Evaluation of the high-quality development of graduate education in mining engineering based on ISM and Bayesian networks
- 1School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo, China
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.
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BibTeX
@article{wu2026evaluation,
title = {{Evaluation of the high-quality development of graduate education in mining engineering based on ISM and Bayesian networks}},
author = {Liyun Wu and Zhipeng Sun and Yuzhong Yang},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359427},
url = {https://doi.org/10.1371/journal.pone.0359427},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Evaluation of the high-quality development of graduate education in mining engineering based on ISM and Bayesian networks
AU - Liyun Wu
AU - Zhipeng Sun
AU - Yuzhong Yang
PY - 2026
DA - 2026/10/01
JO - PLOS One
DO - 10.1371/journal.pone.0359427
UR - https://doi.org/10.1371/journal.pone.0359427
AB - 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.
SN - 1932-6203
ER -