Against the backdrop of the digital transformation of education

and aiming to address issues such as strong subjectivity and lack of process data in traditional science experiment teaching evaluation

this study constructs a quality evaluation system for science experiment teaching integrating four dimensions—students

teachers

resources

and management—from the perspective of educational management

based on big data technology. The system adheres to three principles: integration of process and outcome orientation

balance of objectivity and practicality

and dynamic adaptability

while clarifying the definition

data source

and quantification method of indicators for each dimension. Data are collected through experiment teaching platforms

smart devices

etc.

and after undergoing a four-step preprocessing procedure

a layered and progressive analysis strategy (descriptive statistics

correlation and causation analysis

prediction and early warning) is adopted to explore data value. The evaluation results are applied relying on a three-tier feedback-optimization closed loop. A pilot application in chemistry experiment teaching at a university shows that the system increases device utilization rate by 15% and student operation compliance rate by 20%

effectively solving the pain points of traditional evaluation. Meanwhile

the study identifies challenges such as data governance and teachers' technical barriers

and puts forward optimization suggestions

providing decision support for educational administrators.