Archival Human Factors Jobs Database as a Tool for Tracking Trends in Skills and Knowledge Expectations in the Labor Market

Proceedings of the Human Factors and Ergonomics Society Annual

2016

This study was a replicate of two prior studies on the knowledge and skill expectations for new human factors/ergonomics professionals in the current labor market. Five and four years have passed since new HF/E professionals and their employers were last surveyed. In the rapidly changing work domains it is important to track the changing expectations to detect trends and to continually revise academic curricula to better provide future HF/E professionals education that meets them. Both new professionals and their employers were surveyed for this study. The results are compared with the past surveys. In addition, the relevance of the new core competencies of the Board of Certification for Professional Ergonomics were examined against the workplace expectations.

Section: Other Methodsmentioning

confidence: 99%

“…In 1996In -2007 Moroney and colleagues surveyed annually placement opportunities and hiring expectations. See Rantanen, Claeys, Roder, and Moroney (2013) for a brief meta-analysis of these results. Other surveys include Cooke and Gorman (2004) on the educational training needs of HFES members.…”

Section: Past Surveysmentioning

confidence: 99%

Trends in Knowledge and Skill Expectations for New Human Factors Professionals

Proceedings of the Human Factors and Ergonomics Society Annual

2016

“…The problem of how to keep HFE relevant and up to date has been under discussion in various fora for a number of years. Rantanen & Moroney (2012, 2011 discussed skill needs for new human factors professionals, while Rantanen et al (2013) proposed a jobs database for tracking skills needs in industry. Rantanen (2016) extended the discussion to trends in job skill needs.…”

Section: Education Of Human Factors Engineersmentioning

confidence: 99%

Student Perspectives on Changing Requirements for Human Factors Engineering Education

Zhang

Proceedings of the Human Factors and Ergonomics Society Annual

Chignell

2021

In today’s digital economy, the Internet of Things (IoT) has connected devices, humans, and everyday objects to each other in ways that were unimaginable before. Vast amounts of data are collected everywhere and disrupting how we design systems and products. Data science and emerging technologies offer challenges and opportunities for early-career human factors professionals who are looking to grow their careers and their human factors practice. In this paper, we report on a survey to assess the perspectives of students currently studying human factors. The survey items examined shortfalls in current human factors education with re-spect to relevance to industry trends. The survey results show that students see a need to include more relevant subjects in data science, as well as opportunities to learn trending industry problems, hands-on experience with real-life projects, prior to graduation.

“…Moroney and his collaborators analyzed data on placement opportunities for HF/E profes-sionals annually for over 10 years (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) and reported the results in the proceedings of the HFES annual meetings. For a summary of results from these analyses, see Rantanen, Claeys, Roder, and Moroney (2013).…”

Section: Introductionmentioning

confidence: 99%

“…The entire database can be downloaded into a single spreadsheet for analysis as well. A complete description of the database can be found in and in Rantanen, Claeys, Roder, and Moroney (2013).…”

Section: Introductionmentioning

confidence: 99%

Linguistic Analysis of Archival Human Factors Jobs Data

Hochberg

Johar

Proceedings of the Human Factors and Ergonomics Society Annual

2014

Self Cite

To continually track changing skills and knowledge requirements for human factors professionals in the labor market, an archival human factors jobs database was created. The database contains relevant information from human factors job announcements, including required degree and field of education, required and preferred experience, and information from the job descriptions. The primary purpose of the database is to facilitate longitudinal analyses of employers' expectations. Results from these analyses should be used in the design of academic human factors programs and curricula to better prepare future human factors professionals meet such expectations upon their graduation. In this paper we describe a natural language processing approach to analysis of human factors jobs data. Several trends emerged from our analyses. Although we defer drawing conclusions from these trends until later, until the database contains much more data that are more evenly distributed in time, our methods are detailed here for others to make use in analyses of the jobs data freely available in the database.