Job Recommendation Based on Curriculum Vitae Using Text Mining

Author(s):  
Honorio Apaza ◽  
Américo Ariel Rubin de Celis Vidal ◽  
Josimar Edinson Chire Saire
2019 ◽  
Vol 44 (2/3) ◽  
pp. 209-235
Author(s):  
Florian Fahrenbach ◽  
Kate Revoredo ◽  
Flavia Maria Santoro

Purpose This paper aims to introduce an information and communication technology (ICT) artifact that uses text mining to support the innovative and standardized assessment of professional competences within the validation of prior learning (VPL). Assessment means comparing identified and documented professional competences against a standard or reference point. The designed artifact is evaluated by matching a set of curriculum vitae (CV) scraped from LinkedIn against a comprehensive model of professional competence. Design/methodology/approach A design science approach informed the development and evaluation of the ICT artifact presented in this paper. Findings A proof of concept shows that the ICT artifact can support assessors within the validation of prior learning procedure. Rather the output of such an ICT artifact can be used to structure documentation in the validation process. Research limitations/implications Evaluating the artifact shows that ICT support to assess documented learning outcomes is a promising endeavor but remains a challenge. Further research should work on standardized ways to document professional competences, ICT artifacts capture the semantic content of documents, and refine ontologies of theoretical models of professional competences. Practical implications Text mining methods to assess professional competences rely on large bodies of textual data, and thus a thoroughly built and large portfolio is necessary as input for this ICT artifact. Originality/value Following the recent call of European policymakers to develop standardized and ICT-based approaches for the assessment of professional competences, an ICT artifact that supports the automatized assessment of professional competences within the validation of prior learning is designed and evaluated.


2013 ◽  
Author(s):  
Ronald N. Kostoff ◽  
◽  
Henry A. Buchtel ◽  
John Andrews ◽  
Kirstin M. Pfiel

2020 ◽  
Vol 42 (5) ◽  
pp. 279-307
Author(s):  
Yonglim Joe
Keyword(s):  

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