Making

The scientific and precise level of teaching decision-making by teachers is a key influencing factor on the quality of higher education. Against the backdrop of global educational change, countries are actively adjusting their teacher education and professional growth strategies to cultivate teachers with excellent teaching abilities. In this process, data-driven teaching decisions play a crucial role, providing strong support and guidance for improving teaching quality. The mining of educational big data is a complex and meticulous task that combines interdisciplinary knowledge such as statistics and machine learning (ML), aiming to extract valuable information from massive educational data. This not only helps to gain a deeper understanding of educational phenomena, but also provides strong data support for teaching decision-making. To accurately evaluate the quality of higher education, this article proposes an automated evaluation system based on ML. The system utilizes ML algorithms to conduct in-depth analysis on a large amount of educational data and automatically completes various tasks of teaching quality evaluation. The experimental results show that the system can not only improve the efficiency and accuracy of evaluation, but also reduce the interference of human factors, ensuring the objectivity and impartiality of the evaluation results.