Determination of Professional Competencies Using an Alignment Algorithm of Academic Profiles and Job Advertisements, Based on Competence Thesauri and Similarity Measures

González-Eras, Alexandra; Aguilar, José

doi:10.1007/s40593-019-00185-z

Cited by 9 publications

(8 citation statements)

References 27 publications

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“…For this, we selected the similarity measure to find the similar names (terms) of classes, properties, and instances. The tool offers a set of lexical and semantic similarity methods, typical of text disambiguation context, which analyze terms according to their linguistic structure (comparing terms according to the characters that make them up) [ 16 ] and their semantic domain (comparing terms against a dictionary or thesaurus) [ 15 , 16 ]. In this way, the result represents the relationship strength between pairs of ontology elements assigning a value between zero and one (where one means a maximum similarity and zero that there is no similarity).…”

Section: Ontological Engineering Process To Build the Covid-19 Pandemic Ontologymentioning

confidence: 99%

Ontological engineering for the definition of a COVID-19 pandemic ontology

González-Eras

Santos

Aguilar

et al. 2022

Informatics in Medicine Unlocked

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COVID-19 has generated a lot of information in different formats, and one of them is in the ontology format. Also, there are previous ontologies from other disciplines that can help to analyze the COVID-19 pandemic. Thus, due to the large quantity of COVID-19 information in the form of ontologies, approaches to ontology integration and interoperability could be beneficial. In this context, this research proposes a new ontology, called COVID-19 Pandemic ontology, which is the product of an ontological engineering process proposed in this research that allows the integration of several ontologies to cover all the aspects of this infectious disease. The ontological engineering process defines tasks of fusion, alignment, and linking for integrating the ontologies. The resulting pandemic ontology provides a simple repository for storing information about the COVID-19, reusing existing ontologies, to offer multiple views about the disease, including the social context. This ontology has been tested in different case studies to prove its capabilities to infer useful information about the COVID-19 pandemic.

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Section: Ontological Engineering Process To Build the Covid-19 Pandemic Ontologymentioning

confidence: 99%

Ontological engineering for the definition of a COVID-19 pandemic ontology

González-Eras

Santos

Aguilar

et al. 2022

Informatics in Medicine Unlocked

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“…Skill or competency or implicit hard skill or explicit hard skill is the "ability to apply knowledge and use know-how to complete tasks and solve problems". Also in [16], they consider competence as something "that can demonstrate the application of a generic skill on some knowledge". Based on the two definitions, we can say that competencies and skills are used interchangeably wherein one context competencies can be soft skills or hard skills in another context.…”

Section: B Skill Basesmentioning

confidence: 99%

“…Then, they inspected the evolution of such skill set in a collection of job ads through different dimensions such as salary levels, education requirements, required experience and posting frequency. Moreover, [16] tested the alignment of academic profiles and job advertisements, thus detecting the academic topics with which the job offers are most aligned.…”

Section: ) Skill Mismatch and Alignmentmentioning

confidence: 99%

A Survey on Skill Identification From Online Job Ads

2021

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“…Further, AIED and related research has been interested in modelling professional competencies using methods from artificial intelligence as the basis for intelligent tutoring systems (e.g., Gott et al, 1986;Kay & Kummerfield, 2011;Ley & Kump, 2013) or as the basis for job recommendations (e.g., González-Eras & Aguilar, 2019). There has also been interest in identifying joint learning goals amongst professional learners as the basis for peer support (Littlejohn et al, 2009), in recommending learning goals based on user modelling (Ley et al, 2010), on supporting contextualized reflective learning through learning prompts in knowledge work (e.g., Fessl et al, 2017;Fischer et al, 1993;McCall et al, 1990), and finally on in-situ learning support in knowledge work (e.g., Lindstaedt et al, 2010) or industrial work (e.g., Frasson, & Aı meur, E., 1998;Westerfield et al, 2015).…”

Section: Aied Research In Professional Learningmentioning

confidence: 99%

“…In the confluence of Artificial Intelligence and learning sciences, numerous advances have been made over the past five decades (Balacheff et al, 2009;Woolf, 2015;Kay & Aleven, 2016;Chassignol et al, 2018), both for individual learning with technology (e.g., Koedinger et al, 1997;Kulik & Fletcher, 2016;Koedinger & Aleven, 2016) and for collaborative learning with technology (e.g., Kumar et al, 2007;Hmelo-Silver et al, 2013;Adamson et al, 2014;Graesser et al, 2018). Notable examples of AI in Education in the area of workplace learning and professional learning 1 exist (e.g., Gott et al, 1986;Lajoie & Lesgold, 1989;McCall et al, 1990;Lesgold et al, 1991;Fischer et al, 1993;Collins et al, 1997;Frasson, & Aı meur, E., 1998;Lindstaedt et al, 2010;Schwendimann et al, 2015;Westerfield et al, 2015;Fessl et al, 2017;González-Eras & Aguilar, 2019). However, most advances in AIED in the past two decades have been made in formal learning environments, with the bulk of work focusing on formal K-12 or higher education (Roll & Wylie, 2016).…”

Section: Introductionmentioning

confidence: 99%

Data-Related Ethics Issues in Technologies for Informal Professional Learning

Pammer-Schindler

Rosé

2021

Int J Artif Intell Educ

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Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED – natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges.

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Determination of Professional Competencies Using an Alignment Algorithm of Academic Profiles and Job Advertisements, Based on Competence Thesauri and Similarity Measures

Cited by 9 publications

References 27 publications

Ontological engineering for the definition of a COVID-19 pandemic ontology

Ontological engineering for the definition of a COVID-19 pandemic ontology

A Survey on Skill Identification From Online Job Ads

Data-Related Ethics Issues in Technologies for Informal Professional Learning

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