Evaluation of the high-quality development of graduate education in mining engineering based on ISM and Bayesian networks
Year: 2026
Type: article
Abstract: The quality of graduate education in mining engineering is important for safeguarding national energy security and supporting the green and intelligent transformation of the mining industry. This study developed an evaluation system comprising five criterion-level dimensions, 12 sub-criteria, and 26 indicators. Interpretive Structural Modeling (ISM) was used to identify the hierarchical relationships among the indicators, and an expert-informed Bayesian network was subsequently established for probabilistic evaluation. The results showed that the target node had a positive-state probability of 47%, indicating an overall medium level of development. Curriculum quality exhibited the highest positive-state probability among the sub-criteria. When the target node was set to the positive state, the core curriculum renewal cycle, supervision frequency in practical training, and supervisors' technological updating capability showed comparatively high posterior probabilities, whereas research platform support emerged as a relatively weak node requiring further attention. These findings provide structured decision-support evidence for education administrators and policymakers seeking to improve the quality of graduate education in mining engineering in China.
Source: PLoS ONE
Authors: Liyun Wu, Zhipeng Sun, Yuzhong Yang
Institution: Henan Polytechnic University
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,382
Funder: Division of Graduate Education
OpenAlex ID: W7215083553