A Context-Aware Approach for Extracting Hard and Soft Skills

Wings, Ivo; Nanda, Rohan; Adebayo, Kolawole John

doi:10.1016/j.procs.2021.10.016

Cited by 13 publications

(11 citation statements)

References 11 publications

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“…Good communication and effective teamwork can develop students' soft skills (Wings, Nanda, and Adebayo 2021). Communication skill is a basic thing that needs to be possessed by someone, especially for working (Simpson, Van Rensburg, and Benecke 2018).…”

Section: Figure 1 Soft Skill's Aspectsmentioning

confidence: 99%

Student Soft Skills in Learning Support Services with Webinar Tutorials (Tuweb) and Online Tutorials (Tuton) : A Study in Indonesia Open University’s Postgraduate Program

Nirmala,

Afriani,

Isfarudi

et al. 2023

j. kependidikan. has. penelit. kaji. kepustakaan. bid. pendidik

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This study aims to analyze students' soft skills in learning support services, namely webinar tutorial (tuweb) and online tutorial (tuton), and the relationship of the soft skills to the student's grades in both tuton and tuweb, Indonesia Open University's Postgraduate Program. Aspects of the soft skills developed were 1) teamwork; 2) communication through Tuweb's discussion forums; 3) oral communication; and 4) leadership. This study used a survey method with quantitative approach. The participants were 99 students from two courses. The research instrument was online survey questionnaire using Microsoft Forms. Data analysis used descriptive statistics, correlation, and regression analysis. The results of the study were 1) students' soft skills on tuton and tuweb have developed as expected; 2) teamwork and communication skills correlate with students' attendance and discussion; 3) oral communication and leadership skills do not correlate students' discussions and assignments; and 4) teamwork and written communication skills had the most significant contribution to the tutorials' grade compared to oral communication and leadership skills.

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Section: Figure 1 Soft Skill's Aspectsmentioning

confidence: 99%

Student Soft Skills in Learning Support Services with Webinar Tutorials (Tuweb) and Online Tutorials (Tuton) : A Study in Indonesia Open University’s Postgraduate Program

Nirmala,

Afriani,

Isfarudi

et al. 2023

j. kependidikan. has. penelit. kaji. kepustakaan. bid. pendidik

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“…In the case of skill extraction, the same distinction exists. Within the applied techniques, most approaches to this type of problem are based on supervised models (Sharma, 2019;Chernova, 2020;Wings, Nanda & Adebayo, 2021). 2 Many studies have created a training dataset, trained a model on a specific technique and then applied it to unseen data for testing.…”

Section: Different Approaches To the Skills Extraction Problemmentioning

confidence: 99%

“…2 Many studies have created a training dataset, trained a model on a specific technique and then applied it to unseen data for testing. Techniques such as Word2vec or FastText as non-contextual word embeddings and long short-term memory techniques or BERT as contextual word embeddings combined with Part of Speech Tagging (POS) or Name Entity Recognition (NER) have been used in this area (Chernova, 2020;Bhola et al, 2021;Luoma & Pyysalo, 2021;Wings, Nanda & Adebayo, 2021;Vermeer et al, 2022).…”

Section: Different Approaches To the Skills Extraction Problemmentioning

confidence: 99%

“…Phrases were built in the form of n-grams. N grams correspond to sets of n consecutive words in the form of smaller chunks turning sentences to a better suited form for analysis and annotations(Wings, Nanda and Adebayo, 2021) In other words, n-grams are phrases with two or more words together where n represent how many they are. They are composed essentially by nouns, verbs, adverbs, or adjectives.…”

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But which skills?

Anelli

2023

WOLG

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Skills assessment is essential for today’s labour market. There are many factors that change the requirements for the workplace. More than ever, it is important to monitor which skills are in high demand so that workers stay employed and companies do not lose productivity. This research discusses the relevance of data from online job portals for this task. It then uses a skill extractor in online job advertisements from Chile to identify and extract the skills employers place in their online job advertisements through skills dictionaries. The study shows modest results when using the European Skills, Competences and Occupations (ESCO) dictionary but an enhanced and much-improved result when adding an inductively constructed dictionary of the national labour market. Using this method would allow a new input of information to be incorporated into labour market information systems that would enable better decisions to be made by the various actors in the labour market.

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“…NLP has been used to analyze interviews to examine the relationship between verbal content and perceived hireability ratings (Muralidhar et al 2018). Tools based on NLP have also been developed for the automated extraction of relevant information such as skills, work experience and interests from resumes (Sanyal et al 2017;Wings et al 2021).…”

Section: Natural Language Processing (Nlp) In the Selection Phase Of ...mentioning

confidence: 99%

A machine learning approach to recognize bias and discrimination in job advertisements

2022

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In recent years, the work of organizations in the area of digitization has intensified significantly. This trend is also evident in the field of recruitment where job application tracking systems (ATS) have been developed to allow job advertisements to be published online. However, recent studies have shown that recruiting in most organizations is not inclusive, being subject to human biases and prejudices. Most discrimination activities appear early but subtly in the hiring process, for instance, exclusive phrasing in job advertisement discourages qualified applicants from minority groups from applying. The existing works are limited to analyzing, categorizing and highlighting the occurrence of bias in the recruitment process. In this paper, we go beyond this and develop machine learning models for identifying and classifying biased and discriminatory language in job descriptions. We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements, i.e., masculine-coded, feminine-coded, exclusive, LGBTQ-coded, demographic and racial language. We utilized the combination of linguistic features with recent state-of-the-art word embeddings representations as input features for various machine learning classifiers. Our results show that the machine learning classifiers were able to identify all the five categories of biased and discriminatory language with a decent accuracy. The Random Forest classifier with FastText word embeddings achieved the best performance with tenfolds cross-validation. Our system directly addresses the bias in the attraction phase of hiring by identifying and classifying biased and discriminatory language and thus encouraging recruiters to write more inclusive job advertisements.

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A Context-Aware Approach for Extracting Hard and Soft Skills

Cited by 13 publications

References 11 publications

Student Soft Skills in Learning Support Services with Webinar Tutorials (Tuweb) and Online Tutorials (Tuton) : A Study in Indonesia Open University’s Postgraduate Program

Student Soft Skills in Learning Support Services with Webinar Tutorials (Tuweb) and Online Tutorials (Tuton) : A Study in Indonesia Open University’s Postgraduate Program

But which skills?

A machine learning approach to recognize bias and discrimination in job advertisements

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