Towards Scientists and Researchers Classification Model (SRCM)-based machine learning and data mining methods: An ISM-MICMAC approach

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.