This study introduces an innovative automated model, the Scientists and Researchers Classification Model (SRCM), which employs data mining and machine-learning techniques to classify, rank, and evaluate scientists and researchers in university settings. The SRCM is designed to foster an environment conducive to creativity, innovation, and collaboration among academics to augment universities’ research capabilities and competitiveness. The model's development roadmap, depicted in Figure 1, comprises four pivotal stages: preparation, empowerment strategies, university-recognised research ID, and evaluation and re-enhancement. The SRCM implementation is structured across three layers: input, data mining and ranking, and recommendations and assessments. An extensive literature review identifies ten principal procedures further evaluated by experts. This study utilises Interpretive Structural Modelling (ISM) to analyse these procedures’ interactions and hierarchical relationships, revealing a high degree of interdependence and complexity within the SRCM framework. Key procedures with significant influence include determining the input data sources and collecting comprehensive lists of university scientists and researchers. Despite its innovative approach, SRCM faces challenges, such as data quality, ethical considerations, and adaptability to diverse academic contexts. Future developments in data collection methodologies, and addressing privacy issues, will enhance the long-term effectiveness of SRCM in academic environments. This study contributes to the theoretical understanding of academic evaluation systems and offers practical insights for universities that aim to implement sophisticated data-centric classification models. For example, by implementing data-centric models, universities can objectively assess faculty performance for promotion or tenure. These models enable comprehensive evaluations based on publication records, citation counts, and teaching evaluations, fostering a culture of excellence and guiding faculty development initiatives. Despite its limitations, SRCM has emerged as a promising tool for transforming higher education institutions’ academic management and evaluation processes.

中文翻译：

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迈向基于科学家和研究人员分类模型 (SRCM) 的机器学习和数据挖掘方法：ISM-MICMAC 方法


本研究引入了一种创新的自动化模型，即科学家和研究人员分类模型 (SRCM)，该模型采用数据挖掘和机器学习技术对大学环境中的科学家和研究人员进行分类、排名和评估。 SRCM 旨在营造一个有利于创造力、创新和学术界合作的环境，以增强大学的研究能力和竞争力。该模型的发展路线图如图 1 所示，包括四个关键阶段：准备、赋权策略、大学认可的研究 ID 以及评估和再增强。 SRCM 实施分为三个层次：输入、数据挖掘和排名以及建议和评估。广泛的文献综述确定了专家进一步评估的十个主要程序。本研究利用解释结构模型 (ISM) 来分析这些过程的相互作用和层次关系，揭示了 SRCM 框架内的高度相互依赖性和复杂性。具有重大影响的关键程序包括确定输入数据源和收集大学科学家和研究人员的综合名单。尽管采用创新方法，SRCM 仍面临数据质量、道德考虑和对不同学术环境的适应性等挑战。数据收集方法的未来发展以及隐私问题的解决将增强 SRCM 在学术环境中的长期有效性。这项研究有助于对学术评估系统的理论理解，并为旨在实施复杂的以数据为中心的分类模型的大学提供实践见解。 例如，通过实施以数据为中心的模型，大学可以客观地评估教师的晋升或终身教职表现。这些模型可以根据发表记录、引用计数和教学评估进行综合评估，培育卓越文化并指导教师发展举措。尽管有其局限性，SRCM 已成为变革高等教育机构学术管理和评估流程的有前途的工具。