Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approach

• URL: http://arxiv.org/abs/2001.09797v1
• Date: Thu, 16 Jan 2020 21:37:15 GMT
• Title: Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approach
• Authors: Mahdi Bohlouli, Nikolaos Mittas, George Kakarontzas, Theodosios Theodosiou, Lefteris Angelis, Madjid Fathi
• Abstract summary: This article describes the combined use of software technologies and mathematical and statistical methods for assessing and analyzing competences in human resource information systems. The system has been tested with real human resource data sets in the frame of the European project ComProFITS.
• Score: 5.753758386996188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient human resource management needs accurate assessment and representation of available competences as well as effective mapping of required competences for specific jobs and positions. In this regard, appropriate definition and identification of competence gaps express differences between acquired and required competences. Using a detailed quantification scheme together with a mathematical approach is a way to support accurate competence analytics, which can be applied in a wide variety of sectors and fields. This article describes the combined use of software technologies and mathematical and statistical methods for assessing and analyzing competences in human resource information systems. Based on a standard competence model, which is called a Professional, Innovative and Social competence tree, the proposed framework offers flexible tools to experts in real enterprise environments, either for evaluation of employees towards an optimal job assignment and vocational training or for recruitment processes. The system has been tested with real human resource data sets in the frame of the European project called ComProFITS.

Related papers

• Vers un cadre ontologique pour la gestion des comp{é}tences : {à} des fins de formation, de recrutement, de m{é}tier, ou de recherches associ{é}es [32.26033017109275]
  This paper proposes an ontological-based framework for competence management.<n>It enables a structured representation of competencies, occupations, and training programs.<n>It aims to enhance the automation of competence-to-job matching, the personalization of learning recommendations, and career planning.
  arXiv  Detail & Related papers  (2025-07-08T08:13:30Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Data to Decisions: A Computational Framework to Identify skill requirements from Advertorial Data [1.5621498886998335]
  The proposed framework uses techniques of statistical analysis, data mining and natural language processing for the purpose. The analytical results not only provide useful insights about current state of skill needs in CS&IT industry but also provide practical implications to prospective job applicants, training agencies, and institutions of higher education & professional training.
  arXiv  Detail & Related papers  (2025-03-21T09:49:31Z)
• Tec-Habilidad: Skill Classification for Bridging Education and Employment [0.7373617024876725]
  This paper develops a Spanish language dataset for skill extraction and classification. It provides annotation methodology to distinguish between knowledge, skill, and abilities. It also provides deep learning baselines to advance robust solutions for skill classification.
  arXiv  Detail & Related papers  (2025-03-05T22:05:42Z)
• An Overview of Large Language Models for Statisticians [109.38601458831545]
  Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI) This paper explores potential areas where statisticians can make important contributions to the development of LLMs. We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
  arXiv  Detail & Related papers  (2025-02-25T03:40:36Z)
• Latent-Predictive Empowerment: Measuring Empowerment without a Simulator [56.53777237504011]
  We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner. LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states.
  arXiv  Detail & Related papers  (2024-10-15T00:41:18Z)
• Evaluating Human-AI Collaboration: A Review and Methodological Framework [4.41358655687435]
  The use of artificial intelligence (AI) in working environments with individuals, known as Human-AI Collaboration (HAIC), has become essential. evaluating HAIC's effectiveness remains challenging due to the complex interaction of components involved. This paper provides a detailed analysis of existing HAIC evaluation approaches and develops a fresh paradigm for more effectively evaluating these systems.
  arXiv  Detail & Related papers  (2024-07-09T12:52:22Z)
• Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining [0.0]
  This study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job descriptions.
  arXiv  Detail & Related papers  (2024-03-26T09:51:43Z)
• Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts [58.220879689376744]
  Reinforcement learning (RL) is a powerful approach for acquiring a good-performing policy. We propose textbfDiverse textbfSkill textbfLearning (Di-SkilL) for learning diverse skills. We show on challenging robot simulation tasks that Di-SkilL can learn diverse and performant skills.
  arXiv  Detail & Related papers  (2024-03-11T17:49:18Z)
• Evaluating General-Purpose AI with Psychometrics [43.85432514910491]
  We discuss the need for a comprehensive and accurate evaluation of general-purpose AI systems such as large language models. Current evaluation methodology, mostly based on benchmarks of specific tasks, falls short of adequately assessing these versatile AI systems. To tackle these challenges, we suggest transitioning from task-oriented evaluation to construct-oriented evaluation.
  arXiv  Detail & Related papers  (2023-10-25T05:38:38Z)
• Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy [27.365319494865165]
  The paper details the design, data collection, analytical techniques, and results validating the proposed frameworks. It aims to provide businesses and researchers with actionable insights on effectively adapting LLMs for specialized contexts.
  arXiv  Detail & Related papers  (2023-10-07T23:29:00Z)
• A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics [46.025337523478825]
  Talent analytics has emerged as a promising field in applied data science for human resource management. Recent development of Big Data and Artificial Intelligence techniques have revolutionized human resource management.
  arXiv  Detail & Related papers  (2023-07-03T07:53:20Z)
• Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach [40.06500618820166]
  This paper presents an approach to deriving a learner model directly from an assessment rubric. We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills.
  arXiv  Detail & Related papers  (2022-09-07T10:09:12Z)
• A Field Guide to Federated Optimization [161.3779046812383]
  Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
  arXiv  Detail & Related papers  (2021-07-14T18:09:08Z)
• Decision Rule Elicitation for Domain Adaptation [93.02675868486932]
  Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels from experts. In this work, we allow experts to additionally produce decision rules describing their decision-making. We show that decision rule elicitation improves domain adaptation of the algorithm and helps to propagate expert's knowledge to the AI model.
  arXiv  Detail & Related papers  (2021-02-23T08:07:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.